Quantum computing company Quantum Computing Inc. (QCI) has staked a claim to fame by solving a 3,854-variable optimization issue for BMW. The company made use of its new hardware-based quantum computing solution, Entropy Quantum Computing (EQC), to solve for the ideal placement of vehicle sensors in BMW's Vehicle Sensor Placement Challenge (VSPC) 2022. Its new quantum system delivered performance that was 70 times higher that of its 2021 entry, which leveraged the company's hybrid quantum implementation derived from quantum computing player D-Wave.
“We are very proud to have achieved what we believe to be an important landmark result in the evolution of quantum,” said Bob Liscouski, CEO of QCI in a press release. “We believe that this proves that innovative quantum computing technologies can solve real business problems today. What’s even more significant is the complexity of the problem solved. This wasn’t just a rudimentary problem to show that quantum solutions will be feasible someday; this was a very real and significant problem whose solution can potentially contribute to accelerating the realization of the autonomous vehicle industry.”
This marks a usage of quantum computing to solve actionable, real-world problems that would take classical computers exponentially longer times to solve. QCI says this proves the advantages of its approach to quantum computing compared to other quantum systems available today. Alternatives such as IBM's 127-qubit Eagle Quantum Processing Unit (QPU) and Quantum Brilliance's diamond-based QPUs (already deployed in datacenter environments) are all classed as Noisy Intermediate Scale Quantum (NISQ) systems. QCI says its demonstration is proof of it achieving quantum advantage (the moment where quantum computers solve problems that would be impossible for classical systems).
Placing sensors in vehicles - and especially autonomous vehicles - is an incredible challenge. A multitude of variables have to be taken into account - variables such as chassis design (which has implications on vehicle security), absence of obstruction (different placements offer different fields of view or allow for lower error possibility), wind resistance and weight balancing, just to name a few.
It's a problem that requires numerous trial-and-error processes that may not render the optimal solution, and which have to be redone for every new vehicle and every new sensor advancement. This is part of the reason why vehicle design has remained relatively lifeless for years now - deviation from already-known solutions adds cost, which then cuts into profits.
Due to the number of variables and constraints (QCI quotes 3,854 variables and 500 constraints imposed on the solution), computing all of the possible positions for sensor placement on a classical system hits walls on cost — compute time is an expensive pursuit, as F1 teams will tell you.
Even before money is counted, the very real cost of compute time within classical systems has rendered many, many problems unsolvable (such as logistics management, step sequencing and prioritization).
Those are problems that quantum computing, with its probabilistic approach to computing, can solve in a fraction of the time. So much so that QCI solved BMW's optimization problem in under six minutes, delivering the best-possible solution to the placement problem at hand. In so doing, it delivered a solution consisting of 15 sensors, which yielded a 96% vehicle coverage by leveraging QCI’s quantum hardware and software system.
In responding to the VSPC, QCI leveraged a new hardware form of quantum computing. Entropy Quantum Computing, as it's called, does away with the requirements for a close-to-perfect environment on which the qubits operate, lowering design, installation and operating costs substantially. Entropy refers to the natural evolution of any system, which tends to occur towards chaos (or in this case, disorder).
When you can get away with a noisier environment (in which temperatures, electromagnetic radiation and other variables are more forgiving of the quantum system's coherence), deployment of quantum computers becomes much more feasible.
Coherence is a fundamental requirement of quantum computers, as changes in its environment can cause it to inadvertently change states - introducing costly and sometimes computing-killing errors into the calculations.
QCI's Entropy Quantum Computing approach works by taking into account the environment itself into the calculation results. Time and cost are saved by not having to control for all variables outside the Quantum Processing Unit itself - instead, the system adapts to the changing environment, analyzing its feedback and what it means for the qubits' quantum states.
Simplifying things immensely, think about how modern processors dynamically change voltages and frequency according to the workload, while taking into account variables such power consumption and operating temperature.
The commercial and general feasibility of QCI's quantum computing solution remains to be seen; it's interesting to note that companies with more resources and history than QCI have opted for other approaches to quantum computing. Others, like Microsoft, are still chasing their own specific qubits. Each and every one of them extoll the merits of their chosen approach.
It's not so much a race (although there is a race towards funding and market share) as it is about exploring different venues for quantum computing. It perhaps speaks to the complexity of it that there are so many possible approaches towards harnessing what is likely to become the next big frontier for computing sciences.