Approximately 100 times faster, will accelerate solutions for drug discovery, finance, and other complex problems
Toshiba Corporation has developed a breakthrough algorithm that dramatically boosts the performance of the Simulated Bifurcation Machine (SBM), its proprietary quantum‑inspired combinatorial optimization computer. The new algorithm significantly improves the probability of obtaining an optimal solution or a known best solution within a limited number of trials—referred to as the success probability, a key benchmark for evaluating combinatorial optimization technologies.
The SBM is designed to solve large‑scale combinatorial optimization problems in a wide range of fields, including new drug discovery, delivery route optimization, and investment portfolio design. While previous algorithms could find optimal or known best solutions with a sufficiently large number of trials, large‑scale problems often trapped the search process in local optima, significantly lowering success probability under practical constraints that limit the number of trials.
Toshiba has overcome this challenge by developing a third‑generation simulated bifurcation (SB) algorithm. This ground-breaking advance builds on the original SB algorithm, announced in April 2019*1, and the second‑generation SB algorithm, released in February 2021*2, which delivered major boosts to computational speed and accuracy.
The new algorithm expands the bifurcation parameter that triggers the bifurcation phenomena*3—a defining feature of the SB algorithm—from a single global parameter to individual parameters assigned to each position variable*4. These bifurcation parameters are independently controlled according to the values of the corresponding position variables, enabling a more adaptive and effective solution search.
With the introduction of this advanced control mechanism, the algorithm exhibits either regular or chaotic behavior*5, depending on conditions. Crucially, Toshiba discovered that by effectively harnessing chaos at the edge of chaos—the boundary between regular dynamics and chaotic motion—the algorithm can escape local optima far more efficiently. As a result, the success probability of reaching the global optimum increases dramatically, approaching 100%.
The SBM based on the new algorithm is therefore much faster. It delivers a time to solution (TTS) required to obtain an optimal or known best solution that is approximately 100 times faster than the SBM based on the second‑generation algorithm. These advances are expected to accelerate the practical applications of combinatorial optimization across a broad range of challenges.
The research results were published in the April 6, 2026 issue of Physical Review Applied, a peer‑reviewed journal of the American Physical Society
