Training of quantum circuits on a hybrid quantum computer-Reference-Cited by-同舟云学术

Training of quantum circuits on a hybrid quantum computer

Published:2019-10-04 Issue:10 Volume:5 Page:
ISSN:2375-2548
Container-title:Science Advances
language:en
Short-container-title:Sci. Adv.

Author:

Zhu D.¹^ORCID,Linke N. M.¹^ORCID,Benedetti M.²³^ORCID,Landsman K. A.¹,Nguyen N. H.¹^ORCID,Alderete C. H.¹,Perdomo-Ortiz A.²⁴,Korda N.⁵,Garfoot A.⁵,Brecque C.⁵,Egan L.¹,Perdomo O.⁶,Monroe C.¹⁷^ORCID

Affiliation:

1. Joint Quantum Institute, Department of Physics, and Joint Center for Quantum Information and Computer Science, University of Maryland, College Park, MD 20742, USA.

2. Department of Computer Science, University College London, WC1E 6BT London, UK.

3. Cambridge Quantum Computing Limited, CB2 1UB Cambridge, UK.

4. Zapata Computing Inc., 439 University Avenue, Office 535, Toronto, ON, M5G 1Y8, Canada.

5. