Microsoft announced its next-generation quantum computing chip, Majorana 2, to an audience of developers at its Build conference in San Francisco. The new chip was designed with its Discovery agentic AI and with material changes to accelerate the company's timeline for a practical, working computer.

In a blog post, Chetan Nayak, technical fellow and corporate vice president of quantum hardware, wrote that "To create Majorana 2, the Microsoft Quantum team improved Majorana 1’s material stack to create a more stable topological phase. Majorana 2 replaces Majorana 1’s superconductor, aluminum, with lead, and also updates the semiconductor active region to a combination of indium arsenide and indium arsenide antimonide. This change in materials results in significant increases in performance[.]"

Microsoft explains that this can help "shield fragile qubits from cosmic disturbances that can make them unstable."

The company claims that Majorana 2's qubits, units of information used in quantum computing, are 1,000 times more reliable than the previous generation and far more stable, with a mean lifetime of 20 seconds. Though some quibits have lasted as long as a minute, a result that has the company accelerating its roadmap towards relevant, practical quantum computing.

"Based on this rapid progress, we are accelerating our roadmap to a scalable, practical quantum computer—we have cut our timeline in half and now aim to reach this target by 2029. " Nayak wrote. "This achievement will mark a major milestone on the path to a transformative fault-tolerant quantum computer that has the potential to solve problems that affect all of humanity. "

The previous chip, Majorana 1, relied on states of matter that existed only in theory and was questioned by scientists. But the company says this chip is a massive step forward. We'll see how the scientific community responds to the new chip in due time. The full technical paper can be found here (PDF).

At Build, Microsoft announced that Discovery's agentic AI and the local app the team used to produce the new chips are getting a general release. Discovery is designed to assist with creating AI workflows for science and engineering.

IBM has announced that it will create Anderon, a standalone company and America's first pure-play quantum chip foundry, backed by a proposed $1 billion CHIPS Act R&D award from the U.S. Department of Commerce and a matching $1 billion cash investment from IBM itself.

Headquartered in Albany, New York, Anderon will operate a 300mm quantum wafer fab and offer its manufacturing services to competing quantum hardware vendors. The deal was the centerpiece of a broader $2.013 billion federal quantum portfolio split across nine companies, the largest single quantum R&D commitment in U.S. history.

In launching Anderon, IBM is attempting to build the quantum computing industry's equivalent of TSMC, a neutral third-party manufacturer that’ll fabricate superconducting qubit wafers for other companies as well as IBM's own processors. No such foundry exists anywhere in the world today, with every operational quantum computer having been built by a vertically integrated company that designs, fabricates, and operates its own hardware.

A nine-company package

IBM's $1 billion award accounts for roughly half of the entire DoC quantum package. GlobalFoundries received a separate $375 million allocation to launch a "Quantum Technology Solutions" foundry covering multiple qubit architectures, including superconducting, trapped-ion, photonic, and silicon-spin designs. The remaining seven recipients each received smaller awards: D-Wave, Rigetti, Atom Computing, Infleqtion, PsiQuantum, and Quantinuum were each awarded $100 million, while Australian silicon-spin startup Diraq will receive up to $38 million.

Those seven non-foundry companies are required to give the federal government a minority, non-controlling equity stake in exchange for funding. Rigetti has also disclosed in a memorandum of understanding that the government will receive common stock at a 15% discount, while GlobalFoundries separately disclosed a 1% federal equity stake.

IBM's announcement contains no equivalent equity-stake disclosure for Anderon, a somewhat conspicuous omission given that the Trump administration converted part of Intel’s CHIPS Act manufacturing award into a roughly 10% government equity stake last year.

300mm wafer fabrication

IBM said back in November that all of its current and upcoming quantum processors are built on 300mm silicon wafers at the Albany NanoTech Complex, the largest public-private semiconductor R&D facility operated by the nonprofit NY CREATES. Jay Gambetta, IBM's Director of Research, wrote that the shift from 200mm to 300mm produces device output roughly 30 times faster by multiplying device complexity tenfold and tripling devices per line.

IBM's current production processor, Heron r2, holds 156 fixed-frequency qubits, while the Nighthawk processor, which went live via early access on IBM's quantum cloud in January, packs 120 qubits in a square lattice with 218 tunable couplers and a record median T1 coherence time of approximately 350 microseconds. IBM's fault-tolerance roadmap targets the Starling processor in 2029 at roughly 200 logical qubits running 100 million gates, followed by Blue Jay in 2033 at 2,000 logical qubits and 1 billion gates.

All of those chips need 300mm fabrication, and a dedicated foundry with established process design kits, in-line wafer testing, and baseline production routes could let other superconducting quantum companies skip the years and capital required to build their own cleanrooms. Anderon's initial process will support superconducting wiring, through-silicon vias, and bump interconnects, with plans to expand into other qubit modalities over time.

There’s an obvious comparison to TSMC here, which IBM is lapping up, but there’s also a fundamental difference: TSMC succeeded partly because its founder, Morris Chang, made an explicit promise not to compete with the companies that outsourced their fabrication. IBM obviously can’t credibly make that promise; it claims more than 90 operational quantum computers and an ecosystem spanning over 325 Fortune 500s, universities, and government agencies.

Quantum hardware startups considering Anderon will need to weigh 300mm production access against the risk of sharing process knowledge with their largest competitor. Google, which builds its own superconducting chips at its Santa Barbara facility and recently demonstrated quantum advantage on its 105-qubit Willow processor, is unlikely to outsource fabrication to IBM. IonQ and Quantinuum use trapped-ion architectures with almost no process commonality with superconducting silicon, and Microsoft's topological qubit program is on a different fabrication path entirely.

The near-term addressable market for Anderon is limited to other superconducting companies: Rigetti, IQM, SEEQC, and a handful of smaller companies, plus IBM itself. Whether any will actually outsource to a facility owned by their largest rival remains to be seen.

As for the choice of Albany, it carries some irony given that IBM's chip manufacturing presence in the region was effectively sold off in 2014 when the company paid GlobalFoundries $1.5 billion to take over its East Fishkill 300mm fab and Essex Junction 200mm fab. Those operations were losing roughly $700 million per year combined. GlobalFoundries later sold East Fishkill to ON Semiconductor in a deal finalized in 2023, and IBM and GlobalFoundries settled years of litigation over the original terms in January 2025.

The Albany NanoTech Complex, which sits on the SUNY Polytechnic campus, has received more than $25 billion in cumulative technology investment and hosts tenants including GlobalFoundries, Samsung, Applied Materials, ASML, Tokyo Electron, and Lam Research. In 2023, New York State committed $1 billion toward a High-NA EUV Accelerator at the complex as part of a broader $10 billion public-private partnership.

An escalating global spending race

The $2 billion U.S. quantum package comes amid a rapidly escalating global spending race. China's National Venture Guidance Fund, launched last March, authorized 1 trillion yuan, roughly $138 billion, across “hard technology” sectors, including quantum, with direct Chinese quantum investment estimated at $15 billion or more already deployed. Meanwhile, Japan has committed roughly $7.4 billion to semiconductors and quantum combined under its 2025 “Quantum Sun” industrialization agenda.

The EU Quantum Flagship is a rather paltry-by-comparison €1 billion, 10-year program. Combined with prior National Quantum Initiative spending and separate DARPA and Department of Energy programs, the CHIPS quantum package brings cumulative U.S. public quantum funding closer to parity with Europe and Japan but does little to close the gap with China.

BCG's widely cited estimate that quantum computing could generate up to $850 billion in economic value by 2040, which IBM referenced in its press release, is the optimistic end of a $450 to $850 billion range and describes end-user economic value, not vendor revenue. McKinsey's 2025 Quantum Technology Monitor projects a smaller $28 to $72 billion quantum computing revenue market by 2035, while Nvidia CEO Jensen Huang has publicly argued that practical quantum computing is 20 years away as a minimum.

It’s also worth noting that the Anderon deal isn’t yet finalized. CHIPS Act award histories show proposed amounts can shrink during due diligence: Samsung's manufacturing incentive, for example, fell from a proposed $6.4 billion in April 2024 to a finalized $4.75 billion by December 2024. Definitive documents between IBM and the DoC haven’t been executed.

Belgian semiconductor research giant imec this week announced what it describes as the world's first quantum dot qubit device fabricated using High-NA EUV lithography, marking one of the earliest demonstrations of advanced quantum hardware built using the semiconductor industry's most cutting-edge manufacturing technology. The device, unveiled at ITF World in Leuven on May 19, uses silicon quantum dot spin qubits — nanoscale structures that trap individual electrons and exploit their quantum spin states to store information — patterned at gate gaps of barely 6 nanometers.

At first glance, the announcement may seem like another entry in the increasingly crowded quantum computing race. The actual significance, however, has less to do with raw quantum performance and more to do with manufacturing — arguably the single biggest obstacle standing between experimental quantum systems and commercially useful quantum computers.

Qubits can theoretically solve computational problems that would take classical supercomputers longer than the age of the universe, but only at a scale nobody has yet achieved. With several advancements in the physics side of quantum computing, manufacturing now represents the major limitation. Imec claims to have addressed that directly by using the semiconductor industry's latest and most advanced lithography tool to fabricate silicon quantum dot spin qubits with tolerances compatible with industrial chip production for the first time. If that holds up, the implications for quantum scaling could be tremendous. It’s a significant step towards quantum computing, but we are still not quite there.

Manufacturing, not physics, is now quantum computing’s major bottleneck

Quantum computing’s central problem is no longer simply whether researchers can create functioning quantum systems. Our detailed quantum computing roadmap analysis showed that companies including IBM, Google, IonQ, Quantinuum, D-Wave, PsiQuantum, and others have already demonstrated a wide range of working architectures, from superconducting qubits to trapped ions and photonic systems. The problem is scaling those systems into reliable machines containing millions of reproducible, controllable qubits. — the level widely considered necessary for commercially useful, fault-tolerant quantum computers. The most ambitious industry players' roadmaps place that milestone around or beyond 2030, further proving that manufacturing, not physics, is the current hindrance.

Imec's technology directly targets that problem. The company’s approach centers on silicon quantum dot spin qubits, often described as “industry qubits” because they can, in theory, leverage conventional CMOS semiconductor manufacturing infrastructure. Instead of relying on exotic standalone fabrication ecosystems, silicon quantum dots attempt to piggyback on decades of transistor scaling and wafer manufacturing expertise already developed by the semiconductor industry.

The qubits themselves work by trapping individual electrons inside nanoscale silicon structures. The electron’s quantum “spin” state stores information, while surrounding metallic control gates manipulate interactions between neighboring quantum dots. While the concept may sound deceptively straightforward, its fabrication is exponentially more complex.

Quantum dot performance depends heavily on the spacing between those control electrodes. As neighboring quantum dots move closer together, coupling strength rises exponentially, improving controllability and interaction fidelity. But achieving those gains requires reliably patterning gaps measuring only a few nanometers across an entire wafer.

Imec says it fabricated functioning qubit arrays with gaps of barely 6nm between plunger and barrier gates, using High-NA EUV (High Numerical Aperture Extreme Ultraviolet) lithography, the industry’s latest precision lithography technology.

High-NA EUV: not yet standard, already essential

High-NA EUV is the semiconductor industry’s next major lithography transition, developed primarily for future sub-2nm processors, advanced AI accelerators, and dense memory technologies. The systems, built by ASML, improve patterning precision by increasing the optical system’s numerical aperture, allowing dramatically smaller and more accurate features to be printed onto silicon wafers than current EUV systems can reliably achieve. The key difference between the new High NA EUV and conventional EUV is the increase in numerical aperture from 0.33 to 0.55

The machine weighs around 150 tons, spans the length of a double-decker bus, and requires an entirely redesigned optical system with mirrors twice as large and ten times heavier than those in standard EUV tools, polished by ZEISS to atomic precision. The technology is a ground-up engineering effort years in the making.

Even among mainstream semiconductor manufacturers, High-NA EUV technology is only just entering commercial deployment. Intel installed the industry's first commercial High-NA EUV lithography tool late last year, while imec received the technology in its 300mm cleanroom in March 2026 — two months ago. The machines themselves reportedly cost hundreds of millions of dollars apiece and represent one of the most complex manufacturing systems ever built.

The fact that imec has already applied High-NA EUV to quantum hardware — before most chipmakers have even integrated it into standard production flows — suggests quantum computing may be converging directly with the semiconductor industry's existing manufacturing roadmap rather than evolving as a separate technology stack entirely. That possibility can have significant implications. Instead of waiting for quantum-specific fabrication ecosystems to mature independently, silicon quantum hardware may be able to exploit the extremely advanced infrastructure of a multibillion-dollar industry, potentially significantly compressing quantum computing timelines. Although this does not mean manufacturable quantum computers are suddenly close.

The implications of imec’s achievement for quantum computing and the semiconductor industry

While imec's prototype remains far from a large-scale fault-tolerant quantum computer, it still represents a functioning silicon quantum dot spin qubit device — a type of quantum hardware designed to store and manipulate information using the quantum spin states of trapped electrons. These qubits belong to a class of quantum architectures viewed as promising candidates for tackling computational problems that quickly overwhelm even the world's most powerful supercomputers due to their enormous combinatorial and quantum-mechanical complexity.

Silicon quantum dot spin qubits are particularly notable among those candidates because their production process is compatible with standard CMOS semiconductor manufacturing — the same ecosystem that produces CPUs, GPUs, and AI accelerators. It is worth clarifying that imec's breakthrough lies in the manufacturing process, not in the qubit architecture itself. Silicon quantum dot spin qubits already exist and have been an active area of semiconductor and quantum research for over a decade. Previous devices have been demonstrated using conventional lithography at the laboratory scale. While that proved the architecture works, it stopped well short of what industrial scaling demands: consistent, reproducible fabrication at nanoscale tolerances across an entire wafer.

That is the gap imec is now targeting. By demonstrating that High-NA EUV lithography can pattern silicon quantum dot spin qubits at gate gaps of just 6 nanometers on a 300mm fab-compatible process, imec has shown for the first time that the semiconductor industry's most advanced manufacturing tool can be brought to bear on this class of quantum hardware — moving the architecture from lab-scale demonstration toward something that could eventually be manufactured like a chip.

If sufficiently scaled and stabilized, silicon quantum dot spin qubit systems could accelerate progress in molecular simulation, advanced materials discovery, pharmaceutical research, cryptography, logistics optimization, and complex physical-system modeling — fields whose computational demands can be prohibitively difficult for classical supercomputers, regardless of how powerful those machines become.

Rather than serving consumers directly, these systems would likely be deployed by hyperscalers, governments, national laboratories, pharmaceutical firms, and defense organizations tackling computational problems where even incremental breakthroughs could have massive scientific or strategic consequences. The technology would most probably be accessed through cloud-based quantum infrastructure rather than on-premises hardware.

CAS Cold Atom Technology, a Wuhan-based firm affiliated with the Chinese Academy of Sciences (CAS), unveiled what it claims is the world's first dual-core quantum computer, according to a report from state-owned publication Science and Technology Daily. The system, called Hanyuan-2, pairs two independent neutral atom arrays inside a single cabinet-sized machine, totaling 200 qubits built from 100 rubidium-85 and 100 rubidium-87 atoms.

The company said the twin cores can either run in parallel to split workloads or operate in a "one main and one auxiliary" configuration, where the second array handles real-time error correction while the first executes computations. Ge Guiguo, a senior expert at CAS Cold Atom Technology, told Science and Technology Daily that the system represents the first time a quantum processor has moved from single-core to dual-core architecture.

Hanyuan-2 is built on neutral atom technology, which traps uncharged atoms using laser arrays to cool and manipulate individual neutral atoms as qubits. CAS Cold Atom Technology's general manager, Tang Biao, said the machine uses a compact, cabinet-style integrated design with a small laser cooling system, with total power consumption sitting below 7 kilowatts.

200 qubits places Hanyaun-2 well behind the West’s leading neutral atom systems. Atom Computing, for example, demonstrated a 1,180-qubit neutral atom array back in 2023 and has since partnered with Microsoft to deliver error-corrected logical qubits on commercial hardware, while QuEra has delivered error-correction-ready machines to Japan's National Institute of Information and Communications Technology and secured over $230 million in new capital through 2025.

Metrics and peer-reviewed papers lacking

Crucially, both these Western firms have published metrics like gate fidelity, coherence time, and error rate data for their systems, while CAS Cold Atom Technology has disclosed none of these metrics for Hanyuan-2. No peer-reviewed paper accompanied the announcement either, and, as is usually the case with similar announcements coming out of China, all reporting traces back to Chinese state-affiliated outlets.

The use of "dual-core" nomenclature also draws a deliberate parallel to classical multi-core CPUs, but the underlying concept is closer to modular quantum computing, an approach Western companies are already pursuing at larger scales. IBM has focused on linking superconducting processors through classical and quantum interconnect, and QuEra and Pasqal are scaling single arrays while developing inter-module connectivity. Atom Computing and Microsoft are building integrated systems designed around networked quantum processors.

CAS Cold Atom Technology's approach is more tightly integrated than a networked architecture, placing both arrays inside one machine. Whether that confers a practical advantage over scaling a single, larger array remains an open question, and one that published benchmarks would help answer.

Hanyuan-2 follows the delivery of the company's first-generation system, Hanyuan-1, though technical specifications for that machine are also limited.

It's long been hypothesized, and now broadly accepted, that should practical quantum computers arrive, they could eventually break the conventional cryptography methods that underpin today's digital world. Crucially, they might crack elliptic-curve cryptography (ECC), a principle in use today across many fields. The timeframe for the quantum cryptocalypse was previously believed to lie past 2030, but new research from Google pulls this schedule forward to 2029.

The paper predicts profound effects across blockchain infrastructure and cryptocurrencies, among other applications. The list of blockchains expected to be under fire from the quantum ECC attack is essentially "all of them," as demonstrated by a recent Cambridge study.

Although the potential for quantum attacks on ECC is wide-ranging, it's particularly troublesome for blockchain applications, as the participants' public keys and transaction data are by definition published worldwide and can therefore be stored for later cryptanalysis and attack. The fact that historical data is readily available is why blockchains (and cryptocurrencies by extension) are particularly vulnerable to quantum attacks.

At a technical level, the attack on ECC is called Shor's algorithm, published in 1994, when quantum computers were nothing but a fever dream. The recent development that led Google's researchers to sound the klaxons is the fact that they demonstrated that Shor's attack can be performed with systems comprising 1200 logical qubits and 90 million Toffoli gates, or under 1450 qubits and 70 million gates.

Those are exceedingly high figures compared to existing gear that caps out at 48 logical qubits, but those figures are expected to grow exponentially as the technology evolves in coming years. Ironically, the viability of Shor's algorithm against ECC relies on the key length, meaning it would be far harder to use against the now-deprecated RSA encryption scheme and its long 2048-bit-plus keys.

Should the blockchain networks not quickly adapt to what Google describes as an increasingly imminent threat, the resulting chaos from a quantum attacker is predicted to be swift and brutal: among other consequences, wallet funds could be stolen, identities could be impersonated (thus tilting the scale on transaction verifications), and ledgers' intrinsic integrity could no longer be trusted. The DeFi ecosystem and all its smart contracts will also be at risk, plus intrusions into that network will be harder to trace as all transactions will appear legitimate. With a cryptographically relevant quantum computer (CRQC) in play, the chain of trust is not just broken but smashed to pieces.

