Simulated Bifurcation

The Simulated Bifurcation (SB) algorithm is a fast and highly parallelizable state-of-the-art algorithm for combinatorial optimization inspired by quantum physics and spins dynamics. It relies on Hamiltonian quantum mechanics to find local minima of Ising problems. The last accuracy tests showed a median optimality gap of less than 1% on high-dimensional instances.

This open-source package utilizes PyTorch to leverage GPU computations, harnessing the high potential for parallelization offered by the SB algorithm.

It also provides an API to define Ising models or other NP-hard and NP-complete problems (QUBO, Karp problems, …) that can be solved using the SB algorithm.