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Quantum Machine Learning

QuAS: Quantum Application Score for benchmarking the utility of quantum computers

arXiv
Authors: Koen J. Mesman, Ward van der Schoot, Matthias Möller, Niels M. P. Neumann

Year

2024

Paper ID

66852

Status

Preprint

Abstract Read

~2 min

Abstract Words

98

Citations

N/A

Abstract

Benchmarking quantum computers helps to quantify them and bringing the technology to the market. Various application-level metrics exist to benchmark a quantum device at an application level. This paper presents a revised holistic scoring method called the Quantum Application Score (QuAS) incorporating strong points of previous metrics, such as QPack and the Q-score. We discuss how to integrate both and thereby obtain an application-level metric that better quantifies the practical utility of quantum computers. We evaluate the new metric on different hardware platforms such as D-Wave and IBM as well as quantum simulators of Quantum Inspire and Rigetti.

Why This Paper Matters

  • This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
  • It adds a 2024 reference point for readers tracking recent quantum research.
  • Benchmarking quantum computers helps to quantify them and bringing the technology to the market.

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References & Citation Signals

[1] DOI https://doi.org/arXiv:2406.03905 [2] arXiv https://arxiv.org/abs/2406.03905 [3] Publisher https://arxiv.org/abs/2406.03905

Local Citation Graph (Related-Paper Links)

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External citation index: OpenAlex citation signal

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