Google asserts that moving from ECC to Post-Quantum Encryption (PQC) while there is still time to do so is the most appropriate countermeasure, but the fact that blockchains are by definition distributed and don't have a central authority becomes the main implementation problem. Experts have long warned that these attacks aren't merely theoretical, and generally speaking, blockchains take their sweet time in adapting to changing conditions, arguably by design.

Three years is therefore a very short timeframe for an ecosystem that took seven years to turn Ethereum over to a proof-of-stake system (that is, coin staking enables transaction validation) instead of proof-of-work (miners validate transactions).

Rubbing salt on the wound, Bitcoin in particular is technologically ancient by today's standards. Its original design was indeed resistant to attack and did get some upgrades along the way, but many participants see the continuation of mining and the slow evolution of the platform as a feature rather than a bug. That may well be the network's undoing.

Blockchains aren't the only applications at risk, either. Applications like website key exchange, SSH, messaging applications are transitioning away from ECC to PQC, plus any attack still requires capturing the encrypted data to begin with. X.509 certificates, used for server authentication, are a tough nut to crack, since moving to PQC requires coordination from certificate issuers, root certification authorities, and browsers. Code signing is another pain point, with the technology available but not truly implemented at scale yet.

As ever in the cybersecurity world, legacy gear is particularly at risk. At some point, its encryption will be easily broken, plus any captured traffic from now-legacy hardware and applications will be ripe for slicing open. This is yet another reason why it's critical to keep computing gear updated, particularly but not only networking-related hardware and software.

Looking ahead is rarely a good idea: the act of observation itself tends to collapse probabilities into smaller and smaller feasible options. But the future of an estimated $200 billion market by 2040 must nevertheless be looked at with intense scrutiny – plans and funding on groundbreaking yet specialized technology, such as quantum computing, demands that we do.

This isn’t a technical article – we won’t be taking any deep dives into many of the technologies mentioned, only briefly describing them. But this should serve as a good starting point for looking at the overall quantum technological landscape and its possible developments. We’ll be looking at some (but not all) of the current quantum computing approaches that have had the most promising and consistent developments – their futures inked into corporate roadmaps.

What this roadmap covers

Our approach is structured according to technology “families” among companies with deliverable products or services – think the superconducting qubits we’ve come to associate with IBM, Google, and Rigetti; trapped-ions (which have seen the most solid bets from IonQ and Quantinuum); quantum annealing and its particular track-record in optimization problems (D-Wave); neutral atom tech (Atom Computing, QuEra); and photonics (Xanadu, PsiQuantum).

Then, we cover research that has yet to bear fruit by looking at Intel’s work with silicon spin qubits, and Microsoft’s particularly embryonic topological qubits.

For this article, we’ll only look at superconducting qubits (as interpreted by IBM and Google) and trapped-ion qubits (as designed by IonQ and Quantinuum). Quantum is better taken in slices.

One thing to keep in mind throughout is that, like operating frequencies in processors, which aren’t a direct measure of performance, qubit quantity isn’t the be-all-end-all of quantum computing. The quality of qubits matters more than their quantity, even if quantity does improve quality up to a point.

Superconducting Qubits

Superconducting qubits, as the proximity to “superconductor” implies, take advantage of certain material’s ability to conduct electrical currents with no resistance. Qubits can be built out of these materials through what is called a Josephson junction – essentially, two superconducting layers separated by an insulating, 1-2 nanometer barrier. This junction then induces the emergence of discrete energy levels, which can be used to represent differentiated states (information). Computing is simply what is done to those states, and measuring their outcomes.

Superconducting qubits have the benefit of being compatible(ish) with contemporary 300mm semiconductor wafer fabrication technologies, which significantly improves perspectives on scaling, even if it means that inter-qubit connectivity is an obstacle (think CPU bus designs, and related technologies). However, the required near absolute zero operating temperatures and the degree of hardware cost and maintenance complexity means that superconducting quantum systems tend to be offered to customers via cloud platforms moreso than direct hardware sales.

IBM quantum roadmap

IBM splits its roadmap in two – a development roadmap, focused on the release of production hardware, software, and services available to customers; and an innovation roadmap, tracking the required scientific breakthroughs and internal proofs of concept that allow development to execute. IBM’s services started with cloud-based access to a 5-qubit system around May 2016, with cloud availability of its 20-qubit System One starting from 2019.

IBM’s latest executed hardware revision – Heron – is a quantum processor that offers a choice of 133 or 156 fixed-frequency qubits executed across 7.5K gates, and is part of the company’s current System Two architecture.

Nighthawk, IBM's 2025 execution-target (of which performance metrics are still absent), sees a step-back in qubit density, down to 120 qubits, and gate density, down to 5K gates, with a focus on systemic scaling. There's an increase in inter-qubit connections through L-coupling to improve performance and coherence. This architecture is to be expanded through 2028.

2026 sees a return to 7.5K gates while also allowing multi-chip scaling of up to three Nighthawk chips working in tandem (120 qubits *3).

By 2027, IBM expects to scale Nighthawk towards 10K gates feeding the same 120 qubits (a concession for increased coherence and accuracy) with up to 9 interlinked Nighthawk chips, bringing the total number of qubits up to 1080 (120*9). By 2028, Nighthawk is expected to scale up to 15K gates.

2029, however, is where IBM plans to have error mitigation matured enough so as to achieve their promise of a fault-tolerant quantum processor in the form of Starling. The architecture differences are expected to be significant, with a 100 million gate design powering 200 logical qubits.

By 2033, IBM expects its architecture to scale tenfold, with Blue Jay chips delivering 2000 logical qubits across 1B gates. The roadmap reads this moment as the keystone for unlocking “the full power of quantum computing” – powerful enough systems to demonstrate quantum advantage on several key addressable classes of problems.

Google Quantum Roadmap

Google is now marketing its quantum computing efforts under the “Quantum AI” tagline, and its public roadmap is much less coherent than IBM’s – there are no public commitments to specific performance metrics or qubit counts, nor a describable timeline for the innovations the company is planning on executing on the road to fault-tolerant quantum computing.

The company does seem to be eyeing up quantum computing through an AI-focused lens: as they put it, “quantum computation will be indispensable for collecting training data that’s inaccessible to classical machines, training and optimizing certain learning architectures, and modeling systems where quantum effects are important.” Google definitely has a plan for the future of quantum – but the relative opacity opens up questions.

Google’s current hardware revision for quantum processors is Willow – a 105-qubit superconducting-qubit chip that the company claimed showcases quantum advantage (in this case, being able to perform calculations that are deemed impossible for classical computers to solve within even the lifetime of our observable universe). These claims are always met with backlash – more than once have we seen algorithmic developments in the classical computing space that unlock computations previously thought to only be achievable in the quantum realm.

Much like IBM, Google understands that qubit quality, coherence and interconnectivity are as important (if not more important) than raw qubit counts; while Willow was introduced in 2024, the company’s latest announcement in the area pertains not to a hardware revision but to improved error-correction algorithms, with their “Quantum Echoes” allowing for verifiable, cross-benchmarkable, repeatable quantum computing results.

Google’s roadmap stands at Milestone 2 (achieved in 2023) out of 6, and it directly relates to their October 22, 2025 announcement on Quantum Echoes. This development bridges the gap to Milestone 3 – building a long-lived logical qubit, which the company defines as “capable of performing one million computational steps with less than one error”.

Milestone 4 relates to pairing these long-lived logical qubits in effective, logically-addressable quantum gates, while Milestone 5 looks at pairing multiples of these gates. The aim for Milestone 5 is to achieve up to 100 logical qubits tiled together for high-fidelity quantum operations, with an expected requirement of 10^5 (100,000) physical qubits to achieve it.

The end of Google’s current AI roadmap, Milestone 6, aims to achieve a “large error-corrected quantum computer” – one that operates on 1 million connected and controllable physical qubits. But again, there is no timeline.

There are inferences to be made between IBM’s public commitment, Google’s overall lack of clarity, and the perceivable architectural differences between both approaches. It's interesting to note that Google expects to uncover “10+ error-corrected quantum computing applications” through Milestone 6.

Trapped Ions

Trapped ion quantum computing is a wholly different beast from superconducting qubits – rather than Josephson Junctions, qubits are constructed of individual atoms (typically ytterbium or barium). These atoms are suspended in a vacuum and held stable in precisely-controlled electromagnetic traps at room temperature, with gate operations being typically performed through laser pulses.

These characteristics mean that trapped ion qubits are less susceptible to environmental noise than most other qubit designs, showcasing coherence times in the seconds-to-minutes range. Unlike superconducting qubits’ interconnectivity issues, trapped ions can be designed to have all-to-all connectivity.

Hardware complexity (especially when it comes to laser pulsing) does remain a snag in scaling, and the speed of quantum calculations is limited by laser-based technology. Their useful states last longer than superconducting qubits might, but they are usually slower to retrieve useful work from.

IonQ acquired Oxford Ionics in June 2025, prompting a new way of looking at trapped ions – one that, like superconducting qubits, can take advantage of semiconductor manufacturing to tackle specific scaling issues.

IonQ

IonQ started offering quantum computing services in late 2017 by selling the first commercial trapped-ion quantum computer – a direct-sales model which was then complemented with cloud access to compute starting from 2019 (first through Amazon Bracket, then Azure Quantum, and Google Cloud Marketplace in 2021).

There’s perhaps no point in looking at IonQ’s roadmap before June 2025, when IonQ took a specific strategic turn with its announced acquisition of Oxford Ionics, the results of which are already part of the company’s roadmap. 2025 saw IonQ delivering on their Tempo architecture three months ahead of schedule while pushing the number of physical qubits up to the revised roadmap’s promised 100, while also demonstrating the base working architecture that will allow their 2026 product to more than double that, up to 256 qubits.

This evolution is made possible by IonQ’s move from ytterbium atoms to barium atoms, paired with IP integration from Oxford Ionics (which replaces the laser array with chip-carried microwave control) alongside Lightsync’s interconnect and quantum memory architecture. Through the microwave control replacing the laser control system, a lot of the difficult-to-scale parts in the overall trapped ion architecture become compatible with wafer manufacturing techniques, which helps explain the much increased confidence in IonQ’s new roadmap.

The company previously expected to achieve 384 algorithmic qubits by 2027 (a performance measured used only by itself), the new architecture has moved that goalpost to actually physical and logical qubits, promising systems with as many as 800 logical qubits across 10,000 physical qubits.

By 2028, the company aims to double that to 1,600 logical qubits across 20,000 physical qubits with the full integration of Lightsync’s IP in its photonic interconnect. By 2029, IonQ plans to go further with a design featuring 8,000 logical qubits across 200,000 physical qubits. A note on the slide adds that the company believes this is the era for actual viable, performant commercial quantum computers.

By 2030, the company expects to achieve what is claimed to be the highest number of available qubits on a product (whatever form it might eventually take), with a tenfold increase to 80,000 logical qubits across 2 million physical qubits. However, what that might look like in terms of actual performance remains to be seen.

Quantinuum

in Singapore by 2026 through a collaboration with the National Quantum Computing Hub (NQHC), with a focus on solving problems in finance, pharmaceuticals, and materials science. Quantinuum was founded in November 2021 as a result of a merger between Honeywell Quantum Solutions and Cambridge Quantum Computing. Before that, Honeywell offered commercial quantum computers starting with the H1 in October 2020 (offering 10 interconnected qubits), and their solutions, before evolving into its current model, available through direct cloud or delivered through Microsoft’s Azure Quantum, with their 2025 Helios model now being made available for on-premises installation.

Quantinuum’s Helios is the company’s execution on its 2025 roadmap, offering Barium-atom-based qubits at counts of 98 physical and 48 logical – a switch from the Ytterbium atoms they too previously used (and a technological convergence with IonQ, up to a point).

A physical installation is expected to occur by 2026 in Singapore through a collaboration with the National Quantum Computing Hub (NQHC), with a focus on solving finance, pharmaceutical, and material science problems.

By 2027, Quantinuum is looking to introduce its Sol architecture, which will integrate a 2D-grid-based qubit design to double qubit counts (192 physical and, one would guess, 96 logical), besides a focus on improving error correction capabilities.

It seems that Sol is more of a validation vehicle for Quantinuum’s architecture choice in the lead-up to 2029’s Apollo, which is the point at which he company’s roadmap also becomes blurry – we’re now met with projections that Apollo is expected to hit thousands of physical qubits with a correspondingly uncertain number of hundreds of logical qubits. But Apollo doesn’t yet seemingly meet the requirements to be Quantinuum’s own “large error-corrected quantum computer”; it seems more of a demonstration vehicle for a fully fault-tolerant quantum computer.

Scale is expected to come from its successor, Lumos, which hasn’t even been added to the available roadmaps yet, but has been mentioned for a 2033 release via its selection by the Defense Advanced Research Projects Agency (DARPA) for stage B of the agency’s Quantum Benchmarking Initiative (QBI).

The future of Quantum is being paved

This is but a partial view of the quantum computing landscape – we covered two technology areas and only two companies in each. Google proved to have the most opaque roadmap, but it’s also the biggest company on the list by market cap alone (a reduced advantage in a field where the bottleneck isn’t money, but specialists and IP). But this view still offers a good sense of what's to come for Quantum machines.

For one, all four companies we looked at appear to be mapping out large-scale, error-corrected quantum computers around and beyond 2030. That’s when the utility explosion is expected to happen. On the road there, the trapped-ion side seems to be leaving laser-based interactions and opting for 2D placement of atoms to increase scaling and qubit density with such confidence that IonQ claims to have the most scaling-friendly architecture available.

The superconducting side of the coin appears to be focused on error-correction and architecture fine-tuning, and is conservative in raw qubit count scaling when compared with the trapped-ion roadmaps.

Perhaps one way of looking at commercial and technological viability for the differing quantum architectures is by looking at those that made it into stage B of DARPA’s QBI, which include IBM, Quantinuum, and IonQ – out of the ones we looked at in this article, Google (and its very tentative roadmap) is the only absent player.

Quantum-software firm BlueQubit has launched its Quantum Advantage Challenge, offering a 0.25 BTC wallet prize to the winner. The overall goal of the competition is to prove that a ‘Quantum Advantage’ exists in solving a real-world cryptography problem. BlueQubit says it can find a hidden bitstring in a 2⁵⁶ search space, the key to its 0.25 BTC prize wallet, in “under two hours.” Conversely, it reckons that even the fastest classical supercomputer would take “years” to solve the problem. The challenge is now open at www.bluequbit.io.

“We wanted a clear, public, and verifiable way to demonstrate quantum advantage,” said Hayk Tepanyan, BlueQubit CTO, explaining the idea behind the Quantum Advantage Challenge. “There’s no better proof than a problem where a quantum computer can extract a real cryptographic key in hours and where classical algorithms may simply be outmatched.”

A blog post published ahead of the prize launch explained how the challenge works and how the result is verifiable. It started by sympathizing with industry watchers being bamboozled with various solutions already claiming to have demonstrated a ‘Quantum Advantage’ or even 'Quantum Supremacy.'

The BlueQubit problem takes the form of a random circuit design with 2⁵⁶ possible output bitstrings, which is about 72 quadrillion possibilities. Specifically, the problem is built around peaked circuits, which are quantum circuits engineered to produce an extremely concentrated probability distribution, ‘peaking’ on a single hidden bitstring. An elegant verification protocol has been devised like this:

• Alice constructs a peaked circuit, knowing which bitstring is the peak
• Bob runs the circuit on his quantum computer, measuring outputs
• Alice verifies by checking if Bob's output matches the known peak

All of the RAM

The construction of the problem means that no exponential classical computation is required to verify the answer – a calculation that would “require more RAM than all of the world’s computers combined,” to hold its full state, according to the Quantum firm. Instead, all you have to do is simply check if the answer is right, as it was set. In this case, it is the private key to solve the peaked circuit, which will also open a 0.25 BTC wallet.

“If no one is able to beat the quantum solution, this will stand as compelling evidence that quantum computers have already surpassed classical computing for specific, practical tasks,” sums up BlueQubit, throwing down the gauntlet.

Conversely, success by a classical computer-wielding challenger would net the lucky person/group a worthwhile prize. But perhaps more importantly, it would prove that BlueQubit’s claims of Quantum Advantage were misplaced in this instance.

It is interesting to see this challenge being used as a vehicle to demonstrate that today’s quantum computers can outperform classical machines on a real cryptographic task. Quantum has the potential to disrupt cryptography in a big way.

We feel this prize might be a tad small to attract the most formidable challengers, but BlueQubit says that “even a Google quantum researcher is involved” in the efforts to crack its challenge, classically.

Senior financial strategist Christopher Wood said in the latest issue of the GREED & Fear newsletter that he’s removing the 10% Bitcoin allocation from his recommended portfolio. He justified this move by saying that advancements in quantum computing pose a threat to the cryptocurrency’s cryptographic protections, thus undermining its argument of durability through network security. According to Bloomberg, Wood recommends replacing Bitcoin with an investment with a 5% allocation to physical gold and another 5% set for gold mining stocks.

Wood, who is the Global Head of Equity Strategy at the global investment banking firm Jefferies, first included Bitcoin in his sample portfolio in December 2020. He then grew it to 10% in 2021, citing the fear of inflation because of the stimulus checks the government released during the height of the Covid-19 pandemic. However, advancements in quantum computing have long-term investors concerned about its implications, especially for cryptocurrencies.

Bitcoin currently uses the SHA-256 hashing algorithm, which is technically impossible to crack with current computing technology. However, there have been reports as far back as 2022 that quantum computers could crack Bitcoin by the 2030s. An event like this would cause chaos in the system, resulting in Bitcoin (and other cryptocurrencies) losing its value overnight, especially if the break comes as a surprise. Because of this, Wood recommended moving away from it, especially for long-term investors.

Despite this, many cryptocurrency developers aren’t as concerned as Wood and other financial experts. For one, current quantum computing capabilities are nowhere near powerful or stable enough to defeat current cryptography algorithms, so they remain safe for the time being. Besides that, progress in the field of quantum computing is slow and public, meaning developers would have ample warning that they need to upgrade their algorithms.

Another big reason that cryptocurrencies aren’t particularly concerned about quantum computing right now is that if quantum computers can break Bitcoin security, then they can break cryptography algorithms all across the world. So, if their security protocols were to be broken, then the security of everything else — including traditional banking systems, secured internet protocols, government encryption, and more — will also be affected. Besides, security developers are already looking into post-quantum cryptography, with cryptocurrency developers able to take advantage of their developments as well.

Despite this, Wood says that the debate between cryptocurrency developers and quantum computing will only be a “long-term positive for gold.” It has historically held its value, reaching an 11% annual return over the past 50 years. So, investors looking for a stable, long-term asset to park their funds would probably find the precious metal an attractive option.

IBM and Cisco have announced plans to jointly build a distributed quantum computing network capable of linking fault-tolerant systems over long distances. In an announcement on Thursday, November 20, the companies said they aim to demonstrate a two-machine entanglement proof-of-concept by 2030, with the ultimate goal of enabling scalable quantum workloads that span multiple sites and processors. If successful, the collaboration would mark a shift in how quantum computing resources are deployed, moving beyond single-system scale to a federated architecture capable of trillions of quantum operations.

