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Quantum Machine Learning

Evaluating Three Levels of Quantum Metrics on Quantum-Inspire Hardware

arXiv
Authors: Ward van der Schoot, Robert Wezeman, Pieter Thijs Eendebak, Niels M. P. Neumann, Frank Phillipson

Year

2023

Paper ID

54232

Status

Preprint

Abstract Read

~2 min

Abstract Words

158

Citations

N/A

Abstract

With the rise of quantum computing, many quantum devices have been developed and many more devices are being developed as we speak. This begs the question of which device excels at which tasks and how to compare these different quantum devices with one another. The answer is given by quantum metrics, of which many exist today already. Different metrics focus on different aspects of (quantum) devices and choosing the right metric to benchmark one device against another is a difficult choice. In this paper we aim to give an overview of this zoo of metrics by grouping established metrics in three levels: component level, system level and application level. With this characterisation we also mention what the merits and uses are for each of the different levels. In addition, we evaluate these metrics on the Starmon-5 device of Quantum-Inspire through the cloud access, giving the most complete benchmark of a quantum device from an user experience to date.

Why This Paper Matters

  • This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
  • It adds a 2023 reference point for readers tracking recent quantum research.
  • With the rise of quantum computing, many quantum devices have been developed and many more devices are being developed as we speak.

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