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Title: Generative model benchmarks for superconducting qubits

Abstract

In this paper 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 F 1 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.

Authors:
ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [1]
  1. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR) (SC-21)
OSTI Identifier:
1531259
Grant/Contract Number:  
AC05-00OR22725
Resource Type:
Accepted Manuscript
Journal Name:
Physical Review A
Additional Journal Information:
Journal Volume: 99; Journal Issue: 6; Journal ID: ISSN 2469-9926
Publisher:
American Physical Society (APS)
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING; 71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS

Citation Formats

Hamilton, Kathleen E., Dumitrescu, Eugene F., and Pooser, Raphael C. Generative model benchmarks for superconducting qubits. United States: N. p., 2019. Web. doi:10.1103/PhysRevA.99.062323.
Hamilton, Kathleen E., Dumitrescu, Eugene F., & Pooser, Raphael C. Generative model benchmarks for superconducting qubits. United States. doi:10.1103/PhysRevA.99.062323.
Hamilton, Kathleen E., Dumitrescu, Eugene F., and Pooser, Raphael C. Tue . "Generative model benchmarks for superconducting qubits". United States. doi:10.1103/PhysRevA.99.062323.
@article{osti_1531259,
title = {Generative model benchmarks for superconducting qubits},
author = {Hamilton, Kathleen E. and Dumitrescu, Eugene F. and Pooser, Raphael C.},
abstractNote = {In this paper 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.},
doi = {10.1103/PhysRevA.99.062323},
journal = {Physical Review A},
number = 6,
volume = 99,
place = {United States},
year = {2019},
month = {6}
}

Journal Article:
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This content will become publicly available on June 18, 2020
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