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Entangling Quantum Generative Adversarial Networks

Niu, Murphy Yuezhen and Zlokapa, Alexander and Broughton, Michael and Boixo, Sergio and Mohseni, Masoud and Smelyanskyi, Vadim and Neven, Hartmut (2022) Entangling Quantum Generative Adversarial Networks. Physical Review Letters, 128 (22). Art. No. 220505. ISSN 0031-9007. doi:10.1103/physrevlett.128.220505. https://resolver.caltech.edu/CaltechAUTHORS:20220606-736195000

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Abstract

Generative adversarial networks (GANs) are one of the most widely adopted machine learning methods for data generation. In this work, we propose a new type of architecture for quantum generative adversarial networks (an entangling quantum GAN, EQ-GAN) that overcomes limitations of previously proposed quantum GANs. Leveraging the entangling power of quantum circuits, the EQ-GAN converges to the Nash equilibrium by performing entangling operations between both the generator output and true quantum data. In the first multiqubit experimental demonstration of a fully quantum GAN with a provably optimal Nash equilibrium, we use the EQ-GAN on a Google Sycamore superconducting quantum processor to mitigate uncharacterized errors, and we numerically confirm successful error mitigation with simulations up to 18 qubits. Finally, we present an application of the EQ-GAN to prepare an approximate quantum random access memory and for the training of quantum neural networks via variational datasets.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1103/PhysRevLett.128.220505DOIArticle
https://arxiv.org/abs/2105.00080arXivDiscussion Paper
https://journals.aps.org/prl/supplemental/10.1103/PhysRevLett.128.220505/sm.pdfPublisherSupporting Information
ORCID:
AuthorORCID
Zlokapa, Alexander0000-0002-4153-8646
Boixo, Sergio0000-0002-1090-7584
Neven, Hartmut0000-0002-9681-6746
Additional Information:© 2022 American Physical Society. (Received 23 July 2021; revised 21 March 2022; accepted 12 May 2022; published 3 June 2022) A. Z. acknowledges support from Caltech’s Intelligent Quantum Networks and Technologies (INQNET) research program and by the DOE/HEP QuantISED program grant, Quantum Machine Learning and Quantum Computation Frameworks (QMLQCF) for HEP, Grant No. DE-SC0019227.
Group:INQNET
Funders:
Funding AgencyGrant Number
INtelligent Quantum NEtworks and Technologies (INQNET)UNSPECIFIED
Department of Energy (DOE)DE-SC0019227
Issue or Number:22
DOI:10.1103/physrevlett.128.220505
Record Number:CaltechAUTHORS:20220606-736195000
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20220606-736195000
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:115038
Collection:CaltechAUTHORS
Deposited By: George Porter
Deposited On:06 Jun 2022 22:49
Last Modified:25 Jul 2022 23:14

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