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In this paper, we introduce stochastic simulated quantum annealing (SSQA) forlarge-scale combinatorial optimization problems. SSQA is designed based onstochastic computing and quantum Monte Carlo, which can simulate quantumannealing (QA) by using multiple replicas of spins (probabilistic bits) inclassical computing. The use of stochastic computing leads to an efficientparallel spin-state update algorithm, enabling quick search for a solutionaround the global minimum energy. Therefore, SSQA realizes quantum-likeannealing for large-scale problems and can handle fully connected models incombinatorial optimization, unlike QA. The proposed method is evaluated inMATLAB on graph isomorphism problems, which are typical combinatorialoptimization problems. The proposed method achieves a convergence speed anorder of magnitude faster than a conventional stochastic simulaated annealingmethod. Additionally, it can handle a 100-times larger problem size compared toQA and a 25-times larger problem size compared to a traditional SA method,respectively, for similar convergence probabilities.

Stochastic Simulated Quantum Annealing for Fast Solution of Combinatorial Optimization Problems