The initiative will combine IBM’s superconducting qubit hardware with new networking infrastructure from Cisco, including microwave-optical transducers, quantum network control layers, and physical and software routing protocols designed for entangled quantum state transmission.

The proposed architecture is intended to support fault-tolerant quantum computers already in IBM’s development roadmap. But it would also require the creation of new intermediary hardware — a planned ‘Quantum Networking Unit’, or QNU — to interface with IBM’s quantum processors and translate static quantum states into flying qubits suitable for transmission via photonic links.

The architecture IBM and Cisco want to build

The duo’s ambitions will be built upon a three-tier model that splits qubit modules, networking transduction interfaces, and optical entanglement layers. IBM’s Quantum Processing Unit (QPU) roadmap projects logical fault-tolerant machines with several hundred logical qubits. each requiring thousands of physical qubits, by 2030.

Cisco’s role is to link these cryogenic environments together. Entanglement between processors would be achieved using shared photon pairs or teleportation-style protocols, with photon-based carriers transmitted over optical fiber or potentially free-space links.

Because IBM’s superconducting qubits operate in the microwave scale, while long-distance transmission favors optical frequencies, a high-efficiency transducer is needed to convert quantum information from one format to another. That device — capable of preserving coherence and phase relationships between microwave and optical domains — will have to be developed and is one of the key technical hurdles of the roadmap.

The companies say the initial milestone will be to link two independent QPUs located in separate cryogenic systems. This will test both hardware entanglement and software synchronization layers. If successful, a scaled version of the architecture would allow for modular quantum computing networks, where computation is distributed across many small fault-tolerant nodes, and entanglement is dynamically allocated based on the structure of the problem being solved.

Why networks

IBM’s vision of scalable quantum computing has already shifted from single-monolithic machines toward what it calls quantum-centric supercomputing. Under that model, quantum processors function as accelerators embedded within larger high-performance compute environments, connected to CPUs, GPUs, and shared storage via classical interfaces. However, some workloads, especially those involving chemistry, material science, or cryptographic search, will require quantum circuits with hundreds of millions or billions of gates.

Running those circuits within the coherence window of a single device is infeasible, even under optimistic hardware timelines. Instead, IBM’s roadmap assumes inter-processor coordination, allowing large algorithms to be divided into subcircuits that can run on separate QPUs. This would enable workloads that exceed the qubit count or gate fidelity of any single machine.

The QNU plays a central role here, acting as the entanglement interface between QPUs. While some early experiments in microwave-to-optical transduction have been demonstrated in lab settings — including at Fermilab’s SQMS Center, where an IBM partnership is planned — the level of fidelity and error rate required for distributed fault-tolerant computing is still years away from production.

The companies are also working on software protocols that manage entanglement routing across the network. Unlike classical networks, where bits can be duplicated and retransmitted, quantum systems depend on ephemeral, one-time-use states. That means entangled links must be established just-in-time, managed through a new class of control protocols that coordinate not only logical dataflow but also the physical movement of qubit states. Cisco says it will contribute a high-speed software protocol framework to support these operations.

Quantum computing internet

The long-term vision goes well beyond inter-device communication. IBM and Cisco say their roadmap could extend into a future quantum internet where quantum processors and entangled photonic links form a planetary-scale network of physically distributed (but logically connected) resources.

The idea of a quantum internet has been proposed before. Several research groups have published designs for node-based or repeater-style architectures, but most of those are focused on specific applications such as quantum key distribution or secure messaging. IBM’s goal, however, is to make distributed compute a viable path for running quantum algorithms that can’t fit in memory on a single machine.

If achieved, it could allow new types of applications, from supply chain modelling to real-time climate simulation using quantum-enhanced sensing. IBM has suggested that such networks could support “trillions of quantum gates” across multiple QPUs, far beyond the practical limits of even a thousand-logical-qubit monolithic device.

A long road ahead

The 2030 timeline for demonstrating a basic entanglement between two QPUs is extremely ambitious. A scalable multi-node quantum network is expected to follow just a few years later, with long-distance networking only arriving in the latter half of that decade. The quantum internet vision, where processors and entangled repeaters span entire regions, is likely more than 15 years out.

Some significant engineering challenges will need to be overcome to make this timeline a reality. No existing transducer meets the required efficiency and fidelity thresholds for scalable links. Meanwhile, distributed quantum error correction is still in development, and most of the proposed network protocols are theoretical or exist only in research simulations. There is also the challenge of integrating Cisco’s photonic networking expertise with IBM’s cryogenic systems in a way that minimizes thermal interference and maximizes link yield.

After more than a decade of pushing processor design, IBM is now turning its attention to interconnects. Cisco, for its part, is betting that quantum computing will need entirely new systems thinking, where classical routing is blended with real-time entanglement management.

It is a different way to think about infrastructure, not just as a transport layer, but as a co-designed part of the computational pipeline itself. If IBM and Cisco can build it, they’ll be reshaping what it means to run a program when the processor is no longer a single machine.

Quantum computing is still a long way from becoming a mainstream part of society; however, a Chinese firm has developed an all-new optical quantum computing chip that is closing the gap, called the world's first scalable, "industrial-grade" quantum chip. The South China Morning Post reports that the chip's developer claims it is "1,000 times faster" than Nvidia's GPUs at AI tasks and is already being used in some industries, including aerospace and finance.

The chip in question was built by the Chip Hub for Integrated Photonics Xplore (CHIPX) and is based on a brand-new co-packaging technology for photons and electronics, and it claims to be the first quantum computing platform to be widely deployable. These photonic chips house more than 1,000 optical components on a small 6-inch silicon wafer using a monolithic design, making them incredibly compact compared to traditional quantum computers.

All of these factors have reportedly allowed systems with these quantum chips to be deployed in just two weeks, compared to six months for traditional quantum computers. Its design also allows these chips to work in tandem with each other, just like AI GPUs, with deployments allegedly being "easily" scaled up to support 1 million qubits of quantum processing power.

CHIPX's optical quantum chip uses light (or photons) as the information carriers for qubits, rather than matter-based materials. Light has many advantages over raw electricity for computer processing: it takes up no physical space, generates no heat, and travels more efficiently and faster than electricity. Optical computing is attracting more and more scientists and companies as a potential replacement for electrical connections, especially now that power consumption in data centers is sky-high thanks to AI.

However, the current Achilles heel of China's new quantum chip has been the difficulty in producing these chips in large numbers, due to the delicacy of the materials used. The facilities responsible for producing these chips are reportedly producing 12,000 wafers per year, with each wafer yielding "about" 350 chips. That's a relatively low production volume compared to typical semiconductor fabs.

There are still many unknowns about this new quantum chip; we don't know which kinks need to be worked out (beyond the production issues) to make these chips truly mainstream. But, regardless, China is intent upon beating Western countries in quantum computing capabilities. If the "1,000x faster than Nvidia GPUs" statement is to be believed, it would be a marvelous feat but unsurprising in the world of quantum computers, which, by nature, can solve equations at a rate impossible to comprehend compared to classical computers.

We haven't seen quantum computers this small or as scalable from Western companies, but with companies like Nvidia pouring serious cash into the quantum computing sector, perhaps that will come soon.

IBM has detailed its most significant quantum computing advances to date, revealing new hardware and software designed to push the limits of what today’s superconducting qubits can do. The announcements, made during IBM’s Quantum Developer Conference on November 12, include a new 120-qubit chip, updates to the Qiskit software stack, and a testbed architecture for running quantum error correction in hardware. Together, the new tools represent a serious step forward on the path to fault-tolerant quantum computing.

Nighthawk expands IBM’s hardware

At the center of IBM’s near-term roadmap is the Nighthawk chip, a 120-qubit superconducting processor built to execute deeper circuits more efficiently than its predecessors. The chip moves away from IBM’s earlier “heavy-hex” qubit layout and instead uses a dense square lattice design. Each qubit connects to four neighbors via tunable couplers, for a total of 218 coupler pairs. That marks a 20% increase in inter-qubit connections over IBM’s Heron chip and reduces the number of swap gates needed to implement complex entangling operations.

IBM says this increase in qubit connectivity directly translates into larger algorithmic workloads. According to IBM, Nighthawk will support circuits with 30% greater complexity than Heron while maintaining comparable fidelity. That estimate is based on live metrics from Heron-class machines already deployed in IBM’s quantum cloud, which recently achieved two-qubit gate fidelities above 99.9% for over 50% of the tested pairs. In benchmarking terms, the chip family reached 330,000 circuit layer operations per second (CLOPS), a 65% gain over its 2024 performance. IBM expects similar or better numbers from Nighthawk once it becomes available to the public.

The new processor will play a central role in IBM’s campaign to demonstrate a verified quantum advantage, which the company now says it expects to achieve by the end of 2026. To support that claim, it has backed the formation of an open “quantum advantage tracker,” inviting third-party researchers to test candidate workloads against classical baselines. Early example circuits from partners, including Algorithmiq and the Flatiron Institute, have already been submitted, focusing on observable estimation and constrained optimization problems.

IBM’s timeline includes further performance gains beyond the current 5,000-gate target. The company expects iterative improvements to push that figure to 7,500 gates in 2026 and 10,000 by 2027, without increasing qubit count. These are incremental steps, but they align with the broader strategy IBM has laid out for gradually improving fidelity and circuit depth while using real benchmarks to validate progress against classical solvers.

Loon lays the groundwork for fault-tolerance

While Nighthawk represents IBM’s best shot at a near-term quantum advantage, the company’s ambitions rest on a longer arc toward fault-tolerant quantum systems. IBM’s engineers believe they’ll get there by the end of the decade, and this week’s preview of the “IBM Quantum Loon” test chip hints at what that system might look like.

Loon is a proof-of-concept superconducting test chip built to validate the hardware components required for scalable quantum error correction. IBM says the processor will include architectural features such as long-range inter-qubit couplers — known as “C-couplers” — designed to enable the efficient implementation of quantum low-density parity-check (qLDPC) codes.

IBM has previously demonstrated its ability to achieve 6-way qubit connections, increase layers of routing on the chip surface, and build reset gadgets that reset the qubit to ground state. “With Loon, for the first time, we test all these features together, aided by new electronic design automation (EDA) to realize more complex architectures than ever before,” says Ryan Mandelbaum, Editor in Chief of IBM Quantum.

On the control side, IBM also announced that its latest classical decoder design, implemented on an AMD FPGA, can process error syndromes in under 480 nanoseconds. That performance is roughly ten times faster than previous iterations and meets the latency threshold required for practical error correction on superconducting hardware. The company says this milestone was reached a year ahead of schedule and will form the basis of future real-time decoding logic for larger fault-tolerant systems.

IBM’s hardware roadmap calls for a series of increasingly modular systems starting in 2026. That includes “Kookaburra,” the company’s first prototype for logical qubit storage, and “Cockatoo,” a 2027 multi-chip device intended to demonstrate entanglement between separate processors. By 2029, the company expects to ship “Starling,” a 1,000-qubit system with 200 error-corrected logical qubits capable of performing more than 100 million operations per job.

Software stack matures further

In parallel with its hardware roadmap, IBM has continued expanding its Qiskit software stack to support more advanced compilation, error mitigation, and classical integration. The latest version, Qiskit 2.2, includes several technical improvements that appear designed to close the gap between theoretical algorithm design and practical execution on real hardware.

Key among those features is full support for dynamic circuits, which allow quantum programs to include mid-circuit measurements and conditional operations based on real-time results. At the conference, IBM demonstrated dynamic circuit execution involving more than 100 qubits, with real-time feedback operations and idle qubit stabilization through pulse-based techniques. According to the company, these methods produced a 25% improvement in output accuracy compared to static circuits, while reducing total gate count by over 50%.

IBM also introduced a new Qiskit tool called Samplomatic, which allows users to add custom annotations to specific regions of a quantum circuit. These annotations are compiled into templates and a new object called the samplex, which defines semantics for circuit randomization. Samplomatic integrates with a new executor primitive to enable composable and efficient error mitigation. According to IBM, applying these techniques in combination with tools like propagated noise absorption and shaded light cones allowed developers to reduce the sampling overhead of probabilistic error cancellation by a factor of 100.

IBM’s latest quantum computing updates reflect a deliberate strategy that balances incremental improvements with long-term architectural goals. The company is not alone in its pursuit of fault tolerance — Google, Intel, and IonQ have laid out their own paths to scale — but IBM’s approach is unusually comprehensive, combining chip design, fabrication, software, and system-level integration under a single roadmap.

Whether the company can hit its 2026 goal remains to be seen. Verified advantage is still a high bar, and the best classical algorithms continue to improve. But Nighthawk appears to offer a legitimate increase in accessible circuit complexity, and IBM’s move to open benchmarking suggests it is willing to let independent comparisons define that moment. On the other side of the equation, Loon offers a credible early platform for testing the essential components of fault-tolerant logic.

Hot on the heels of Google's breakthrough in the practical application of quantum computing, IBM is now poised to announce a quantum leap of its own. According to a Reuters report, a team of IBM researchers will publish a paper on Monday, October 27, detailing how they performed quantum error correction on standard AMD chips. That should smooth one of the major roadblocks to the practical usability of quantum computers, namely, result accuracy.

The error-correction algorithm reportedly not only runs in real time but also runs 10x faster than necessary atop AMD FPGA chips. This latest development is likely the result of the collaboration with AMD that IBM announced at the end of August, one that sent AMD shares rising sharply at the time.

Reuters quotes IBM's director of research, Jay Gambetta, as saying this work with the freshly minted algorithm was completed a year ahead of schedule. This bit of news should be welcome to enthusiasts and investors alike, as it could push forward the schedule of IBM's Starling large-scale quantum computer, originally set for 2029.

FPGAs are Field Programmable chips, meaning their internal structure can be reconfigured via software. They trade general-purpose usability and raw performance for the ability to run highly specialized tasks efficiently. They're most everywhere in computing, but you're bound to have heard of them in the consumer space inside vintage console, arcade, and PC emulators.

As for the software itself, it's almost certainly the Relay-BP (Relay Belief Propagation) algorithm, first published in a paper in June. IBM then proudly announced in August that its quantum research team had achieved good results running it on FPGAs, calling it the only quantum LDPC (Low-Density Parity-Check) error correction algorithm that "is not only flexible and compact, but also faster and more accurate than all known alternative methods."

Error correction is one of the key elements in making a usable quantum computer. As an oversimplification, quantum computers can run a highly limited set of programs. Still, they can do so considering every possible input at once, thanks to its qubits being 0 and 1 simultaneously. However, reading the result is extremely complicated, as altering it would not change the result, and the result is not deterministic anyway; think of 2+2 = 3.999 or 4.001. Reducing the margin of error is a cornerstone of practicality, and both IBM and Google have made great progress in this area.

The U.S. government is negotiating with leading quantum-computing companies to acquire ownership stakes in return for Federal financial support, reports the Wall Street Journal. The plan would provide public funds to quantum technology startups that badly need money while granting Washington direct equity participation and expanding its role as an investor in the private sector.

Atom Computing, D-Wave Quantum, IonQ, Rigetti Computing, and Quantum Computing are among the companies that are discussing or considering entering into a deal with the U.S. government to get funds, according to the WSJ report, which cites people familiar with the matter. Each of the firms is seeking at least $10 million in funding from the U.S. Commerce Department, which would take shares or equivalent financial instruments in return.

The requested financing will come from the Chips Research and Development Office, which manages the CHIPS Act resources in the U.S. Department of Commerce. Secretary Howard Lutnick has reportedly reclaimed several billion dollars that had previously been allocated to a technology research program launched under the Biden administration, so the organization now has money for initiatives like the current one.

Oversight of the new quantum program has been assigned to Deputy Commerce Secretary Paul Dabbar, formerly a quantum computing executive and Energy Department official. His previous company, Bohr Quantum Technology, which he co-founded and led for four years, will not be eligible for the initiative, though.

Quantum machines promise to perform calculations far beyond the reach of conventional supercomputers, potentially dramatically streamlining areas like drug discovery and materials science, which could have a dramatic impact on the whole industry. As a result, politicians consider quantum computing a noteworthy part of the global competition between countries. Therefore, if the deals are reached, American quantum computing companies will get an immediate financial boost, whereas the U.S. government will return its money with profit if these companies develop commercially successful products.

The initiative follows previous high-profile deals in which the U.S. Commerce Department obtained a 9.9% stake in Intel, converting nearly $9 billion in earlier grants into equity, and the Pentagon got a 15% stake in MP Materials, a producer of rare earth materials from the U.S.

Quantum computing doesn't show up often in the news. After all, it has gigantic potential, but the technology has been promising practical real-world uses in "just a few years" for decades now. The science world may have finally reached a tipping point for practicality, though. Google's engineers demonstrated a world-first of running a verifiable algorithm on its Willow quantum chip — 13,000 times faster than a supercomputer.

The algorithm in question is called Quantum Echo and models a physics experiment in Nuclear Magnetic Resonance (NMR, the spectroscopic variant of the popular MRI), revealing internal molecular structures by detecting magnetic spins at the center of atoms. According to Google, Willow runs this 13,000x faster than "the best classic algorithm on the world's fastest supercomputers", meaning that the one chip is many order of magnitudes faster than an entire datacenter at this specific task.

What's exciting and and a world-first about this experiment is that it's verifiable, meaning that Willows' results are reproducible and were checked against normal algorithms — or in this case, by nature itself, since it is molecules and atoms that are being analyzed. Likewise, the experiment marks what is arguably the first real-world use case for quantum computing.

This is a gross oversimplification, but the main issue with quantum computers is that they're not intrinsically deterministic. They arrive at a probable solution, although they have the advantage of considering most every possible input simultaneously — hence the insane speedups for highly specific tasks that fit the exact mold. A quantum computer's solutions are thus inherently error-prone, and it's necessary to reduce the error rate by several orders of magnitude to make practical applications viable.

The fact that Quantum Echo is de facto verifiable and "deterministic" comes by way of sending a "ping" into Willow's 105-qubit array and reading its effect millions per second, revealing information about the state of the system, thus (again, I'm oversimplifying) letting scientists peer into the outcome without significantly altering it.

This is apparently the largest such type of data collection in any quantum computing project, and was key to making the NMR analysis verifiable by helping reduce the error rate enough to arrive at a "deterministic" solution.

The team predicts that quantum computers are key to modelling quantum phenomena in nature, as in the aforementioned example, and it's not hard to imagine other similar applications now that a baseline for practicality has been reached. Google Quantum AI is now moving to Milestone 3 of its roadmap of building a long-lived logical qubit. Schrödinger would be happy.

A group of physicists from Harvard and MIT just built a quantum computer that ran continuously for more than two hours. Although it doesn’t sound like much versus regular computers (like servers that run 24/7 for months, if not years), this is a huge breakthrough in quantum computing. As reported by The Harvard Crimson, most current quantum computers run for only a few milliseconds, with record-breaking machines only able to operate for a little over 10 seconds.

