Generative model benchmarks for superconducting qubits

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

doi:10.1103/PhysRevA.99.062323

Title: Generative model benchmarks for superconducting qubits

Journal Article · 18 June 2019 · Physical Review A

DOI: https://doi.org/10.1103/PhysRevA.99.062323 · OSTI ID:1531259

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Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

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.

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Research Organization:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR) (SC-21)

Grant/Contract Number:: AC05-00OR22725

OSTI ID:: 1531259

Journal Information:: Physical Review A, Journal Name: Physical Review A Journal Issue: 6 Vol. 99; ISSN PLRAAN; ISSN 2469-9926

Publisher:: American Physical Society (APS)Copyright Statement

Country of Publication:: United States

Language:: English

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Cited By (9)

A generative modeling approach for benchmarking and training shallow quantum circuits Benedetti, Marcello; Garcia-Pintos, Delfina; Perdomo, Oscar npj Quantum Information, Vol. 5, Issue 1 https://doi.org/10.1038/s41534-019-0157-8	journal	May 2019
Quantum chemistry as a benchmark for near-term quantum computers McCaskey, Alexander J.; Parks, Zachary P.; Jakowski, Jacek npj Quantum Information, Vol. 5, Issue 1 https://doi.org/10.1038/s41534-019-0209-0	journal	November 2019
Parameterized quantum circuits as machine learning models Benedetti, Marcello; Lloyd, Erika; Sack, Stefan Quantum Science and Technology, Vol. 4, Issue 4 https://doi.org/10.1088/2058-9565/ab4eb5	journal	October 2019
XACC: a system-level software infrastructure for heterogeneous quantum–classical computing McCaskey, Alexander J.; Lyakh, Dmitry I.; Dumitrescu, Eugene F. Quantum Science and Technology, Vol. 5, Issue 2 https://doi.org/10.1088/2058-9565/ab6bf6	journal	February 2020
Training of quantum circuits on a hybrid quantum computer Zhu, D.; Linke, N. M.; Benedetti, M. Science Advances, Vol. 5, Issue 10 https://doi.org/10.1126/sciadv.aaw9918	journal	October 2019
A generative modeling approach for benchmarking and training shallow quantum circuits Benedetti, Marcello; Garcia-Pintos, Delfina; Perdomo, Oscar arXiv https://doi.org/10.48550/arxiv.1801.07686	text	January 2018
Training of Quantum Circuits on a Hybrid Quantum Computer Zhu, D.; Linke, N. M.; Benedetti, M. arXiv https://doi.org/10.48550/arxiv.1812.08862	text	January 2018
Quantum Chemistry as a Benchmark for Near-Term Quantum Computers McCaskey, Alexander J.; Parks, Zachary P.; Jakowski, Jacek arXiv https://doi.org/10.48550/arxiv.1905.01534	preprint	January 2019
Parameterized quantum circuits as machine learning models Benedetti, Marcello; Lloyd, Erika; Sack, Stefan arXiv https://doi.org/10.48550/arxiv.1906.07682	text	January 2019

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