Multi-Objective Optimization by Quantum-Annealing-Inspired Algorithms

TL;DR

This paper demonstrates that GPU-based quantum-annealing-inspired algorithms outperform both quantum processors and classical solvers in multi-objective optimization tasks, offering faster sampling and better end-to-end runtime.

Contribution

It introduces GPU-based quantum-annealing-inspired algorithms as competitive classical alternatives to quantum processors for multi-objective optimization.

Findings

QAIAs sample solutions two orders of magnitude faster than quantum processors.

QAIAs outperform classical solvers in end-to-end runtime for MO-MaxCut.

QAIAs serve as strong classical contenders by mimicking quantum sampling.

Abstract

Combinatorial optimization is widely regarded as a primary application for near-term quantum processors, although a definitive demonstration of the practical quantum advantage remains elusive. Recent studies have reported that both gate-based quantum circuits and quantum annealers can outperform state-of-the-art classical heuristics on multi-objective optimization (MO-MaxCut) problems. However, these studies did not fully account for the substantial pre- and post-processing overheads intrinsic to quantum solvers, leading to incomplete comparisons between quantum and classical approaches. In this work, we re-examine the same benchmark suite using GPU-based quantum-annealing-inspired algorithms (QAIAs), which, analogously to quantum processors, generate probabilistic samples and thus serve as formidable classical contenders. Our results show that QAIAs can sample candidate solutions approximately two orders of magnitude faster than previously studied quantum processors. In terms of end-to-end runtime, QAIAs also surpass industry-leading classical solvers, thereby establishing themselves as the superior performers among the quantum and classical solvers evaluated thus far for the MO-MaxCut instances.