Although two hours is still a bit limited, researchers say that the concept behind this could allow future quantum computers to run for much longer, maybe even indefinitely. “There is still a way to go and scale from where we are now,” says research associate Tout T. Wang, “But the roadmap is now clear based on the breakthrough experiments that we’ve done here at Harvard.”

The main difference between “regular” and quantum computing is that the latter uses qubits, which are subatomic particles, to hold and process data. But unlike the former, which retain information even without power, quantum computers can lose these qubits in a process called “atom loss”. This results in information loss and eventually system failure.

The research team addressed this by developing the “optical lattice conveyor belt” and “optical tweezers” to replace qubits as they’re lost. This system has 3,000 qubits and allows them to inject 300,000 atoms per second into the quantum computer, overcoming the qubit loss. “There’s now fundamentally nothing limiting how long our usual atom and quantum computers can run for,” said Wang. “Even if atoms get lost with a small probability, we can bring fresh atoms in to replace them and not affect the quantum information being stored in the system.”

Other team members believe that this breakthrough will allow us to have quantum computers that can run forever in about three years. Before this, experts said that it was at least half a decade away, if not longer. Quantum computing has the potential to change the way we do computing, breaking barriers in cryptography, finance, medicine, and more. However, despite these advancements, it’s unlikely that we’ll have personal quantum computers in our living rooms and offices within the next decade, unless you’re a physicist or researcher working on these cutting-edge devices.

Researchers from Boston University, UC Berkeley, and Northwestern University have created something that sounds straight out of a sci-fi movie: a “quantum light factory” squeezed onto a 1 mm² silicon chip. Built using a standard 45 nm CMOS manufacturing process—the same kind used for standard x86 and ARM processors—this breakthrough brings quantum hardware a step closer to the world of mass production. The work, published in Nature Electronic, could pave the way for scalable quantum computing that doesn't require exotic setups, instead relying on mass production techniques that we already employ today.

Think of this chip as a prototype for a future quantum factory line. It packs 12 tiny silicon loops, called "microring resonators," each acting as a generator of photon pairs with special quantum properties. These photon pairs are the lifeblood of many quantum technologies, but producing them usually requires fragile lab setups. Here, they’re generated directly on a chip no bigger than a fingernail.

What makes this remarkable is that the chip doesn't just produce quantum light; it’s more about keeping that light stable. Microring resonators are powerful but temperamental—small temperature changes or manufacturing quirks can throw them out of tune, halting the photon flow. To solve this, the researchers built a feedback system directly into the chip; each resonator has a tiny photodiode to monitor its performance, along with miniature heaters and control circuits that adjust it on the fly. This self-tuning approach means all 12 resonators can work together in perfect sync, without the bulky stabilization equipment usually needed.

“This is a small step, but an important one,” said Miloš Popović, associate professor at Boston University and one of the senior authors. “It shows we can build repeatable, controllable quantum systems in commercial semiconductor foundries.” That’s the real story here—this isn’t just a niche, bleeding-edge lab demo, but proof that quantum chips can be made with the same industrial techniques used to build CPUs and GPUs. Of course, quantum computing is nowhere near the maturity of standard semiconductors powering our devices today, but this is a step closer to that eventuality.

Let's talk about the most important revelation. The team’s choice of CMOS (Complementary Metal-Oxide-Semiconductor) is a game-changer. CMOS is the backbone of modern electronics, used by companies like TSMC to mass-produce everything from smartphones to supercomputers. While the 45 nm node used here isn’t cutting-edge, it’s proven, cost-effective, and compatible with the vast infrastructure of silicon manufacturing. The chip was built using a platform co-developed with GlobalFoundries and Ayar Labs, a company already leading the charge in optical interconnects for AI and high-performance computing.

This overlap with the AI world is no coincidence. Nvidia CEO Jensen Huang recently called out microring resonators—like those on this chip—as key components for scaling AI hardware via optical connections. This new research shows that the same photonics technology could also unlock scalable quantum systems. It’s not hard to imagine a future where quantum and AI hardware share similar silicon platforms. Moreover, Nvidia is already heavily investing in this field, so we can only expect development to ramp up.

The term “quantum light factory” isn’t just for flair, either. Just as classical chips rely on streams of electrons and optical networks depend on laser light, future quantum technologies will need a steady supply of quantum light. By proving that these quantum light sources can be built, stabilized, and replicated on silicon, the team has shown that quantum hardware can move beyond one-off experiments and into something that can be scaled like traditional computing.

Some of the researchers involved are already taking this expertise into industry roles. Team members have joined companies like PsiQuantum, Ayar Labs, and Google X, all of which are betting heavily on photonic and quantum technologies. It’s another sign that this field is moving rapidly from academic research to real-world products, even if they're sometimes more fun-oriented than revolutionary.

Backed by the National Science Foundation’s Future of Semiconductors (FuSe) program, the Packard Fellowship, and the Catalyst Foundation, the project shows how far interdisciplinary collaboration can go. Photonics, electronics, and quantum optics are worlds apart, but this chip proves they can be brought together on a commercial platform.

If Intel’s 4004 microprocessor marked the start of mass-produced computing power, this 1 mm² quantum light factory could be remembered as the first step toward mass-produced quantum hardware. What once needed an entire lab bench now fits onto a silicon wafer—and that’s a leap worth paying attention to. Who knows? Maybe a decade from now, you'll be seeing a new TSMC competing for quantum computing excellence, especially now that there's already an operating system for it out there.

IBM has shared its roadmap to deliver the “world's first large-scale, fault-tolerant quantum computer.” It claims that, due to be delivered to clients in 2029, IBM Starling will also be 20,000 times more powerful than today’s leading quantum computers. Furthermore, IBM says that to merely represent the computational state of Starling “would require the memory of more than a quindecillion (10⁴⁸) of the world's most powerful supercomputers.” However, we are used to rather lofty claims in the world of Quantum Computing, so let’s take a closer look.

On its newly published roadmap, IBM has set out several milestones, which will be powered by a handful of quantum computers and processor architectures ahead of the arrival of Starling. In 2026, we will see the first tantalizing demonstration of what IBM calls ‘quantum advantage.’ This is defined as the milestone where classical computers start to be outclassed by quantum computers in practical computing applications.

IBM Quantum Loon is set to debut later this year alongside the first Nighthawk chip, so we will assume that it will be the vehicle to demonstrate this first glimpse at quantum advantage. According to IBM, this will be a platform designed to test architecture components for its new quantum low-density parity check (qLDPC) code.

IBM Quantum Kookaburra will be passed the baton sometime in 2026, says IBM. This will feature the firm's first modular processor designed to store and process encoded information. Innovations in this design will be key to scaling fault-tolerant systems beyond a single chip.

Penultimately, IBM Quantum Cockatoo is expected to roll out in 2027. IBM’s press release says that this architecture “will link quantum chips together like nodes in a larger system, avoiding the need to build impractically large chip.” That is another important nod to scalability that it hopes to put into practice by leveraging the entanglement of component modules.

All the above milestones are hoped to culminate in Starling in 2029. They bring together testing and demonstrations based on two new technical papers IBM published today, providing background detail to its proposed large-scale, fault-tolerant architecture, and direction for its roadmap.

You read the astounding claims for this hardware innovation in the intro. But for more perspective, it is claimed that Starling will be capable of “running 100 million quantum operations using 200 logical qubits.”

When it arrives, Starling will “solve real-world challenges and unlock immense possibilities for business,” indicates Arvind Krishna, Chairman and CEO, IBM. Behind it there will be an architecture that can run “hundreds or thousands of logical qubits could run hundreds of millions to billions of operations,” reckons IBM. The most obvious beneficiaries of this computing power will include organizations involved in such as drug development, materials discovery, chemistry, and optimization.

IBM Starling isn’t quite the endpoint of the IBM Quantum roadmap, as published today. Its second-gen fault-tolerant quantum computing ISA will be Blue Jay. When this arrives, the computing platform may have scaled up to an astounding 1 billion gates and 2,000 logical qubits. Blue Jay isn’t expected to be here until 2033+.

Quantum advantage vs quantum supremacy

Members of the Quantum Internet Alliance (QIA) from TU Delft, QuTech, University of Innsbruck, INRIA, and CNRS have published a research paper detailing what they bill as the world's first operating system, QNodeOS, designed for quantum networks (via Phys.org). QNodeOS is designed to be hardware-agnostic and strives to abstract away low-level details from programmers for easy application development and deployment. It is a platform-independent framework capable of executing applications in a quantum network using high-level programming languages.

It is important to understand that QNodeOS targets quantum networks, rather than quantum computers. Put simply, quantum computers or processors like Microsoft's latest Majorana 1 chip are built to perform computations using the laws of quantum physics, such as entanglement and superposition. In contrast, quantum networks connect these quantum devices, facilitating coordination and are key for distributed quantum computing.

Quantum networks require an operating system to manage the flow of quantum information, manage entanglement, and synchronize all connected devices. Previous designs of quantum network applications relied on ad hoc, hardware-specific software, which was limited in functionality and lacked user-friendliness. Consider it the classical equivalent of low-level programming languages. High-level languages provide microarchitectural abstraction, enabling code portability across different designs. The quantum computing field requires similar advancements as Mariagrazia Luliano from QuTech explains: "The system is like the software on your computer at home: You don't need to know how the hardware works to use it."

The paper details how QNodeOS is compatible with different quantum chip designs: trapped ion processors and diamond color center (NV) based systems. Moreover, the platform supports multitasking for maximum hardware resource usage and efficiency. From what we can infer, this is done by translating high-level code to low-level NetQASM, which is then converted into hardware-specific instructions using what the paper defines as a QDriver.

The team demonstrated QNodeOS on two quantum nodes based on NV centers in diamonds, each with a single qubit. High-level instructions, explicitly mentioned as arbitrary, were executed following a basic QDC protocol in which a client node sends instructions to a server node.

This is the first implementation of high-level programming and execution of quantum network applications. The research further details long-distance connectivity measures to improve the architecture and reduce latency.

Microsoft's recently announced Majorana 1 quantum chip, which it claims uses a Topological Core architecture capable of packing a million qubits into a single quantum processor. However, some scientists are skeptical of the results delivered by Redmond. University of Pittsburgh Professor of Physics and Astronomy Sergey Frolov told The Register, “This is a piece of alleged technology that is based on basic physics that has not been established. So, this is a pretty big problem.”

Many scientists have their reservations about Microsoft's breakthrough, with Frolov even going as far as saying that the Majorana 1 chip is “essentially a fraudulent project.” He said that these concerns have been going on for years, especially as the company has previously retracted a 2018 paper it published about Majorana particles in 2018. Other scientists also expressed concerns because Microsoft's submission was missing some crucial details.

Aside from that, Professor Frolov said that he talked with fellow physicists and researchers whom Microsoft has shared its data with, and he said to The Register, "People were not impressed and there was a lot of criticism." The company is set to present its paper and more recent developments at the American Physical Society (APS) Global Physics Summit on March 15 to 20, but he's still skeptical that this will clear the air on Microsoft's claims.

Frolov said that Microsoft's planned presentation next week won't answer all the questions and concerns raised by experts based on what his contacts told him. He also added that the company's Majorana results are questionable — and without that, then the topological qubit that it claims will not work.

But whatever the case, the company’s presentation in the coming days will certainly reveal more information. And, in the end, if this quantum processor does not work, it will not have any commercial value, putting Microsoft at a disadvantage as it’s essentially throwing away money and resources it could have used to pursue a different path to achieve quantum computing.

In its defense, Microsoft’s researcher Chetan Nayak pointed out that they submitted the paper to Nature in March 2024, and that it was published eleven months later in February 2025. He said that the company has made significant progress since then, which will be presented at the American

Microsoft said that its Majorana 1 quantum chip can “observe and control Majorana particles to produce more reliable and scalable qubits, which are building blocks for quantum computers.” This particle was first theorized in 1937, but there’s still no definitive proof that it even exists. That’s why some scientists find it unbelievable that the tech giant has detected and put them to use in its quantum processor with its eight topological qubits.

Quantum computing is set to deliver processing power that is light-years away from what classical computing can achieve. However, this technology is also incredibly complex, and over 40 years of research still hasn’t resulted in a commercially viable quantum chip. Aside from Microsoft, several companies like IBM, Quantum Brilliance, QCI, and more are working on the problem, with each one taking a different approach.

Given humanity’s ever-increasing appetite for processing data, quantum computing is expected to have a market value of around $20 to $30 billion by 2030. This is likely to increase, especially as AI demands more and more processing power, with many companies investing billions into larger and more powerful data centers. So, the first company to come up with a commercially viable solution to the quantum computing problem will likely reap billions, if not trillions, of dollars in returns.

Earlier this morning, Amazon's AWS (or just Amazon Web Services) introduced Ocelot, a new scalable quantum computing solution claimed to reduce the costs of quantum computing error correction by up to 90% "compared to current approaches." Perhaps coincidentally, this scalable quantum computing solution debuted within less than ten days of Microsoft's own scalable quantum computing solution, Majorana 1, though that solution is focused more on accelerating raw quantum computing power through new "topoconductor" technology rather than lowering the pricing of existing quantum computing error correction capabilities.

What AWS' Ocelot chip seems to be prioritizing is quantum error correction, but especially making it less expensive to implement. When one considers the scale at which AWS operates — supporting over 1.45 million companies according to Avention Media, with the list including powerhouses like Netflix — seeking out ways to reduce costs for cutting-edge, scalable quantum computing solutions makes a lot of sense.

But how does it work, and why is it named Ocelot? Well, like the infamous Metal Gear Solid (1999) character, Ocelot is named after a specific breed of wild cat native to the Americas. AWS Ocelot is also stated to be built around a novel design that implements "cat qubits" that intrinsically suppress certain forms of errors, which should reduce the costs and resources required for quantum error correction. Add "additional quantum error components into a microchip that can be manufactured in a scalable fashion using processes borrowed from the microelectronics industry," and you're left with the AWS Ocelot architecture.

On the off-chance you're reading this without knowing what a regular old qubit is, just think of it as a quantum computing equivalent to a regular old computing bit. Regular old bits can only represent a "1" or a "0", while qubits can represent either of those or "a quantum superposition of 0 and 1", per Microsoft's documentation.

So, what exactly then is a "cat qubit"? Apparently, just a form of qubit with some forms of quantum error correction built in from the ground up. Basically, a qubit equivalent to ECC (error-checking code) RAM.

According to Oskar Painter, AWS director of Quantum Hardware, "With the recent advancements in quantum research, it is no longer a matter of if, but when practical, fault-tolerant quantum computers will be available for real-world applications. Ocelot is an important step on that journey. In the future, quantum chips built according to the Ocelot architecture could cost as little as one-fifth of current approaches, due to the drastically reduced number of resources required for error correction. Concretely, we believe this will accelerate our timeline to a practical quantum computer by up to five years."

These are some very strong, encouraging words — particularly considering Microsoft's own recent comments around Majorana 1 also point toward "quantum computers capable of solving meaningful, industrial-scale problems in years, not decades". Time will tell which megacorporation's approach to feasible quantum computing will lead to viable market products sooner, but the competition heating up like this should only prove a good thing for consumers and the enterprise in the long run.

Nvidia CEO Jensen Huang purportedly was responsible for tanking quantum computing stocks on Wednesday after a claim he had made about quantum computers' usefulness. Reuters reports that Jensen Huang believes quantum computer usefulness will only truly take place in 20 years, tanking several quantum computing stocks by more than 40%.

According to Reuters, Jensen Huang claimed on Tuesday, "If you kind of said 15 years... that'd probably be on the early side. If you said 30, it's probably on the late side. But if you picked 20, I think a whole bunch of us would believe it."

Jensen's statement alone caused Rigetti Computing (RGTI.O), D-Wave Quantum (QBTS.N), Quantum Computing (QUBT.O), and IonQ (IONQ.N), stock to fall more than 40%, combining for a lost market value of over $8 billion.

Apparently, the Nvidia CEO is not the only person with this view of quantum computers. Reuters reveals that Ivana Delevska, an investment chief of Spear Invest, holds the same views, saying the 15 to 20-year timeline "seems very realistic." It is the same amount of time Nvidia took to develop accelerated computing.

The Nvidia CEO's far-away viability claim on quantum computing comes as the technology has reached an inflection point in functionality and effectiveness. Just a few months ago, Chinese scientists could crack military-grade encryption, some of the world's highest and most complex encryption, with D-Wave quantum computers.

Quantum computers are also breaking new records. Google's new Willow quantum computing chip can solve a workload that would take a classical computer 10 septillion years to complete. Last month, China unveiled the fastest quantum computer the country has built, featuring 504 qubits of performance.

Quantum computing is still in its infancy, similar to the 1980s and 1990s of classical computing development. Only time will tell whether it will take 20 years to mature enough for mainstream use, as Huang has predicted.

A Chinese group led by China Telecom Quantum Computing Group has unveiled Xiaohong-504, a 504-qubit quantum computing chip, and Tianyan-504 superconducting quantum computer. The achievement can be considered a groundbreaking milestone in China, reports Interesting Engineering.

The heart of the Tianyan-504 is Xiaohong, a superconducting chip with 504 qubits, which is a record for China. The report says that Xiaohong's performance metrics, such as qubit lifetime, gate fidelity, and quantum circuit depth, are designed to rival international platforms like those offered by IBM. However, the primary focus of the processor and the quantum computer is on advancing the infrastructure for large-scale quantum systems rather than solely pursuing computing performance or quantum supremacy.

The first processor will be delivered to QuantumCTek, a quantum technology company based in Anhui Province, China. The chip will enable large-scale testing of QuantumCTek's kilo-qubit measurement and control system, which is the primary purpose of the processor.

While developers officially claim that the Tianyan-504 is designed to advance infrastructure for quantum systems, this is still one of the most powerful quantum computers on the planet. As of December 2024, the quantum computer with the highest number of qubits is Atom Computing's prototype, featuring 1,180 qubits. This system was announced on October 24, 2023, and it uses neutral atoms in optical lattices to achieve this qubit count. IBM has also made significant strides, with its Condor processor with over 1,000 qubits.

The Tianyan-504 quantum computer was developed through a joint effort by China Telecom Quantum Group (CTQG), the Center for Excellence in Quantum Information and Quantum Physics under the Chinese Academy of Sciences, and QuantumCTek. This device represents a leap forward in quantum computing capabilities, supporting advanced research and applications.

Eventually, Tianyan-504 will be integrated into the Tianyan quantum cloud platform, which provides access to quantum computing resources worldwide. Since its launch in November 2023, this platform has garnered over 12 million visits from users in more than 50 countries. The collaboration between CTQG and QuantumCTek aims to further expand these capabilities by developing additional quantum computing systems based on a new chip architecture.

China is unique in achieving quantum computational breakthroughs using both photonic and superconducting technologies, the report says. Two major accomplishments in this area include the development of Jiuzhang 2.0, which uses 113 detected photons, and Zuchongzhi 2.1, featuring 66 qubits. These advancements highlight the nation’s ability to innovate across multiple quantum domains.

Google has announced its latest quantum processor, dubbed Willow. In a blog post, the tech giant claims this state-of-the-art chip stands apart from competitors due to two major achievements: its incredible speed in computation benchmarks, and the way it reduces errors exponentially as qubits are scaled up.

