Simulated Bifurcation Machine (SBM) comes with a set of solvers which enables users to quickly obtain good approximate solutions for large combinatorial optimization problems expressed as ISING, MAXCUT and MAXSAT problems.

Diverse technological challenges facing society today - logistics optimization, financial portfolio optimization, and molecular modeling in drug discovery, to name a few - typically involve solving "combinatorial optimization problems," which entails finding the best solution out of an astronomical number of combinatorial patterns. As this requires a huge amount of calculation, previous technologies have been inadequate in increasing the size of the problems that can be tackled and in reducing the amount of time required to find a solution.

By successfully implementing a novel optimization algorithm proposed by Toshiba (Goto et al. (2019)), SBM has made it possible to obtain good approximate solutions to these complex, large-scale problems quickly, without requiring dedicated hardware.