QPack: Quantum Approximate Optimization Algorithms as universal benchmark for quantum computers

TL;DR

QPack is a comprehensive, hardware-agnostic benchmark for NISQ quantum computers based on QAOA, evaluating problem size, runtime, and accuracy across multiple applications to guide hardware design.

Contribution

Introduces QPack, a novel universal benchmark for quantum hardware that assesses multiple performance aspects using diverse QAOA-based applications.

Findings

QPack effectively measures maximum problem size, runtime, and accuracy.

Demonstrated on IBM quantum machines and simulators.

Provides practical metrics for hardware evaluation.

Abstract

In this paper, we present QPack, a universal benchmark for Noisy Intermediate-Scale Quantum (NISQ) computers based on Quantum Approximate Optimization Algorithms (QAOA). Unlike other evaluation metrics in the field, this benchmark evaluates not only one, but multiple important aspects of quantum computing hardware: the maximum problem size a quantum computer can solve, the required runtime, as well as the achieved accuracy. The applications MaxCut, dominating set and traveling salesman are included to provide variation in resource requirements. This will allow for a diverse benchmark that promotes optimal design considerations, avoiding hardware implementations for specific applications. We also discuss the design aspects that are taken in consideration for the QPack benchmark, with critical quantum benchmark requirements in mind. An implementation is presented, providing practical metrics. QPack is presented as a hardware agnostic benchmark by making use of the XACC library. We demonstrate the application of the benchmark on various IBM machines, as well as a range of simulators.