As per our headline, Willow is a benchmarking beast. Google tested the chip in the random circuit sampling (RCS) benchmark, which is claimed to be "the classically hardest benchmark that can be done on a quantum computer today." It flew through the calculation and completed it in under five minutes. By way of contrast, one of the leading Supercomputers in 2024, Frontier, would take 10²⁵ or 10 septillion years to finish the same calculation, according to Google. That's 10,000,000,000,000,000,000,000,000 years, which exceeds the age of the known universe. Of course, running anything for that length of time would be impossible in practice. Our sun is estimated to only have another five billion years of life left, for example.

Google was also being generous with its estimates, with regard to Frontier, assuming full access to secondary storage as needed, without any bandwidth overhead.

Another major leap forward claimed for Willow is how it can reduce errors exponentially as it scales up to include more qubits. According to Google this advance "cracks a key challenge in quantum error correction that the field has pursued for almost 30 years." Google backs up its claims with a heavyweight research paper dubbed Quantum error correction below the surface code threshold and published for all to peruse.

In tests, Google used increasingly large arrays of qubits, scaling from a grid of 3x3 encoded qubits to a grid of 5x5, to a grid of 7x7. Each time it saw the error rate cut in half.

Willow and Google's next steps

Google says it fabricated Willow at its purpose-built state-of-the-art facility in Santa Barbara. Willow features 105 qubits, which may not sound like a big deal, but the company insists that it is "focusing on quality, not just quantity — because just producing larger numbers of qubits doesn’t help if they’re not high enough quality."

In the official blog, Willow is also compared to previous-generation Quantum chips from Google. For example, Willow is claimed to retain qubit excitation (T1) levels 5x longer than previous chips.

Google will continue to work with Willow to advance its quantum roadmap. Next up, Google hopes that it can "step into the realm of algorithms that are beyond the reach of classical computers and that are useful for real-world, commercially relevant problems." Only once these two goals can dovetail will we start to see the first promises of quantum computing delivered.

The Willow chips have come into view about five years since Google boldly claimed “quantum supremacy” with its 54-qubit Sycamore quantum processor. However, Sycamore proved to be quite controversial as IBM very publicly disputed Google's claims.

Chinese researchers claim to have uncovered a “real and substantial threat” to the classical cryptography widely used in banking and the military sectors. According to a report published by the SCMP, the researchers utilized a D-Wave quantum computer to mount the first successful quantum attack on widely used cryptographic algorithms. These algorithms, classed as substitution–permutation network (SPN) cryptographic algorithms, are at the heart of widely used standards like the Rivest-Shamir-Adleman (RSA) and Advanced Encryption Standard (AES).

The Chinese-language research paper is titled Quantum Annealing Public Key Cryptographic Attack Algorithm Based on D-Wave Advantage (PDF). The paper outlines how two technical approaches grounded in the quantum annealing algorithm can be used to challenge classical RSA cryptographic security.

The first attack route is “entirely based on D-Wave computers,” explains the paper. It coaxes the Canadian quantum computer into a cryptographic attack by presenting the combination of an optimization problem and exponential space search problem to the computer. The issues are solved using the Ising and QUBO models.

The second proposed attack incorporates classical computing-based cryptographic technology, such as the Schnorr signature algorithm and the Babai rounding technique, layered with a quantum annealing algorithm, to work “beyond the reach of traditional computing methods.”Applying the above techniques, with the help of the D-Wave quantum computer, the team led by Wang Chao of Shanghai University claim to have successfully breached the widely used SPN structure. Wang refused to give further details to the SCMP due to the sensitivity of this topic. However, the direction of travel means that AES-256 and other ‘military grade’ encryption algorithms are closer than ever before to being cracked. Moreover, quantum-reliant and quantum-aided techniques, as discussed in the paper, quantum-reliant and quantum-aided techniques could bring forward the day when current military and enterprise-grade encryption tech is good enough.

With the above news in mind, it is reassuring that organizations like the National Institute of Standards and Technology (NIST) are busy assessing and establishing post-quantum cryptographic algorithms designed to be crack-proof to future quantum computers.

DOOM has been ported to quantum computers, marking another milestone for this seminal 3D gaming title. However, the coder behind this feat admits that there is currently no quantum computer capable of executing (playing) this code right now. All is not lost, though, as Quandoom can run on a classical computer, even a modest laptop, using a lightweight QASM simulator.

Barcelona ICFO-based Quantum Information PhD student Luke Mortimer, AKA Lumorti, is behind this newest port of DOOM. In the ReadMe file accompanying the Quandoom 1.0.0 release, Lumorti quips that “It is a well-known fact that all useful computational devices ever created are capable of running DOOM,” and humorously suggests that Quandoom may be the first practical use found for quantum computers.

Quandoom’s quantum computer minimum specs are quite steep. Lumorti says that the QASM code requires 72,376 qubits and 80 million gates. That’s almost like saying your 2024 game needs an RTX 9090, as there is no such quantum computer available with that kind of spec. Thankfully DOOM fans can sidestep the physical hardware requirements on their home PCs by running the code in a QASM simulator.

Even with the simulator running on a humble laptop PC, Quandoom can achieve 10-20 FPS, according to the originator of this port. An animated GIF has been shared for a sample of on-screen Quandoom action. It looks pretty good in an Atari Battlezone (1980) kind of way. Lumorti calls this X-ray mode.

If you want to play Quandoom on your PC, once you have downloaded the files from GitHub, all you have to do is drag the Quandoom.qasm file onto the simulator (simulator.exe). Please note that the file will take some time to load, requiring about 5-6GB of RAM. Moreover, when you get into the game you will only have the first level, there’s no color, no music, no sound, and other aspects of the original that need tweaking to work in Quandoom.

For those who are into coding, Lumorti provides some tips for compiling the code for yourself, or Linux. It is also interesting to read that the Quandoom.qasm file is also not completely compliant, and abbreviations were used to cut it dramatically down in size to what could have been a 30GB+ file.

The developer is still working on Quandoom but admits to sometimes getting bored with the project. Lumorti’s work includes over 8,000 lines of C++ code, a small 3D engine, game logic, and more – with functions using quantum registers. Lastly, the quantum coder hints that if enough people are interested in the source it will be made available.

Quantum Brilliance (QB) and Oak Ridge National Laboratory (ORNL) have announced a strategic collaboration that will integrate the former’s diamond-based quantum accelerators with the latter’s state-of-the-art high-performance computing (HPC) infrastructure. The partners hope to validate the potential of QB’s compact, room-temperature quantum accelerators in tasks that go beyond the realms of classical computing.

Readers may not be familiar with Australia-based QB, but the U.S.-based ORNL is well known as the home of the Department of Energy’s Frontier supercomputer. Thus, this collaboration is a very serious step forward for quantum computing. Frontier sits proudly atop the Top500 list, thanks to its 8.7 million cores delivering up to 1,206 PFlop/s (while eating through 22,786kW). Perhaps this collaboration will one day take supercomputing even higher, hand-in-hand with greater efficiency.

In an email to Tom’s Hardware, QB says that the key initial objective of the collaboration will be to integrate “an on-premises cluster of QB’s quantum accelerators with ORNL’s HPC systems.” This will allow researchers to explore the performance of parallelized and hybridized quantum computing solutions. With the hybridized solution, both quantum and classical processors will work in concert.

“Parallel quantum computing holds transformative potential for scientific discovery and industrial applications that require high-performance computing,” said Dr. Travis Humble, Director at the Quantum Science Center at ORNL. Humble said the partnership with QB may end up “paving the way for groundbreaking advancements that will inform the design of future HPC infrastructure.”

Mark Luo, CEO of Quantum Brilliance, also fanfares the technological collaboration. Luo described the integration of “the world’s first cluster of room-temperature QPUs” with ORNL’s leading HPC infrastructure as a “critical milestone.”

It is also vital to co-develop new computational methods and software tools to make the most of the remarkable hardware partnership. It may only be when hardware, software, and new techniques for quantum-classical hybrid solutions reach a refined, coalesced state that we see the long-promised potential of quantum computing is realized.

QB is now selling its Gen1 Model rackmount accelerator to commercial customers. A PCIe card, packing one of its diamond-based QPUs (pictured in our gallery), is also in the works.

A University of California (UC) Riverside research team, led by physicist Peng Wei, has developed a two-dimensional interface superconductor by combining trigonal tellurium and gold. This new material has the potential to carry and process quantum information more reliably, making it a possible replacement for the materials used in current quantum computers, which are prone to errors.

The team added the non-magnetic trigonal tellurium with a thin film of gold that had a surface-state superconductor. From there, the researchers saw quantum states with well-defined spin polarization at the interface of the two materials, as reported by UC Riverside News. This meant that it could be used to create qubits, which are the fundamental units of quantum information.

“By creating a very clean interface between the chiral material (trigonal tellurium) and gold, we developed a two-dimensional interface superconductor,” Wei told UC Riverside News. “The interface superconductor is unique as it lives in an environment where the energy of the spin is six times more enhanced than those in conventional superconductors.”

This meant the qubits produced using this material could be more stable and robust. Aside from this, the trigonal tellurium's non-magnetic properties meant there was less chance for decoherence or when environmental information interfered with the quantum computer, thus leading to inaccurate results.

These properties of the new superconductor material could allow us to build larger, more reliable quantum computers. “Our material could be a promising candidate for developing more scalable and reliable quantum computing components,” said Wei to UC Riverside News. This could then overcome the limitations of current technologies used in quantum computing.

If the research team is successful in fielding this new material and delivering a quantum computer that is as reliable as the current non-quantum computers we have today, this could exponentially increase humanity’s computing power. For example, a quantum computer would only take seconds to compute something that an ordinary supercomputer would take thousands of years to finish.

As AI computing demands more processing power (and increased cooling and electricity), quantum computers could potentially deliver the computing power we need for our digital future without overrunning the face of the earth with data centers and power plants.

A research team at the Swiss Federal Institute of Technology Lausanne (EFPL) developed a 2D quantum cooling system that allowed it to reduce temperatures to 100 millikelvins by converting heat into electrical voltage. Very low temperatures are crucial for quantum computing, as quantum bits (qubits) are sensitive to heat and must be cooled down to less than 1K. Even the thermal energy generated by the electronics needed to run the quantum computer has been known to impact the performance of qubits.

"If you think of a laptop in a cold office, the laptop will still heat up as it operates, causing the temperature of the room to increase as well. In quantum computing systems, there is currently no mechanism to prevent this heat from disturbing the qubits," LANES PhD student Gabriele Pasquale explained.

However, most conventional cooling solutions no longer work efficiently (or don't work at all) at these temperatures. Because of this, heat-generating electronics must be separated from quantum circuits. This, in turn, adds noise and inefficiencies to the quantum computer, making it difficult to create larger systems that would run outside of lab conditions.

The headlining 2D cooling system was fabricated by a research team led by Andras Kis at EPFL's Laboratory of Nanoscale Electronics and Structures (LANES). Aside from its capability to cool down to 100mK, the more astounding innovation is that it does so at the same efficiency as current cooling technologies running at room temperature.

Pasquale said, "We are the first to create a device that matches the conversion efficiency of current technologies, but that operates at the low magnetic fields and ultra-low temperatures required for quantum systems. This work is truly a step ahead."

The LANES team called their technological advance a 2D quantum cooling system because of how it was built. At just a few atoms thick, the new material behaves like a two-dimensional object, and the combination of graphene and the 2D-thin structure allowed it to achieve highly efficient performance. The device operates using the Nernst effect, a thermomagnetic phenomenon where an electrical field is generated in a conductor that has both a magnetic field and two different temperatures on each side of the material.

Aside from its performance and efficiency, the 2D quantum cooling system is made from readily manufactured electronics. This means it could be easily added to quantum computers in other labs that require such low temperatures. Pasqual adds, "These findings represent a major advancement in nanotechnology and hold promise for developing advanced cooling technologies essential for quantum computing at millikelvin temperatures. We believe this achievement could revolutionize cooling systems for future technologies."

But even if some manufacturer mass produces this 2D cooling system that can hit sub-1K temperatures in the near future, don't expect to find it on Newegg to use it for overclocking your CPU, unless you plan to overclock a quantum computer in your living room lab. 

Quantum computers are apparently a "national security risk" for some countries, which have mysteriously issued identical restrictions on exports of quantum computing systems. France, Spain, the United Kingdom, the Netherlands, and Canada have all restricted the sale of quantum computers containing more than 34 qubits and above a certain error threshold.

The quantum computing export bans across all these countries have matching specific qualifications for what makes a quantum computer "dangerous enough" to deserve a ban. New Scientist, which first broke the news, contacted dozens of countries asking about the bans and was rebuffed, with the UK claiming that explaining the rationale behind the numbers would be a national security risk.

The countries who have issued the mysterious identical bans are all participants in the Wassenaar Agreement, an export control regime that facilitates a way for its 42 member nations to set limits on dual-use technologies or tech that can be used for both civilian and military applications. New Scientist also wrote to many other Wassenaar states who haven't yet set the matching bans about the source of the 34 qubit number or the potential for other nations to join in the bans. 

Milan Godin, a Belgian advisor to the EU, responded, "We are obviously closely following Wassenaar discussions on the exact technical control parameters relating to quantum," pointing to some level of international cooperation on the research behind the quantum restrictions. Experts in the quantum computing field have no clue where the numbers may have come from, with Christopher Monroe of IonQ saying, "I have no idea who determined the logic behind these numbers."

Quantum computers work fundamentally differently from standard computers. A qubit is analogous to a bit (or more accurately a transistor) in a computer, with higher qubit counts meaning higher power. While classical computers work with deterministic outcomes and dead-set calculations, quantum computers handle multiple variable problems at insane complexities that would stall the most powerful supercomputers of today. Our quantum computing explainer, linked at the top of this paragraph, goes in-depth into how quantum works and what it can do.

There is an immense amount of excitement but also fear for the tech, with governments concerned about the potential for military applications, like use for designing new nuclear or biological weapons, but quantum computers will eventually be able to crack the best cryptographic encryption in minutes. The only problem is that quantum computers today are not that good. Quantum computers have high error rates and require cooling solutions that take the qubits down to temperatures of -269° Celsius to function efficiently, so economics dictate that, barring a massive breakthrough in the tech, quantum will not pose any serious risk to anyone for years to come.

Other Wassenaar Agreement nations are likely to come out with similar trade restrictions on quantum computers in the coming days. However, the "national security risk" behind the bans is likely nothing to worry about based on the low capabilities of quantum systems. What is likely to happen in the short term is more national isolation of quantum computing research, as U.S.-based companies will no longer be able to expand to the UK or vice versa. 

IBM has ambitions to take the lead in quantum computing, with a new governmental partnership inbound to make this a reality. Japanese news outlet Nikkei reports on a leaked joint effort by IBM and Japan's National Institute of Advanced Industrial Science and Technology (AIST) that seeks to produce a quantum computer containing 10,000 qubits by 2029, vastly outclassing today's class-leading 133-qubit machines. 

Quantum computing has been a major focus of IBM for a few years now, and this newest step forward is a notable one. The 10,000 qubit machine explodes past IBM's current quantum roadmap, which doesn't even reach 2,000 qubits in commercial products until 2033 and beyond. (IBM had previously planned on a 2025 release of a 1,000 qubit computer, Condor, but the prototype has been shelved.) The goal of the 10,000-qubit machine is to run quantum calculations without a traditional supercomputer as backup, as modern 133-qubit machines often make enough mistakes to need support computers checking their work. 

IBM and AIST are set to announce the deal with a signed memorandum "in the coming days", according to Nikkei's source. The partnership has some major goals already set forth. IBM and AIST will seek to develop semiconductors and circuits that function in near-absolute zero temperatures. Quantum computers work more efficiently and correctly the closer to zero Kelvin they get, and today's largest machines have to have their qubits and chips/circuits in separate rooms or chambers, so creating components that function at extreme temperatures is a necessary step for advancing quantum research. 

AIST will leverage its patents, AI knowledge base, and connections to Japanese part-makers in the production of the forthcoming supercomputer. AIST will also help ensure future quantum supercomputers get into the hands of Japanese companies and industries, by providing training to companies and lobbying for the adoption of quantum by Japanese companies. This access to the lifeblood of Japanese industry is reportedly why IBM made the deal, the company's largest deal with a governmental industry in the quantum field.

It is important to note that much like every other part of computing, one massive number does not a great machine make. Qubit quality and efficiency increase quickly, which is why IBM has shelved recent attempts at 1,000-qubit computers in favor of their 133-qubit machines which beat 1,000-qubit prototypes in quality and efficiency. And just as traditional CPUs utilize hyper-threading and caching for better performance, quantum computing has other methods that increase its performance beyond simply boosting qubit numbers forever. After all, quantum computers become less stable at higher qubit counts, so the future of quantum will rely on smart engineering in keeping the 10,000-qubit and beyond computers of the future stable and inexpensive to run.

IBM and AIST's partnership may turn out to have a serious impact on quantum computing's growth and adoption. But today's quantum is still in its infancy, and has a long way to go before it becomes useful for consumers or professionals. IBM's 2021 quantum processor was recently out-classed by a team of researchers and a Commodore 64, proving IBM and the industry have a long road ahead of them to reach the point of true quantum utility.

SureCore announced its new SRAM modules for quantum computing SoCs that can function at cryogenic temperatures as low as 4 Kelvin (-269°C). The innovation comes as a major breakthrough for cooling and downsizing quantum computer data centers.

SureCore CEO Paul Wells said, "Data centres have a heat problem and we can potentially provide a solution." Wells elaborated, "As part of an InnovateUK funded project, we have worked closely with our partner Semiwise who have developed cryogenic transistor SPICE models. These have enabled us to port and tune our low power memory technology to work at temperatures down to 4K."

SureCore has been working on its cryogenic-friendly SRAM along with Semiwise and many other companies and research teams as part of a joint project by InnovateUK, the UK's government research agency. IUK's goal is to make cooling quantum data centers cheaper and more scalable, a prospect made difficult by how large-scale quantum computers currently operate.

Quantum computers, which use "qubits" rather than traditional bits, need near-absolute zero temperatures on their qubits for them to function most efficiently and correctly. At present, the qubits are kept in cryogenic cooling while the rest of the motherboard (controllers, RAM, etc.) are connected via expensive cabling from outside the cryostat. Most modern semiconductor technology is only rated to function at or above -40°C, necessitating a separated computer with its brain in a jar in another room.

SureCore's success could allow for new low-power and super-cold SRAM to potentially live with the qubits in a cryostat, reducing cooling costs and saving space.

Wells explained, "The goal was not only to develop memory for cryogenic operation, but also to exploit our power saving techniques so as to minimise the thermal load in the cryostat. For a datacentre operating at 77K [the temperature of liquid nitrogen], similar challenges apply and, by saving up to 50% of the memory power, a significant cut in thermal dissipation is possible with knock-on effects for the cooling power budget."

If your head is spinning with talk of qubits and quantum, give our quantum computers explainer a read. While quantum computing may yet prove to be the future, today it is still largely theoretical. Even IBM's own recent quantum computing experiments have been outperformed by a 1980s era Commodore 64.

