A Caltech Library Service

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.

[img] PDF - Published Version
See Usage Policy.

[img] PDF (further numerical experiments regarding training stability/convergence, technical details of implemented circuits, and a generalization of the proposal to mixed states) - Supplemental Material
See Usage Policy.

[img] PDF - Submitted Version
Creative Commons Attribution Non-commercial No Derivatives.


Use this Persistent URL to link to this item:


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 Paper Information
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.
Funding AgencyGrant Number
INtelligent Quantum NEtworks and Technologies (INQNET)UNSPECIFIED
Department of Energy (DOE)DE-SC0019227
Issue or Number:22
Record Number:CaltechAUTHORS:20220606-736195000
Persistent URL:
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:115038
Deposited By: George Porter
Deposited On:06 Jun 2022 22:49
Last Modified:25 Jul 2022 23:14

Repository Staff Only: item control page