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

Emerging Technologies 2022-04-20 v3 Quantum Physics

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.

Keywords

Cite

@article{arxiv.2103.17193,
  title  = {QPack: Quantum Approximate Optimization Algorithms as universal benchmark for quantum computers},
  author = {Koen Mesman and Zaid Al-Ars and Matthias Möller},
  journal= {arXiv preprint arXiv:2103.17193},
  year   = {2022}
}

Comments

24 pages, 11 figures