Toshiba announced that a quantum computing-inspired algorithm could outperform other similar (combinatorial optimisation) algorithms running on supercomputers, but while using the resources of a single desktop computer.

Its creator Hayato Goto, a senior research scientist at Toshiba, called it the “Simulated Bifurcation Algorithm” after noticing how the qualities of certain complex systems can suddenly change after new inputs are added, creating a phenomenon called bifurcation. A combinatorial optimisation algorithm attempts to extract an approximate (good) solution out of a high number of possible combinations.

After he first came up with the idea in 2015, it took Goto another two years to implement the algorithm in a way that it could efficiently sift through a huge number of possibilities, much like a quantum computer can. The difference being that it only required a desktop machine with off the shelf components to run it.

Goto also partnered with Kosuke Tatsumura, another Toshiba senior research scientist whose semiconductor expertise allowed the team to make the algorithm highly scalable. It can now work not just on a single computer, but also on clusters of server CPUs and FPGAs.

Using a cluster of FPGAs, Toshiba's algorithm proved to be ten times faster than a laser-based quantum computer, too, which is currently the fastest at solving a particular set of problems.

Quantum computers are meant to solve many of the same problems that this algorithm addresses, but so far, they haven’t become powerful enough to deal with large numbers of possibilities. In the meantime, scientists like Hoto continue to find new classical algorithms, some even inspired by quantum computers that can solve certain problems much faster than it was previously possible or with much lower resources.

Until quantum computers become more practical, Toshiba intends to sell access to its Simulated Bifurcation Algorithm to financial and stock trading companies, social networks, manufacturing companies, and any other company that needs to solve combinatorial optimization problems much more efficiently (and therefore more cheaply).