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Variational quantum optimization with multibasis encodings

Patti, Taylor L. and Kossaifi, Jean and Anandkumar, Animashree and Yelin, Susanne F. (2022) Variational quantum optimization with multibasis encodings. Physical Review Research, 4 (3). Art. No. 4.033142. ISSN 2643-1564. doi:10.1103/physrevresearch.4.033142. https://resolver.caltech.edu/CaltechAUTHORS:20220909-232706000

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Abstract

Despite extensive research efforts, few quantum algorithms for classical optimization demonstrate a realizable quantum advantage. The utility of many quantum algorithms is limited by high requisite circuit depth and nonconvex optimization landscapes. We tackle these challenges by introducing a variational quantum algorithm that benefits from two innovations: multibasis graph encodings using single-qubit expectation values and nonlinear activation functions. Our technique results in increased observed optimization performance and a factor-of-two reduction in requisite qubits. While the classical simulation of many qubits with traditional quantum formalism is impossible due to its exponential scaling, we mitigate this limitation with exact circuit representations using factorized tensor rings. In particular, the shallow circuits permitted by our technique, combined with efficient factorized tensor-based simulation, enable us to successfully optimize the MaxCut of the 512-vertex DIMACS library graphs on a single GPU. By improving the performance of quantum optimization algorithms while requiring fewer quantum resources and utilizing shallower, more error-resistant circuits, we offer tangible progress for variational quantum optimization.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1103/PhysRevResearch.4.033142DOIArticle
ORCID:
AuthorORCID
Patti, Taylor L.0000-0002-4242-6072
Kossaifi, Jean0000-0002-4445-3429
Anandkumar, Animashree0000-0002-6974-6797
Additional Information:This work was done during T.L.P.'s internship at NVIDIA. At CalTech, A.A. is supported in part by the Bren endowed chair, and Microsoft, Google, Adobe faculty fellowships. S.F.Y. thanks the AFOSR and the NSF for funding. The authors would like to thank Brucek Khailany, Johnnie Gray, Garnet Chan, Andreas Hehn, and Adam Jedrych for conversations.
Funders:
Funding AgencyGrant Number
Air Force Office of Scientific Research (AFOSR)UNSPECIFIED
NSFUNSPECIFIED
Bren Professor of Computing and Mathematical SciencesUNSPECIFIED
Microsoft Faculty FellowshipUNSPECIFIED
Google Faculty Research AwardUNSPECIFIED
AdobeUNSPECIFIED
Issue or Number:3
DOI:10.1103/physrevresearch.4.033142
Record Number:CaltechAUTHORS:20220909-232706000
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20220909-232706000
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:116869
Collection:CaltechAUTHORS
Deposited By: Olivia Warschaw
Deposited On:29 Oct 2022 22:11
Last Modified:01 Nov 2022 18:00

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