Digital Annealing Engine for High-speed Solving of Constrained Binary Quadratic Problems on Multiple GPUs.

This paper presents a digital annealing engine capable of high-speed solving the Binary Quadratic Programming Problem (BQP) with equality and inequality constraints. While many Quadratic Unconstrained Binary Optimization (QUBO) solvers have been developed for combinatorial optimization problems, they are specialized for “unconstrained” BQP and struggle to solve “constrained” BQP at high speed. To address this issue, we developed a digital annealing engine on GPUs that leverages constraints to narrow the search space and directly evaluates inequality violations during Markov Chain Monte Carlo based searches. We evaluated the performance of the Digital Annealer (DA) system with the developed engine on benchmark problems with one-hot (a type of equality constraint) and inequality constraints. The results showed that the developed DA system achieved superior solving performance compared to the previous generation of DA. It also showed comparable performance to state-of-the-art dedicated solvers. Our results suggest that this digital annealing engine is a promising solution for high-speed solving constrained BQP.