A device called a quantum drum may serve as "a crucial piece in the very foundation for the Internet of the future with quantum speed and quantum security", says Mads Bjerregaard Kristensen, postdoc from the Niels Bohr Institute in a new research piece. The original research paper has an official briefing available for free on Phys.org, and can be found published in full in the Physical Review Letters journal for a subscription fee.

One key issue with quantum computing and sending quantum data ("qubits") over long distances is the difficulty of maintaining data in a fragile quantum state — where losing data or "decohering" becomes a much higher risk. Using a quantum drum at steps along the chain can prevent this data decoherence from occurring, enabling longer and even potentially global communication distances.

The current record for sending qubits over a long distance is held by China and Russia, and is about 3,800 km with only encryption keys sent as quantum data. The standard wired qubit transmission range is roughly 1000 kilometers before loss of photons ruins the data. Quantum drums could potentially address this limitation.

How does a 'quantum drum' work? In a similar manner to how existing digital bits can be converted into just about anything (sound, video, etc.), qubits can be converted as well. However, qubits require a level of precision literally imperceivable to the human eye, so converting qubits without data loss is quite difficult. The quantum drum seems like a potential answer. Its ceramic glass-esque membrane was shown to be capable of maintaining quantum states as it vibrates with stored quantum information.

Another important purpose served by these quantum drums is security. Were we to start transferring information between quantum computers over the standard Internet, it would inherit the same insecurities as our existing standards. That's because it would need to be converted to standard bits and bytes, which could become essentially free to decode in the not-so-distant quantum future.

By finding a quantum storage medium that doesn't lose any data and allows information to be transferred over much longer distances, the vision of a worthwhile "Quantum Internet" begins to manifest as a real possibility, and not simply the optimism of quantum computing researchers.

Quantum computing research continues to be a major area of interest, often with highly technical discussions and details on the technology. A research paper on quantum drums and their potential of course doesn't mean that this technique will prove to be commercially viable. Still, every little step forward creates new opportunities for our seemingly inevitable quantum-powered future.

A paper released during the SIGBOVIK 2024 conference details an attempt to simulate the IBM ‘quantum utility’ experiment on a Commodore 64. The idea might seem preposterous - pitting a 40-year-old home computer against a device powered by 127-Qubit ‘Eagle’ quantum processing unit (QPU). However, the anonymous researcher(s) conclude that the ‘Qommodore 64’ performed faster, and more efficiently, than IBM’s pride-and-joy, while being “decently accurate on this problem.”

At the beginning of the paper, the researchers admit that their ‘Qommodore 64’ project is “a joke,” but, sadly for IBM, its proof of quantum utility was also built upon shaky foundations, and the Qommodore 64 team came up with some convincing-looking benchmarks. There was some controversy about IBM’s claims at the time, and we are reminded it took just five days for the quantum experiment to be simulated on an ordinary MacBook M1 Pro laptop. The jokey Quantum Disadvantage paper (PDF link, headlining section starts at page 199) ports this experiment to a machine packing the far more humble MOS Technology 6510 processor.

To get deep into the weeds with the quantum theory and math behind the quantum utility experiment, please follow the above PDF link. However, to summarize, the C64-based experiment uses the sparse Pauli dynamics technique developed by Beguŝić, Hejazi, and Chan to approximate the behavior of ferromagnetic materials. Famously, IBM claimed such calculations were “too difficult to perform on a classical computer to an acceptable accuracy, using the leading approximation techniques,” recalls the paper. Not quite, and as already mentioned above, an ordinary laptop can obtain similar results.

The anonymous C64 user(s) provide some interesting details of their quantum-defeating feat. Their aggressively truncated and shallow depth-first search model used just 15kB of the spacious 64kB available on the iconic Commodore machine. Meanwhile, the final code consisted of about 2,500 lines of 6502 assembly, stored on a cartridge that fitted in the C64’s expansion port. This code was handled by the mighty 1 MHz 8-bit MOS 6510 CPU. The C64 took approx 4 minutes per data point. (Testing the same code on a modern laptop achieved roughly 800μs per data point.)

In conclusion, the researcher(s) asserts that the ‘Qommodore 64’ is “faster than the quantum device datapoint-for-datapoint… it is much more energy efficient… and it is decently accurate on this problem.” On the topic of how applicable this research is to other quantum problems, it is snarkily suggested that “it probably won’t work on almost any other problem (but then again, neither do quantum computers right now).” Overall, it is difficult to know whether the results are entirely genuine, though a lot of detail is provided and the linked research references in the paper seem genuine.

We know many readers are retro computing enthusiasts, as well as DIYers and makers. So it is good to know that the author(s) of this paper say that they will provide source code to allow others to replicate their results. However, source code will only be supplied in one of three formats, they say: “a copy handwritten on papyrus, a slide-show of blurry screenshots recorded on a VHS tape, or that I dictate it to you personally over the phone.” So please add an extra pinch of salt to this story for that.

Australia’s National Supercomputing and Quantum Computing Innovation Hub is set to use Nvidia Grace Hopper Superchips to push the boundaries of quantum computing. In a news release sent to Tom’s Hardware, Nvidia says that the Pawsey Supercomputing Research Centre in Perth will deploy eight Nvidia Grace Hopper Superchip nodes to power the open-source CUDA Quantum computing platform. It is expected that the new supercomputer will be able to deliver up to 10x higher processing performance than the center has access to now.

The stated purpose of the Grace Hopper Superchip nodes in Pawsey is for researchers at the center to run powerful simulation tools and hopefully make breakthroughs in fields like algorithm discovery, device design, quantum machine learning, chemistry simulations, image processing for radio, astronomy, financial analysis, bioinformatics, and more. It is also hoped to advance scientific exploration in Australia and the world.

The Nvidia Grace Hopper Superchip’s Grace CPU and Hopper GPU architectures are central to the above aspirations and the Nvidia cuQuantum software development kit. This powerful hardware and software melding forms the green team’s open-source hybrid quantum computing platform, known more succinctly as the CUDA Quantum platform.

At Pawsey, eight Grace Hopper Superchip nodes based on the Nvidia MGX modular architecture will be deployed, according to the press release we received. It explains that “GH200 Superchips eliminates the need for a traditional CPU-to-GPU PCIe connection by combining an Arm-based Nvidia Grace CPU with an Nvidia H100 Tensor Core GPU in the same package, using Nvidia NVLink-C2C chip interconnects MGX modular architecture.” A significant benefit of the new interconnects is that the bandwidth between the GPU and CPU is seven times greater than the latest PCIe technology. Moreover, the researchers in Australia are looking forward to a ten-fold increase in application performance when processing data sets measured in terabytes.

We asked Nvidia for some more technical details about the Superchip nodes at Pawsey. It turns out that each node will be using 'just' a single GH200 with Grace CPU and a H100 96GB of HBM3. Thus, the new installation at Pawsey Supercomputing Research Centre in Perth will feature eight nodes each with one GH200 for a total of 8x GH200 (8x Grace CPU and 8x H100 96GB GPU).

One of the other major appealing features of the Nvidia CUDA Quantum platform is that it offers a hybrid solution bridging the worlds of quantum and classical computing. Nvidia claims it is a first-of-its-kind and “enables dynamic workflows across disparate system architectures.” Researchers can use this platform to integrate and program quantum processing units (QPUs), GPUs, and CPUs in one system. It is also, of course, GPU-accelerated for scalability and performance.

The installation of the new Nvidia Grace Hopper Superchip nodes at Pawsey isn’t purely for advancing knowledge or solving some esoteric scientific problems. The Australian government also reckons investments like this make good business sense. According to Australia’s national science agency, the domestic market opportunity offered by quantum computing is set to be worth $2.5 billion per annum. Additionally, it is estimated that quantum advances could create 10,000 new Australian jobs by 2040.

Following recent demos of quantum communication using undersea fiber optics, scientists from Russia and China have successfully demonstrated quantum communication over satellite, using China's quantum satellite (dubbed "Mozi"), as the two countries lay the groundwork for advanced encrypted communication networks that are safe from prying Western eyes — possibly for BRICS-aligned countries. The test was conducted using the satellite from a ground station close to Moscow, Russia, to another station based near Urumqi, China, over 3,800 kilometers, according to the South China Morning Post.

The satellite used to achieve this quantum communication, Mozi (also called Micius), has been in orbit since 2016 and is managed primarily by the Chinese Academy of Science. The collaboration with Russian scientists started in 2020. Then, in March 2023, a full quantum communication experiment was conducted between two ground stations, using encryption keys from Mozi to distribute two coded messages.

The coded messages used in the March 2023 test were fairly innocuous, before you get too excited— just a quote from Chinese philosopher Mozi and an equation from Soviet physicist Lev Landau. The more recent "full cycle" quantum communication test on December 14th, 2023, also used a few (presumably harmless) quantum key-encoded images.

For those unfamiliar, "quantum communication" refers to communication using "qubits". Qubits, like traditional "bits," can contain binary information. However, qubits are also incredibly fragile to outside interference, which means that it's very easy for a quantum computer to tell if qubits have been intercepted or interfered with in some way. 

Quantum communications are — in theory, at least — the most secure possible form of data transmission, exploiting quantum mechanics to be unbreakable without detection. The main drawbacks are the limited adoption/evolution of quantum computing and fundamental range weaknesses in current qubit transmission technologies— reportedly about 1,000 kilometers due to photon loss over long-distance wiring. 

While we can freely send regular old bits or bytes worldwide, it's much harder to do that with more fragile qubits since they're generally more prone to degradation. Advancements like this (satellites boost the effective range by as much as 3,800 kilometers) may start pushing us closer to a future of quantum communication networks, though, since international ranges are now clearly within the range of possibility. 

However, the communication test was ultimately just two regular old static images. How well quantum communication may fare in real-time video calling isn't currently known. However, a 2017 voice calling test was done between China and Austria, which could make high-bandwidth quantum communication feasible one day.

While quantum communications finally being achievable over undersea fiber optics and satellite communications is impressive, it isn't likely to become a dominant form of communication for any consumer, business, or state for quite some time. The rapidly approaching future of quantum computing may still be full of surprises, though, so who knows?

Alexey Fedorov of Russia's National University of Science and Technology and the Russian Quantum Center states, "Quantum communication networks could have many uses, but for now, quantum systems would ideally be suited to scientific research." 

While the technology is developing, it still seems there will be some time before it's used on a large scale for any real purpose. But Fedorov did speak of interest in quantum computing from the Russian finance sector and even alluded to the possibility of a quantum communication network between BRICS nations (Brazil, Russia, India, China, and South Africa) in the future.

At its Quantum Summit 2023, IBM took the stage with an interesting spirit: one of almost awe at having things go their way. But the quantum of today – the one that’s changing IBM’s roadmap so deeply on the back of breakthrough upon breakthrough – was hard enough to consolidate. As IBM sees it, the future of quantum computing will hardly be more permissive. IBM announced cutting-edge devices at the event, including the 133-qubit Heron Quantum Processing Unit (QPU), which is the company's first utility-scale quantum processor, and the self-contained Quantum System Two, a quantum-specific supercomputing architecture. And further improvements to the cutting-edge devices are ultimately required.

Each breakthrough that afterward becomes obsolete is another accelerating bump against what we might call quantum's "plateau of understanding." We’ve already crested this plateau with semiconductors, so much so that the latest CPUs and GPUs are reaching practical, fundamental design limits where quantum effects start ruining our math. Conquering the plateau means that utility and understanding are now enough for research and development to be somewhat self-sustainable – at least for a Moore’s-law-esque while.

IBM’s Quantum Summit serves as a bookend of sorts for the company’s cultural and operational execution, and its 2023 edition showcased an energized company that feels like it's opening up the doors towards a "quantum-centric supercomputing era." That vision is built on the company's new Quantum Processing Unit, Heron, which showcases scalable quantum utility at a 133-qubit count and already offers things beyond what any feasible classical system could ever do. Breakthroughs and a revised understanding of its own roadmap have led IBM to present its quantum vision in two different roadmaps, prioritizing scalability in tandem with useful, minimum-quality rather than monolithic, hard-to-validate, high-complexity products.

IBM's announced new plateau for quantum computing packs in two particular breakthroughs that occurred in 2023. One breakthrough relates to a groundbreaking noise-reduction algorithm (Zero Noise Extrapolation, or ZNE) which we covered back in July – basically a system through which you can compensate for noise. For instance, if you know a pitcher tends to throw more to the left, you can compensate for that up to a point. There will always be a moment where you correct too much or cede ground towards other disruptions (such as the opponent exploring the overexposed right side of the court). This is where the concept of qubit quality comes into account – the more quality your qubits, the more predictable both their results and their disruptions and the better you know their operational constraints – then all the more useful work you can extract from it.

The other breakthrough relates to an algorithmic improvement of epic proportions and was first pushed to Arxiv on August 15th, 2023. Titled “High-threshold and low-overhead fault-tolerant quantum memory,” the paper showcases algorithmic ways to reduce qubit needs for certain quantum calculations by a factor of ten. When what used to cost 1,000 qubits and a complex logic gate architecture sees a tenfold cost reduction, it’s likely you’d prefer to end up with 133-qubit-sized chips – chips that crush problems previously meant for 1,000 qubit machines.

Enter IBM’s Heron Quantum Processing Unit (QPU) and the era of useful, quantum-centric supercomputing. 

The Quantum Roadmap at IBM’s Quantum Summit 2023

The two-part breakthroughs of error correction (through the ZNE technique) and algorithmic performance (alongside qubit gate architecture improvements) allow IBM to now consider reaching 1 billion operationally useful quantum gates by 2033. It just so happens that it’s an amazing coincidence (one born of research effort and human ingenuity) that we only need to keep 133 qubits relatively happy within their own environment for us to extract useful quantum computing from them – computing that we wouldn’t classically be able to get anywhere else.

The “Development” and “Innovation” roadmap showcase how IBM is thinking about its superconducting qubits: as we’ve learned to do with semiconductors already, mapping out the hardware-level improvements alongside the scalability-level ones. Because as we’ve seen through our supercomputing efforts, there’s no such thing as a truly monolithic approach: every piece of supercomputing is (necessarily) efficiently distributed across thousands of individual accelerators. Your CPU performs better by knitting and orchestrating several different cores, registers, and execution units. Even Cerebra’s Wafer Scale Engine scales further outside its wafer-level computing unit. No accelerator so far – no unit of computation - has proven powerful enough that we don’t need to unlock more of its power by increasing its area or computing density. Our brains and learning ability seem to provide us with the only known exception.

IBM’s modular approach and its focus on introducing more robust intra-QPU and inter-QPU communication for this year’s Heron shows it’s aware of the rope it's walking between quality and scalability. The thousands of hardware and scientist hours behind developing the tunable couplers that are one of the signature Heron design elements that allow parallel execution across different QPUs is another. Pushing one lever harder means other systems have to be able to keep up; IBM also plans on steadily improving its internal and external coupling technology (already developed with scalability in mind for Heron) throughout further iterations, such as Flamingo’s planned four versions which still “only” end scaling up to 156 qubits per QPU. 

Considering how you're solving scalability problems and the qubit quality x density x ease of testing equation, the ticks - the density increases that don't sacrifice quality and are feasible from a testing and productization standpoint - may be harder to unlock. But if one side of development is scalability, the other relates to the quality of whatever you’re actually scaling – in this case, IBM’s superconducting qubits themselves. Heron itself saw a substantial rearrangement of its internal qubit architecture to improve gate design, accessibility, and quantum processing volumes – not unlike an Intel tock. The planned iterative improvements to Flamingo's design seem to confirm this.

Utility-Level Quantum Computing

There’s a sweet spot for the quantum computing algorithms of today: it seems that algorithms that fit roughly around a 60-gate depth are complex enough to allow for useful quantum computing. Perhaps thinking about Intel’s NetBurst architecture with its Pentium 4 CPUs is appropriate here: too deep an instruction pipeline is counterproductive, after a point. Branch mispredictions are terrible across computing, be it classical or quantum. And quantum computing – as we still currently have it in our Noisy Intermediate-Scale Quantum (NISQ)-era – is more vulnerable to a more varied disturbance field than semiconductors (there are world overclocking records where we chill our processors to sub-zero temperatures and pump them with above-standard volts, after all). But perhaps that comparable quantum vulnerability is understandable, given how we’re essentially manipulating the essential units of existence – atoms and even subatomic particles – into becoming useful to us.

Useful quantum computing doesn’t simply correlate with an increasing number of available in-package qubits (announcements of 1,000-qubit products based on neutral atom technology, for instance). But useful quantum computing is always stretched thin throughout its limits, and if it isn’t bumping against one fundamental limit (qubit count), it’s bumping against another (instability at higher qubit counts); or contending with issues of entanglement coherence and longevity; entanglement distance and capability; correctness of the results; and still other elements. Some of these scalability issues can be visualized within the same framework of efficient data transit between different distributed computing units, such as cores in a given CPU architecture, which can themselves be solved in a number of ways, such as hardware-based information processing and routing techniques (AMD’s Infinity Fabric comes to mind, as does Nvidia's NVLink).

This feature of quantum computing already being useful at the 133-qubit scale is also part of the reason why IBM keeps prioritizing quantum computing-related challenges around useful algorithms occupying a 100 by 100 grid. That quantum is already useful beyond classical, even in gate grids that are comparably small to what we can achieve with transistors, and points to the scale of the transition – of how different these two computational worlds are.

Then there are also the matters of error mitigation and error correction, of extracting ground-truth-level answers to the questions we want our quantum computer to solve. There are also limitations in our way of utilizing quantum interference in order to collapse a quantum computation at just the right moment that we know we will obtain from it the result we want – or at least something close enough to correct that we can then offset any noise (non-useful computational results, or the difference of values ranging between the correct answer and the not-yet-culled wrong ones) through a clever, groundbreaking algorithm.

The above are just some of the elements currently limiting how useful qubits can truly be and how those qubits can be manipulated into useful, algorithm-running computation units. This is usually referred to as a qubit’s quality, and we can see how it both does and doesn’t relate to the sheer number of qubits available. But since many useful computations can already be achieved with 133-qubit-wide Quantum Processing Units (there’s a reason IBM settled on a mere 6-qubit increase from Eagle towards Heron, and only scales up to 156 units with Flamingo), the company is setting out to keep this optimal qubit width for a number of years of continuous redesigns. IBM will focus on making correct results easier to extract from Heron-sized QPUs by increasing the coherence, stability, and accuracy of these 133 qubits while surmounting the arguably harder challenge of distributed, highly-parallel quantum computing. It’s a one—two punch again, and one that comes from the bump in speed at climbing ever-higher stretches of the quantum computing plateau.

But there is an admission that it’s a barrier that IBM still wants to punch through – it’s much better to pair 200 units of a 156-qubit QPU (that of Flamingo) than of a 127-qubit one such as Eagle, so long as efficiency and accuracy remain high. Oliver Dial says that Condor, "the 1,000-qubit product", is locally running – up to a point. It was meant to be the thousand-qubit processor, and was a part of the roadmap for this year’s Quantum Summit as much as the actual focus, Heron - but it’s ultimately not really a direction the company thinks is currently feasible.

