Stochastic Simulated Quantum Annealing for Fast Solution of Combinatorial Optimization Problems

TL;DR

This paper presents stochastic simulated quantum annealing (SSQA), a classical computing method inspired by quantum annealing, which efficiently solves large-scale combinatorial optimization problems faster and at larger scales than traditional methods.

Contribution

The introduction of SSQA, combining stochastic computing and quantum Monte Carlo, enabling quantum-like annealing on classical hardware for large, fully connected optimization problems.

Findings

Achieves convergence speed an order of magnitude faster than conventional stochastic simulated annealing.

Handles 100 times larger problem sizes than quantum annealing.

Handles 25 times larger problem sizes than traditional simulated annealing.

Abstract

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