Towards Robust Benchmarking of Quantum Optimization Algorithms

TL;DR

This paper proposes universal guidelines for fair benchmarking of quantum optimization algorithms, addressing challenges in optimization, data selection, and hyperparameter tuning to ensure equitable comparisons with classical methods.

Contribution

It introduces comprehensive, application-specific benchmarking guidelines and tests them across multiple scenarios involving quantum and classical algorithms.

Findings

Guidelines improve fairness in quantum-classical benchmarking

Benchmarking with Max-Cut and TSP demonstrates practical applicability

Hyperparameter training eliminates bias towards specific methods

Abstract

Benchmarking the performance of quantum optimization algorithms is crucial for identifying utility for industry-relevant use cases. Benchmarking processes vary between optimization applications and depend on user-specified goals. The heuristic nature of quantum algorithms poses challenges, especially when comparing to classical counterparts. A key problem in existing benchmarking frameworks is the lack of equal effort in optimizing for the best quantum and, respectively, classical approaches. This paper presents a comprehensive set of guidelines comprising universal steps towards fair benchmarks. We discuss (1) application-specific algorithm choice, ensuring every solver is provided with the most fitting mathematical formulation of a problem; (2) the selection of benchmark data, including hard instances and real-world samples; (3) the choice of a suitable holistic figure of merit, like time-to-solution or solution quality within time constraints; and (4) equitable hyperparameter training to eliminate bias towards a particular method. The proposed guidelines are tested across three benchmarking scenarios, utilizing the Max-Cut (MC) and Travelling Salesperson Problem (TSP). The benchmarks employ classical mathematical algorithms, such as Branch-and-Cut (BNC) solvers, classical heuristics, Quantum Annealing (QA), and the Quantum Approximate Optimization Algorithm (QAOA).