Generative model benchmarks for superconducting qubits

Kathleen E. Hamilton, Eugene F. Dumitrescu, and Raphael C. Pooser
Phys. Rev. A 99, 062323 – Published 18 June 2019

Abstract

In this work we demonstrate experimentally how generative model training can be used as a benchmark for small (fewer than five qubits) quantum devices. Performance is quantified using three data analytic metrics: the Kullback-Leibler divergence and two adaptations of the F1 score. Using the 2×2 bars and stripes data set, we train several different circuit constructions for generative modeling with superconducting qubits. By taking hardware connectivity constraints into consideration, we show that sparsely connected shallow circuits outperform denser counterparts on noisy hardware.

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  • Received 5 December 2018
  • Revised 4 May 2019
  • Corrected 7 October 2020

DOI:https://doi.org/10.1103/PhysRevA.99.062323

©2019 American Physical Society

Physics Subject Headings (PhySH)

Quantum Information, Science & Technology

Corrections

7 October 2020

Correction: The previously published Figure 1 contained an error and has been replaced.

Authors & Affiliations

Kathleen E. Hamilton, Eugene F. Dumitrescu, and Raphael C. Pooser

  • Computer Science and Engineering Division, Oak Ridge National Laboratory, One Bethel Valley Road, Oak Ridge, Tennessee 37831, USA

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Issue

Vol. 99, Iss. 6 — June 2019

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