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Quantum state discrimination using noisy quantum neural networks

Patterson, Andrew and Chen, Hongxiang and Wossnig, Leonard and Severini, Simone and Browne, Dan and Rungger, Ivan (2021) Quantum state discrimination using noisy quantum neural networks. Physical Review Research, 3 (1). Art. No. 013063. ISSN 2643-1564. https://resolver.caltech.edu/CaltechAUTHORS:20210121-101410995

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Abstract

Near-term quantum computers are noisy, and therefore must run algorithms with a low circuit depth and qubit count. Here we investigate how noise affects a quantum neural network (QNN) for state discrimination, which is applicable on near-term quantum devices as it fulfils the above criteria. We find that for the required gradient calculation on a noisy device a quantum circuit with a large number of parameters is disadvantageous. By introducing a smaller circuit ansatz we overcome this limitation, and find that the QNN performs well at noise levels of current quantum hardware. We present a model showing that the main effect of the noise is to increase the overlap between the states as circuit gates are applied, hence making discrimination more difficult. Our findings demonstrate that noisy quantum computers can be used for state discrimination and other applications, such as classifiers of the output of quantum generative adversarial networks.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1103/physrevresearch.3.013063DOIArticle
https://arxiv.org/abs/1911.00352arXivDiscussion Paper
ORCID:
AuthorORCID
Patterson, Andrew0000-0002-9833-3174
Chen, Hongxiang0000-0001-6792-7394
Wossnig, Leonard0000-0002-0861-9540
Severini, Simone0000-0001-7305-6759
Browne, Dan0000-0003-3001-158X
Rungger, Ivan0000-0002-9222-9317
Additional Information:© 2021 The Author(s). Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI. (Received 16 June 2020; accepted 22 December 2020; published 20 January 2021) A.P. is supported by the InQUBATE Training and Skills Hub [Engineering and Physical Sciences Research Council (EPSRC) Grant No. EP/P510270/1]. I.R. acknowledges financial support from the United Kingdom Department of Business, Energy, and Industrial Strategy. H.C. acknowledges support through a Teaching Fellowship from University College London (UCL). L.W. acknowledges support through the Google PhD Fellowship in Quantum Computing. D.B. acknowledges support from the EPSRC Prosperity Partnership in Quantum Software for Modelling and Simulation (Grant No. EP/S005021/1). A.P. acknowledges the use of the UCL Myriad High Throughput Computing Facility (Myriad@UCL), and associated support services, in the completion of this work.
Group:AWS Center for Quantum Computing
Funders:
Funding AgencyGrant Number
Engineering and Physical Sciences Research Council (EPSRC)EP/P510270/1
Department of Business, Energy, and Industrial Strategy (United Kingdom)UNSPECIFIED
University College LondonUNSPECIFIED
GoogleUNSPECIFIED
Engineering and Physical Sciences Research Council (EPSRC)EP/S005021/1
Issue or Number:1
Record Number:CaltechAUTHORS:20210121-101410995
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20210121-101410995
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:107629
Collection:CaltechAUTHORS
Deposited By: George Porter
Deposited On:21 Jan 2021 18:58
Last Modified:21 Jan 2021 18:58

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