IBM did manage to yield all 1,000 Josephson Junctions within their experimental Condor chip – the thousand-qubit halo product that will never see the light of day as a product. It’s running within the labs, and IBM can show that Condor yielded computationally useful qubits. One issue is that at that qubit depth, testing such a device becomes immensely expensive and time-consuming. At a basic level, it’s harder and more costly to guarantee the quality of a thousand qubits and their increasingly complex possibility field of interactions and interconnections than to assure the same requirements in a 133-qubit Heron. Even IBM only means to test around a quarter of the in-lab Condor QPU’s area, confirming that the qubit connections are working.

But Heron? Heron is made for quick verification that it’s working to spec – that it’s providing accurate results, or at least computationally useful results that can then be corrected through ZNE and other techniques. That means you can get useful work out of it already, while also being a much better time-to-market product in virtually all areas that matter. Heron is what IBM considers the basic unit of quantum computation - good enough and stable enough to outpace classical systems in specific workloads. But that is quantum computing, and that is its niche.

The Quantum-Centric Era of Supercomputing

Heron is IBM’s entrance into the mass-access era of Quantum Processing Units. Next year’s Flamingo builds further into the inter-QPU coupling architecture so that further parallelization can be achieved. The idea is to scale at a base, post-classical utility level and maintain that as a minimum quality baseline. Only at that point will IBM maybe scale density and unlock the appropriate jump in computing capability - when that can be similarly achieved in a similarly productive way, and scalability is almost perfect for maintaining quantum usefulness.

There’s simply never been the need to churn out hundreds of QPUs yet – the utility wasn’t there. The Canaries, Falcons, and Eagles of IBM’s past roadmap were never meant to usher in an age of scaled manufacturing. They were prototypes, scientific instruments, explorations; proofs of concept on the road towards useful quantum computing. We didn’t know where usefulness would start to appear. But now, we do – because we’ve reached it.

Heron is the design IBM feels best answers that newly-created need for a quantum computing chip that actually is at the forefront of human computing capability – one that can offer what no classical computing system can (in some specific areas). One that can slice through specific-but-deeper layers of our Universe. That’s what IBM means when it calls this new stage the “quantum-centric supercomputing” one.

Classical systems will never cease to be necessary: both of themselves and the way they structure our current reality, systems, and society. They also function as a layer that allows quantum computing itself to happen, be it by carrying and storing its intermediate results or knitting the final informational state – mapping out the correct answer Quantum computing provides one quality step at a time. The quantum-centric bit merely refers to how quantum computing will be the core contributor to developments in fields such as materials science, more advanced physics, chemistry, superconduction, and basically every domain where our classical systems were already presenting a duller and duller edge with which to improve upon our understanding of their limits.

Quantum System Two, Transmon Scalability, Quantum as a Service

However, through IBM’s approach and its choice of transmon superconducting qubits, a certain difficulty lies in commercializing local installations. Quantum System Two, as the company is naming its new almost wholesale quantum computing system, has been shown working with different QPU installations (both Heron and Eagle). When asked about whether scaling Quantum System Two and similar self-contained products would be a bottleneck towards technological adoption, IBM’s CTO Oliver Dial said that it was definitely a difficult problem to solve, but that he was confident in their ability to reduce costs and complexity further in time, considering how successful IBM had already proven in that regard. For now, it’s easier for IBM’s quantum usefulness to be unlocked at a distance – through the cloud and its quantum computing framework, Quiskit – than it is to achieve it by running local installations. 

Quiskit is the preferred medium through which users can actually deploy IBM's quantum computing products in research efforts – just like you could rent X Nvidia A100s of processing power through Amazon Web Services or even a simple Xbox Series X console through Microsoft’s xCloud service. On the day of IBM's Quantum Summit, that freedom also meant access to the useful quantum circuits within IBM-deployed Heron QPUs. And it's much easier to scale access at home, serving them through the cloud, than delivering a box of supercooled transmon qubits ready to be plugged and played with.

That’s one devil of IBM’s superconducting qubits approach – not many players have the will, funding, or expertise to put a supercooled chamber into local operation and build the required infrastructure around it. These are complex mechanisms housing kilometers of wiring - another focus of IBM’s development and tinkering culminating in last year’s flexible ribbon solution, which drastically simplified connections to and from QPUs.

Quantum computing is a uniquely complex problem, and democratized access to hundreds or thousands of mass-produced Herons in IBM’s refrigerator-laden fields will ultimately only require, well… a stable internet connection. Logistics are what they are, and IBM’s Quantum Summit also took the necessary steps to address some needs within its Quiskit runtime platform by introducing its official 1.0 version. Food for thought is realizing that the era of useful quantum computing seems to coincide with the beginning of the era of Quantum Computing as a service as well. That was fast.

Closing Thoughts

The era of useful, mass-producible, mass-access quantum computing is what IBM is promising. But now, there’s the matter of scale. And there’s the matter of how cost-effective it is to install a Quantum System Two or Five or Ten compared to another qubit approach – be it topological approaches to quantum computing, or oxygen-vacancy-based, ion-traps, or others that are an entire architecture away from IBM’s approach, such as fluxonium qubits. It’s likely that a number of qubit technologies will still make it into the mass-production stage – and even then, we can rest assured that everywhere in the road of human ingenuity lie failed experiments, like Intel’s recently-decapitated Itanium or AMD’s out-of-time approach to x86 computing in Bulldozer.

It's hard to see where the future of quantum takes us, and it’s hard to say whether it looks exactly like IBM’s roadmap – the same roadmap whose running changes we also discussed here. Yet all roadmaps are a permanently-drying painting, both for IBM itself and the technology space at large. Breakthroughs seem to be happening daily on each side of the fence, and it’s a fact of science that the most potential exists the earlier the questions we ask. The promising qubit technologies of today will have to answer to actual interrogations on performance, usefulness, ease and cost of manipulation, quality, and scalability in ways that now need to be at least as good as what IBM is proposing with its transmon-based superconducting qubits, and its Herons, and scalable Flamingos, and its (still unproven, but hinted at) ability to eventually mass produce useful numbers of useful Quantum Processing Units such as Heron. All of that even as we remain in this noisy, intermediate-scale quantum (NISQ) era.

It’s no wonder that Oliver Dial looked and talked so energetically during our interview: IBM has already achieved quantum usefulness and has started to answer the two most important questions – quality and scalability, Development, and Innovation. And it did so through the collaboration of an incredible team of scientists to deliver results years before expected, Dial happily conceded. In 2023, IBM unlocked useful quantum computing within a 127-qubit Quantum Processing Unit, Eagle, and walked the process of perfecting it towards the revamped Heron chip. That’s an incredible feat in and of itself, and is what allows us to even discuss issues of scalability at this point. It’s the reason why a roadmap has to shift to accommodate it – and in this quantum computing world, it’s a great follow-up question to have.

Perhaps the best question now is: how many things can we improve with a useful Heron QPU? How many locked doors have sprung ajar?

This week, Shenzhen, China-based company SpinQ claimed the shipment of the first China-made Quantum Processing Unit (QPU), Shaowei, based on superconducting qubit technology. The claim that SpinQ is now the first Chinese quantum-focused company to sell its technologies beyond mainland China - whilst leveraging a superconducting qubit design setup at that - seems to point to a newfound source of quantum processing chips for any global players that wouldn't be easily provided for by the western market. According to SpinQ, the recipient of its Shaowei chips (and the first international customer of the company's product) is located somewhere in the Middle East.

Qubits are the quantum computing equivalent of a classical bit; while bits are deterministic and can only ever represent either a 0 or a 1, qubits are probabilistic, and consider the entire solution space between both. Recent advantages have brought quantum computing up to a point where the best products actually have enough quantum volume (a measure of a quantum computer's overall performance) to provide useful calculations that are beyond what could be possible with classical computers or even supercomputers.

Established in 2018, SpinQ recently drew our attention to its quantum processing offerings by providing "quantop" solutions: these are relatively simple, one-to-three-qubits, desktop-based quantum processing systems meant for the research and education markets. Far and away from providing any significant quantum computing capability, the "quantops" delivered by SpinQ used nuclear magnetic resonance qubits. But the new Shaowei QPU, being based on superconducting qubit technology that's theoretically similar to IBM's approach, means that the company is branching out its understanding and capability to deliver useful quantum computers. SpinQ says Shaowei utilizes a stable, all-solid-state system that's especially geared towards taking advantage of and reusing more classical chip manufacturing technology. 

Considering how China keeps skirting the impact of the US technological sanctions and has achieved an internal 5 nm chip manufacturing milestone without the aid of US tech, this looks like a winning bet.

According to SpinQ, its new superconducting-qubit Shaowei chips were built completely in-house through the company's factories in the Shenzhen-Hong Kong Innovation and Technology Cooperation Zone. Its approach is much like IBM's (and like that of most quantum tech suppliers) in that the company aims to provide a "full-stack" approach to quantum computing by delivering every required element of the ecosystem: quantum processing units, low-temperature electronics, temperature and qubit measurement and control systems, as well as software and algorithm development applications.

Unfortunately, there's little information available on what exactly makes a Shaowei chip, well, tick. Qubit number and connection density are useful metrics, but SpinQ provides none. However, the company claims the coherence time for the qubits inside Shaowei is in the order of 10-100 microseconds (where a higher window of qubit coherence means the qubits are processing information without any catastrophic data loss). But in quantum computing (and every computational effort), results have to be trusted: SpinQ mentioned that Shaowei can perform both single and double-bit gate operations (in the nanosecond scale) and can achieve more than 99.9% single-bit gate fidelity and more than 98% double-bit gate fidelity. While that may sound like a lot, it really isn't: when your CPU can process millions of calculations per second, that 0.01% error rate can add up quickly, and impact the validity (and truthfulness) of the computed results.

It remains to be seen where SpinQ will take its superconducting qubits next, but it's perhaps surprising that China is already selling Quantum Processing Units overseas before 2023 comes to a close.

A commercial smartphone or Linux computer can be used to crack RSA-2048 encryption, according to a prominent research scientist. Dr Ed Gerck is preparing a research paper with the details but couldn’t hold off from bragging about his incredible quantum computing achievement (if true) on his LinkedIn profile. Let us be clear: the claims seem spurious, but it should be recognized that the world isn’t ready for an off-the-shelf system that can crack RSA-2048, as major firms, organizations, and governments haven’t yet transitioned to encryption tech that is secured for the post-quantum era.

In his social media post, Gerck states that a humble device like a smartphone can crack the strongest RSA encryption keys in use today due to a mathematical technique that “has been hidden for about 2,500 years – since Pythagoras.” He went on to make clear that no cryogenics or special materials were used in the RSA-2048 key-cracking feat.

BankInfoSecurity reached out to Gerck in search of some more detailed information about his claimed RSA-2048 breakthrough and in the hope of some evidence that what is claimed is possible and practical. Gerck shared an abstract of his upcoming paper. This appears to show that instead of using Shor's algorithm to crack the keys, a system based on quantum mechanics was used, and it can run on a smartphone or PC.

In some ways, it is good that the claimed breakthrough doesn’t claim to use Shor’s algorithm. Alan Woodward, a professor of computer science at the University of Surrey, told BankInfoSecurity that no quantum computer in existence has enough gates to implement Shor’s algorithm and break RSA-2048. So at least this part of Gerck’s explanation checks out. However, the abstract of Gerck’s paper looks like it is “all theory proving various conjectures - and those proofs are definitely in question,” according to Woodward.

The BankInfoSecurity report on Gerck’s “QC Algorithms: Faster Calculation of Prime Numbers” paper quotes other skeptics, most of whom are waiting for more information and proofs before they organize a standing ovation for Gerck.

If you head over to Dr Gerck’s LinkedIn post you can see that the scientist has been busy answering community queries ahead of a full paper publication. He also isn’t afraid of stoking controversy by saying the likes of IBM and Google are “plain wrong” in their interpretations of superposition and entanglement in quantum computing.

Gerck is the developer of a “post-quantum, HIPAA compliant, end-to-end, patent-free, export-free, secure online solution” for cryptography, which he says can be used to replace RSA. This would be handy if his RSA-2048 cracking claims are correct. Naturally, that also raises the question of whether this 'crack' is merely a publicity stunt for his product. 

We will watch with interest to see how this RSA-2048 cracking story develops. It looks almost like a new LK-99 moment, but could have even greater impacts on our lives if the headline claims survive scrutiny.

Achieving higher-orbit quantum communications remains an objective for all institutional and private players with enough expertise and funding to consider it. And while quantum computing and the capability to communicate in unbreakable, unsnoopable channels is of interest to most entities, only China has manifested a low-orbit satellite — Micius — that enables two-way research and quantum information traffic between space and the surface. This was back in 2016 — the US doesn’t have a publicly-known, operational Quantum Key Distribution satellite system, and Europe’s is only expected to launch next year.

Not one to rest on its laurels, China is nonetheless aiming to take QKD (Quantum Key Distribution) communication to new heights, and is plotting out the ways to break its current, 310-mile (~500 km) geostationary orbit limit towards an impressive 6,200 mile (10,000 km) radius.

"Low-orbit quantum key satellite networking and medium- and high-orbit quantum science experiment platforms are the main development directions in the future," said Wang Jianyu, dean of the Hangzhou Advanced Research Institute of the Chinese Academy of Sciences (CAS). While timelines weren’t given for medium or high-orbit QKD, work is underway in understanding what problems need to be solved to get there.

Of course, satellites sitting at higher orbits could cover larger portions of the surface and additional ground stations, enabling a wider and more resilient quantum network coverage. But distance isn’t exactly helpful in increasing the survival of information-carrying qubits, and high-orbit satellites will require improved on-board micro-vibration suppression technology so spacecraft can send precise optical or laser signals. Fortunately, photons within the 1550nm band (used in our day-to-day fiber optics communications) can be leveraged for this, facilitating a number of implementation and adaptation steps.

Current satellite-based quantum communications leverages the entanglement susceptibility of photons — individual light particles that can be quantized — towards using them as information carriers. Much like the binary system of information, a single photon can be polarized in one way or another — in being able to discern more than one state, they can be encoded into information.

Due to this ability to encode useful information within photons, QKD leverages the property of entanglement to make it so that two separate photons become a qubit pair — a single system, where to describe one of them requires describing the other. Because they’re light-based, photonic qubits showcase a higher resilience to outside interference, placing them as the prime candidates towards ferrying sensitive information across long distances — and specifically between the Earth, its atmosphere, and space.

At this stage, the information (the entangled photon) reaching its destination or not becomes dependent on the absence of interference that could lead to a collapse of its entangled state. This collapse would also lead to the loss of all in-transit information.

What light-speed quantum key distribution and quantum-key-encrypted communications will lead to is to a future where certain communications streams will become unhackable but, up to a point, blockadeable (up to a point) by savvy-enough opponents. This has implications in the design of quantum communications systems for higher reliability and redundancy, as interrupted communications can have just as dire consequences as it being unencrypted.

Micius was recently used to successfully distribute quantum keys between the cities of Delingha and Nashan (756 miles apart) and, in 2018, between the Austrian city of Braz and the Chinese city of Xinglong — an intercontinental quantum key distribution separated by some 4,700 miles (7,600 kilometres). Meanwhile, Europe’s own QKD system as orchestrated by the European Space Agency (ESA) expects to see the first European QKD satellite — Eagle-1 — in space from 2024.

It’s clear that China is looking to capitalize on the years of experience it has low-orbit QKD system, and plans to increase its resiliency and redundancy. Considering the limited throughput of current QKD systems, however, it’ll likely be decades before these applications become pervasive — and even more before they’re used for communications in non-critical systems.

For the first time, a research team with the University of York has managed to send unhackable quantum information between Ireland and the UK. Leveraging ultra-low-loss fiber infrastructure capable of carrying "multiple terabits" of information, the researchers demonstrated how photonic qubits can already cover the 224 kilometers between the Irish Sea.

The feat - which included the collaboration of The Quantum Communications Hub and infrastructure provider euNetworks -  simultaneously set a new record for longest-distance subsea quantum communication. 

It's perhaps sometimes easy to get carried away with the details of new and more powerful Quantum Processing Units (QPUs), or clever new ways of using quantum engineering to use the subatomic world as our calculators. But perhaps the best measure of a technology lies in how it's actually applied, rather than idealized; and the reality is that quantum communication is already being tested over commercial-grade optical fiber infrastructure. And if there's something we know from our own PC world, it's that compatibility too can be key. 

Quantum communications takes advantage of the property of quantum entanglement - where two qubits become linked across distance, and where you can't describe one without also describing the other. The issue with entangled quantum states, however, is that they're fickle and prone to failure - their useful states can be collapsed through any outside interference, including any attempts at pulling data from them. This instability is why a qubit traversing a partly underwater, 224 kilometer distance within a high-tech fiber-optics cable is so impressive. Back in 2021, quantum communication had already been shown across 660 kilometers - but there were no high-pressure water bodies in the way.

The research serves as a reminder of how far along quantum communications already are towards commercialization. The cable bit should be one of the lesser problems: Rockabill, whose fiber optics elements are composed of Corning glass, was installed back in 2019. At the time, it was indeed among the latest and greatest available, but technology has since advanced. Considering how it took only eight months for Rockabill to be installed, however, it doesn't seem that quantum-compatible infrastructure will be the bottleneck - it's simply the case that we are already using it for other purposes. 

Rockabill being a fraction of the euNetworks' Super Highway web of fiber-optics connections means that infrastructure is already ahead of the quantum curve. It's more likely that any bottleneck will lie at the ends of the fibre optics, in the field of sensors, their sizes, reliability, ease of manufacturing, and ultimately, cost.

“Many large companies and organizations are interested in quantum communications to secure their data, but it has limitations, particularly the distance it can travel,” said research lead Professor Marco Lucamarini. “The longer the distance, the more likely it is that the photon – the particles of light that we use as carriers of quantum information – are lost, absorbed or scattered in the channel, which reduces the chances of the information reaching its target. This presents a problem when organizations need to send private digital information to other cities or other countries, where the additional challenge could also be an ocean between the communications’ start and end point.”

Chinese scientists across multiple national research institutions have published a paper on Nature that could spell a breakthrough in DNA-based computing. The researchers led by Lv Hui et al. published that they've designed a liquid, programmable-DNA-based computer that can process billions of different computing circuits - meaning that it can be used for general processing in a way similar to how CPUs are used today.

Due to the nature of its biological units, DNA computing offers the possibility of storing immense densities of data at up to 1 billion gigabytes per cubic millimeter - a boon for memory-heavy storage and processing tasks. With data densities like that, being able to manipulate DNA as an information processing system looks like something we'd want to pursue.

This is true especially when paired with the increased flexibility of a computing system that uses DNA strands as inputs and outputs. The computing we know is based on the ability to codify, store, and process information in a binary system: on or off, zero or one.

But DNA computing uses the four essential DNA molecules - adenine, thymine,  guanine, and cytosine (ATGC), to build a computing system to store and process more information per operation. While transistors limit you to map either a zero or a one to it being powered up, DNA computing can encode 00, 01, 10, and 11 as A, T, G, and C. This allows DNA computing to offer more possible, condensed combinations of information that the binary system (0 and 1) can't easily reach.

Suppose we know that the four essential DNA molecules (A, T, G, and C) only bind in a particular manner between each other. We can use this property as a computing mechanism for additions and subtractions and more complex and broad (general) operations.

But it's one thing to be able to use DNA strands as a computing mechanism; it's another to make it so that you can control which strands connect as you begin scaling the computer with additional strands. Additional copies of each (and their additional information content) could connect haphazardly.

To introduce actual control into the equation, the research paper describes a DNA origami - a DNA sequence designed in such a way that it can become a 2D or 3D shape. Interestingly, this ability of shapes to provide different information elements is a principle that topological quantum computing and MC Esher's work explore and is fundamental in superconductivity, quantum mechanics, and others.

When applied to DNA origami structures, topology makes it so that it's more difficult for compatible DNA strands to adhere to each other. It's not enough that ATGC molecules find TACG molecules anymore - now, they have to be folded to fit together first. They become pieces in a computational puzzle.

Using this principle, the researchers used a DNA computer made of 30 logic gates (around 500 DNA strands), which they could use to compute accurate square root calculations. And in every field of computing and information processing, accuracy is king. With this proven accuracy, the researchers used their small DNA computer to identify three genetic molecules related to kidney cancer. Within two hours, the computer could correctly tell which samples possessed the identified molecules and which did not (within a pool of 23 total samples, 18 of which were healthy and 5 of which presented lung cancer disease).

Further gains in accurate, specialized computing forms such as quantum computing, DNA computing, and others will help researchers and humanity understand our world. To that end, the probability that the future computing landscape will look like the one from today. In particular, it would seem that different computing architectures will have to interoperate. Perhaps DNA computing will be most important in providing extremely-high-density cold storage, and not as a general processing computer floating inside a test tube (which it does; and I don't know about you, but I wasn't expecting that). Who knows?

Science (like us) isn't always sure of where the best possible future is, and computing is no exception. Whether in classic semiconductor systems or in the forward-looking reality of quantum computing, there are sometimes multiple paths forward (and here's our primer on quantum computing if you want a refresher). Transmon superconducting qubits (such as the ones used by IBM, Google, and Alice&Bob) have gained traction as one of the most promising qubit types. But new MIT research could open up a door towards another type of superconducting qubits that are more stable and could offer more complex computation circuits: fluxonium qubits. 

Qubits are the quantum computing equivalent to transistors - get increasing numbers of them together, and you get increased computing performance (in theory). But while transistors are deterministic and can only represent a binary system (think of the result being either side of a coin, mapped to either 0 or 1), qubits are probabilistic and can represent the different positions of the coin while it's spinning in the air. This allows you to explore a bigger space of possible solutions than what can easily be represented through binary languages (which is why quantum computing can offer much faster processing of certain problems).

One current limitation to quantum computing is the accuracy of the computed results - if you're looking for, say, new healthcare drug designs, it'd be an understatement to say you need the results to be correct, replicable, and demonstrable. But qubits are sensitive and finicky to external stressors such as temperature, magnetism, vibrations, fundamental particle collisions, and other elements, which can introduce errors into the computation or collapse entangled states entirely. The reality of qubits being much more prone to external interference than transistors is one of the roadblocks on the road to quantum advantage; so a solution lies in being able to improve the accuracy of the computed results.

It's also not just a matter of applying error-correcting code to low-accuracy results and magically turning them into the correct results we want. IBM's recent breakthrough in this area (applying to transmon qubits) showed the effects of an error-correction code that predicted the environmental interference within a qubit system. Being able to predict interference means you can account for its effects within the skewed results and can compensate for them accordingly - arriving at the desired ground truth.

But in order for it to be possible to apply error-correction codes, the system has to already have passed a "fidelity threshold" - a minimum operating-level accuracy that enables those error-correcting codes to be just enough for us to be able to extract predictably useful, accurate results from our quantum computer.

Some qubit architectures - such as fluxonium qubits, the qubit architecture the research is based on - possess higher base stability against external interference. This enables them to stay coherent for longer periods of time - a measure of how long the qubit system can be effectively used between shut-downs and total information loss. Researchers are interested in fluxonium qubits because they've already unlocked coherence times of more than a millisecond - around ten times longer than can be achieved with transmon superconducting qubits. 

The novel qubit architecture enables operations to be performed between fluxonium qubits with important accuracy levels. Within it, the research team enabled fluxonium-based two-qubit gates to run at 99.9% accuracy and single-qubit gates to run at a record 99.99% accuracy. The architecture and design were published under the title "High-Fidelity, Frequency-Flexible Two-Qubit Fluxonium Gates with a Transmon Coupler" in PHYSICAL REVIEW X.

You could think about fluxonium qubits as being an alternative qubit architecture with its own strengths and weaknesses; not as an evolution of the quantum computing that has come before. Transmon qubits are made of a single Josephson junction shunted by a large capacitor, while fluxonium qubits are made of a small Josephson junction in series with an array of larger junctions or a high kinetic inductance material. It's partly for this that fluxonium qubits are harder to scale: they require more sophisticated coupling schemes between qubits, sometimes even using transmon qubits for this purpose. The fluxonium architecture design described in the paper does just that in what's called a Fluxonium-Transmon-Fluxonium (FTF) architecture.

Transmon qubits such as the ones used by IBM and Google are relatively easier to manipulate into bigger qubits arrays (IBM's Osprey is already at 433 qubits) and have faster operation times, performing fast and simple gate operations mediated by microwave pulses. Fluxonium qubits do offer the possibility of performing slower yet more accurate gate operations through shaped pulses than a transmon-only approach would enable.

There's no promise of an easy road to quantum advantage through any qubit architecture; that's the reason why so many companies are pursuing their differing approaches. In this scenario, it may be useful to think about this Noisy-Intermediate Scale Quantum (NISQ) era being the age where multiple quantum architectures flourish. From topological superconductors (as per Microsoft) through diamond vacancies, transmon superconduction (IBM, Google, others), ion traps, and a myriad of other approaches, this is the age where we will settle into certain patterns within quantum computing. All architectures may flourish, but it's perhaps most likely that only some will - which also justifies why states and corporations aren't pursuing a single qubit architecture as their main focus.

The numerous, apparently viable approaches to quantum computing we're witnessing put us right in the middle of the branching path before x86 gained dominance as the premier architecture for binary computing. It remains to be seen whether the quantum computing future will readily (and peacefully) agree on a particular technology, and how will a heterogeneous quantum future look like.

China's rate of quantum computing research and development is expected to slow down in the coming years, following the US' announcement of the finalized (and clarified) guardrails around its multi-billion-dollar CHIPS and Science Act. The fact that these new clarifications include provisions specifically aimed at quantum computing is a strong indication of just how concerningly close we are to achieving useful enough forms of it.

The guardrails around the CHIPS and Science Act, as issued by the Department of Commerce through NIST, seek to "prevent funding provided through the program from being used to directly or indirectly benefit foreign countries of concern." It's through this lens that the Department of Commerce has classified semiconductors as "critical to national security," subjecting them to increased scrutiny due to their obvious ability to increase a foreign country of concern's technological level.

And apparently, "semiconductors designed for quantum information systems" make the cut. Other clarifications include "Semiconductors designed for operation in cryogenic environments (at or below 77 Kelvin), which includes sensors for quantum computing and superconductor research. "Silicon photonic semiconductors," too, have quantum computing applications. "Semiconductors utilizing nanomaterials, including 1D and 2D carbon allotropes such as graphene and carbon nanotubes" also make the cut. 

From a technological perspective, the US covered a lot of ground with these guardrails. But increasingly, it seems that our world isn't exactly what it seems.

Around a year ago, we reported that the United States intended to extend the tapestry of trade restrictions and sanctions toward quantum computing. And now it's happened. But there are many difficulties with technologically restricting a "foreign country of concern" in a globalized world (logistics and international business relations is just one of them). 

One of the most fundamental of those difficulties is simply the scope of design and application of semiconductors: Tthey fit everywhere and can be made to aid in performing almost anything. Semiconductors themselves being "critical" would be an unenforceable policy. Hence a need to clarify what exactly these semiconductors "critical to national security" are.

The questions around sanctions, of course, almost always relate to how effective they are. Do these sanctions slow down the "opposition" more than they slow us down? In the case of quantum, the case isn't as clear-cut as one might expect. But then again, we've been seeing sanctions falling short of their intended, projected effects for a while now. There are always back-alleys and gray markets. And there's also the ability to simply cram and outinvent the US's restrictions, which China seems to be doing in some ways.

As quantum computing becomes more useful and more feasible, the world's attention has become more and more focused on this "mostly future" technology. And it's interesting to note how "fast" governments regulate emerging tools (blockchain, quantum computing, generative AI, among others). The US, for one, has shown particular public concern around quantum computing and its potential impact on national security. The National Institute of Standards and Technology itself has been coordinating the federal government's uptake of quantum-resistant encryption, with a few hiccups here and there, perhaps to be expected of something as complex as quantum cryptography.

In general, the US seems to be taking a proactive approach to dealing with whatever problems might scour the world when we reach a post-NISQ quantum era. One of its dark facets (among all the positive ones) is that most encryption will become useless. Most of the algorithms that currently protect all human communication will be breakable.

Quantum computing itself may provide the keys to generate the future's "unbreakable encryption." But the current world of geopolitics simply won't like unbreakable secrets -- perhaps not even in the backyard of a "foreign country of concern." That looks like a facet we should be wary of, at least.

Research from a team at the VTT Technical Research Centre of Finland may point the way toward more sustainable and performant quantum computers. Tthe research team designed a vacuum-tube-like device that allows for cooling to happen in a purely electronic way - a possible road toward slashing cooling costs for dilution-refrigerated quantum computers by a factor of ten. In their experiments, the researchers found their design allowed for temperatures to drop by as much as 40%. 

These quantum computers mostly leverage superconducting transmon qubits to perform useful computational work, and have been the qubits of choice for quantum-forward companies such as IBM, Google, Amazon, and others (but not all). But in order for these superconducting qubits to work, they have to be cooled close to the absolute-zero temperature of space (~ 1 Kelvin). The necessity of mixing different helium isotopes to achieve these ideal operating temperatures adds additional layers of complexity.

One of the fundamental limits to any high-level computation is cooling capacity - the ability to draw computationally generated heat away from operating circuits. This particular limit is seen everywhere these days - from the difficulty in cooling 16-core Zen 5 CPUs, to the humongous hunks of fans and metal keeping our GPUs pumping frames, heat is one of the toughest engineering problems in the computing worlds of today, and tomorrow.

But quantum computers are even more sensitive than traditional electronics - they're more prone to outside interference, and are more fickle as to what types of interference can collapse their useful, working qubit states. So new techniques that allow for simpler, more efficient cooling are much in need. While some advances in new cooling techniques have been achieved (such as Frore's AirJet technique), they all mostly work the same way: by channeling a heat-bearing medium (such as water or air) away from the heat source.

But the Finnish scientists at VTT are taking a wholly different road: They developed a thermionic device that sheds heat in the form of electrons (channeling electrons requires energy, which is why thermionic devices taking advantage of the Peltier effect usually introduce yet another energy-consumption step). But crucially, this device allows cooling to be taken almost to its extreme: the researchers expect to be able to cool electronics down to a range between 1.5 K and 0.1 K - more than sufficient to serve as a fundamental cooling mechanism for "absolute-zero" computing. And this technique should be much smaller, less expensive, and less prone to errors from both a logistical and operational standpoint compared to fluid-based cooling.

“Our technology could help the industry scale down overall quantum computer system size,” said Mika Prunnila at the VTT Technical Research Center of Finland, in Espoo.

However, one issue with thermionic coolers is that electron activity isn't the only source of fundamental heat. Other particles, semi-particles, and quasiparticles also interact with one another; and it isn't infrequent that the cooling achieved through electron shedding is lost as a result of other particles (in this case, phonons) "coming back", interacting (colliding) with particles within the previously-cooled material, and heating it up again, in a process known as "backscattering". Crucially, the researchers' thermionic device is able to both direct electrons and block the returning phonons from interacting with (and heating up) the previously cooled surface.

The researchers' thermionic device works by channeling heat through different mediums at their junction points (where the materials interface with one another). In this case, heat is drawn from the superconductor medium to the semiconductor one, pushing heat away from the most sensitive bits (the ones we want near absolute zero), toward the less sensitive ones. In this way, the cooling effect can be maximized.

It's still early days for the technology, but if quantum computers and classical computers are to keep developing at a useful pace, fundamental breakthroughs in heat management are required. Perhaps the Finnish researchers' thermionic device is the answer, or perhaps not. At the very least, it cuts through some prvious unknowns toward smaller, more capable cooling solutions.

With all the rage on superconductors zipping through the news (looking at you, LK-99), it's sometimes easy to let other stories fly under the radar. But science happens everywhere, all the time: now, a research team with the Massachusetts Institute of Technology (MIT) has developed a superconducting device that they say will bring improved energy and thermal efficiency to electronics. Their work was published in an online issue of Physical Review Letters.

Like LK-99 (which is still going through a messy replication and peer-review process), the MIT-designed diode (a kind of switching device) is still in its design infancy. Yet even so, Jagadeesh Moodera (lead author) et al. say this diode is already twice as efficient as previous diode architectures when it comes to carrying current (and preventing losses), with ample design room left to improve its characteristics.

It could even impact quantum computing. And in fact, this development came as a serendipitous discovery as the team looked into Majorana fermions, one of the building blocks of topological qubits, a yet-to-be-vindicated qubit design that's been pursued by none other than Microsoft. The team soon realized their Majorana-inspired work on superconducting diodes could be easily transferred into the realm of classical (i.e., non-quantum) circuits.

Diodes are a crucial part of any chip, and are an integral part of a circuit's design. While transistors are frequently used to amplify input signals from low resistance circuits to high resistance circuits within the chip, diodes can be used as either voltage stabilizers or as one-way valves (in that they only allow current to flow in one direction). It seems that either of those applications would benefit from this new superconducting design.

With chip design being forcefully constrained by the amount of heat generated by electrical losses (a bottleneck that's seen increasingly more complex transistor designs and new cooling technologies that deal with these issues in a limited manner), the benefits of lossless diodes in improving computing and thermal efficiency shouldn't be underestimated.

All the hallmarks of a superconductor were required to make the super-efficient diodes. The MIT research team showed that tiny differences between the edges of the diode devices could be optimized (by adding serrated edges, or applying other deformations). That's why the design is still open for optimization: the amount of possible design variations is enormous, and there's only so much time to find what the best asymmetrical configuration is. 

The design quirk shows that even microscopic differences in materials can result in disproportionate results. These diodes also have superconducting hallmarks such as the Meissner effect and the ability to lock into pre-existing magnetic fields (known as flux pinning).

Speaking with SciTechDaily, Philip Moll (Director of the Max Planck Institute for the Structure and Dynamics of Matter in Germany and not involved in the research) said that the MIT team's paper showcases how superconducting diodes are now an "entirely solved problem from an engineering perspective". He also added that the record efficiencies showcased by the design were hit "without even trying", with structures being "far from optimized yet". That sounds like perfectly smart (although still hard) science.

Crucially, the team says its superconducting diode is robust, and is able to operate over a wide temperature range while potentially opening the door to new technologies and designs. Adding relevancy to the discovery, the engineers say these diodes' design is simple and compatible enough that it's easily scalable - millions of them can be produced across a single silicon wafer.

So let's get them out here already?

Humanity may be in the throes of another breakthrough that's every bit as impactful as the invention of the transistor and the advent (and eventual vindication) of quantum computing. LK-99, as it's been named, is a new compound that researchers believe will enable the fabrication of room-temperature, ambient-pressure superconductors. Initially published by a Korean team last Friday, frantic work is underway throughout the research world to validate the paper's claims. For now, two separate sources have already provided preliminary confirmations that this might actually be the real thing — Chinese researchers have even posted video proof. Strap in, this is a maglev-powered, superconducting ride.

Superconductors, a wild category of compounds that can conduct electricity without any losses, have been a metaphorical goose chase for years now, with multiple research teams claiming (and then retracting) papers and announcements of its achievement. The reason is simple: Few things come close to the potential of an actual superconductor discovery in terms of what it can do for humanity's current and future technology. Imagine if your 16-core mainstream CPU (which likely requires a competent watercooling solution to avoid incinerating itself) operated without power losses — no current leakage, no electricity waste in the form of heat. Superconductors mean almost perfectly efficient computing.

Scale that to the world's supercomputers, and you begin to get an idea of the performance impact when trillions of transistors based on superconducting materials work in tandem across GPU and CPU tiles to accelerate things like Artificial Intelligence (AI) workloads. Or scale it in the realm of consumer electronics, quantum computing (where superconductors are important for Josephson junctions), and magnets in general (maglev trains, tokamak fusion reactors, Magnetic Resonance Imaging (MRI), electric motors and generators...)

If you can dream it and it features an electrical current or magnetism, it's likely a superconducting material would improve most aspects of it while leaving a surplus of previously-wasted energy within humanity's batteries. Environmental sustainability, then, is also a factor.

There might be more to LK-99 than skeptics expected, as two research teams claim to have informally confirmed certain aspects of the superconductivity claims — albeit in preliminary testing.  Researcher Sinéad Griffin from the U.S.'s Lawrence Berkeley National Lab pored over the original paper, taking advantage of the supercomputing capabilities within the Department of Energy to simulate the LK-99 material. This complex-yet-simple concoction results from combining the minerals lanarkite (Pb₂SO₅) and copper phosphide (Cu₃P), which are then baked within a 4-day, multi-step, small batch, solid-state synthesis process.

As a result of the simulations, the researcher published an analysis letter in pre-print form to Arxiv, where she confirmed that the resulting material should manifest the superconduction pathways for electrons to travel through unimpeded and without any resistance. Interestingly, she noticed that these superconducting pathways only form in very specific areas of the compound, namely the highest-energy areas of the resulting crystal lattice.

Because physics dictates that systems tend to remain stable at their lowest-possible energy states, this means that the amount of superconducting material produced with each "shake-and-bake" manufacturing attempt will result in relatively low quantities of the material. The hope, then, is that further refinements to the fabrication process will yield higher quantities of the material that can then be harvested and put toward building the superconductors themselves.

But in what's perhaps the most definite sign of a verification, Chinese researchers with the Huazhong University of Science and Technology have claimed to have successfully replicated the superconductor's manufacturing process, posting a video on Bilibili as proof.

The above video showcases the Meissner effect as being definite proof of the material's superconducting capabilities. The Meissner effect refers to the expulsion of a magnetic field due to the superconducting process. It is the reason why the video showcases levitating materials — they are interacting with LK-99's Meissner-induced magnetic field.

The entire story surrounding this discovery is a scientific rollercoaster ride, with rogue scientists, updated papers, plus cloudy definitions and process descriptions within the paper that make replication efforts more difficult, and even a Russian soil scientist (and anime catgirl) deconstructing the original Korean paper to unveil the trademark levitation of the Meissner effect over her own kitchen counter.

We've seen movies with much less complex plots than this already. It's eerily appropriate that such a monumental discovery would be rife with drama. And we're still waiting for a definite announcement that yes, humanity has finally produced room-temperature, ambient-pressure superconductors. After that, there are plenty more physics barriers to crash through, as always.

Edit 8/2/2023 1:40 pm ET: Embedded BiliBili video from the Chinese researchers.