Benchmark of quantum-inspired heuristic solvers for quadratic unconstrained binary optimization

TL;DR

This paper benchmarks four quantum-inspired heuristic solvers for quadratic unconstrained binary optimization, revealing their relative strengths across different problem types to guide future applications and improvements.

Contribution

It provides a comparative analysis of four state-of-the-art solvers on diverse problem instances, highlighting their performance differences.

Findings

HSS performed best on MQLib instances

DA outperformed others on NAE 3-SAT problems

SBM was most effective on the SK model

Abstract

Recently, inspired by quantum annealing, many solvers specialized for unconstrained binary quadratic programming problems have been developed. For further improvement and application of these solvers, it is important to clarify the differences in their performance for various types of problems. In this study, the performance of four quadratic unconstrained binary optimization problem solvers, namely D-Wave Hybrid Solver Service (HSS), Toshiba Simulated Bifurcation Machine (SBM), Fujitsu DigitalAnnealer (DA), and simulated annealing on a personal computer, was benchmarked. The problems used for benchmarking were instances of real problems in MQLib, instances of the SAT-UNSAT phase transition point of random not-all-equal 3-SAT(NAE 3-SAT), and the Ising spin glass Sherrington-Kirkpatrick (SK) model. Concerning MQLib instances, the HSS performance ranked first; for NAE 3-SAT, DA performance ranked first; and regarding the SK model, SBM performance ranked first. These results may help understand the strengths and weaknesses of these solvers.