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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 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.

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)
OSTI Identifier:
1531259
Alternate Identifier(s):
OSTI ID: 1546308
Grant/Contract Number:  
AC05-00OR22725; ERKJ332
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. https://www.osti.gov/servlets/purl/1531259.
@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}
}

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Cited by: 5 works
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Works referenced in this record:

Quantum machine learning
journal, September 2017

  • Biamonte, Jacob; Wittek, Peter; Pancotti, Nicola
  • Nature, Vol. 549, Issue 7671
  • DOI: 10.1038/nature23474

Opportunities and challenges for quantum-assisted machine learning in near-term quantum computers
journal, June 2018

  • Perdomo-Ortiz, Alejandro; Benedetti, Marcello; Realpe-Gómez, John
  • Quantum Science and Technology, Vol. 3, Issue 3
  • DOI: 10.1088/2058-9565/aab859

Differentiable learning of quantum circuit Born machines
journal, December 2018


A generative modeling approach for benchmarking and training shallow quantum circuits
journal, May 2019

  • Benedetti, Marcello; Garcia-Pintos, Delfina; Perdomo, Oscar
  • npj Quantum Information, Vol. 5, Issue 1
  • DOI: 10.1038/s41534-019-0157-8

Quantum Generative Adversarial Learning
journal, July 2018


Quantum generative adversarial networks
journal, July 2018


Adversarial quantum circuit learning for pure state approximation
journal, April 2019

  • Benedetti, Marcello; Grant, Edward; Wossnig, Leonard
  • New Journal of Physics, Vol. 21, Issue 4
  • DOI: 10.1088/1367-2630/ab14b5

Quantum generative adversarial learning in a superconducting quantum circuit
journal, January 2019


Learning and inference on generative adversarial quantum circuits
journal, May 2019


Information Perspective to Probabilistic Modeling: Boltzmann Machines versus Born Machines
journal, August 2018

  • Cheng, Song; Chen, Jing; Wang, Lei
  • Entropy, Vol. 20, Issue 8
  • DOI: 10.3390/e20080583

Hardware-efficient variational quantum eigensolver for small molecules and quantum magnets
journal, September 2017

  • Kandala, Abhinav; Mezzacapo, Antonio; Temme, Kristan
  • Nature, Vol. 549, Issue 7671
  • DOI: 10.1038/nature23879

Benchmarking gate-based quantum computers
journal, November 2017

  • Michielsen, Kristel; Nocon, Madita; Willsch, Dennis
  • Computer Physics Communications, Vol. 220
  • DOI: 10.1016/j.cpc.2017.06.011

Error mitigation extends the computational reach of a noisy quantum processor
journal, March 2019


Benchmarking gate-based quantum computers
text, January 2017


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    A generative modeling approach for benchmarking and training shallow quantum circuits
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    Quantum chemistry as a benchmark for near-term quantum computers
    journal, November 2019

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    • DOI: 10.1038/s41534-019-0209-0

    Parameterized quantum circuits as machine learning models
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    • DOI: 10.1088/2058-9565/ab4eb5

    XACC: a system-level software infrastructure for heterogeneous quantum–classical computing
    journal, February 2020

    • McCaskey, Alexander J.; Lyakh, Dmitry I.; Dumitrescu, Eugene F.
    • Quantum Science and Technology, Vol. 5, Issue 2
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    Training of quantum circuits on a hybrid quantum computer
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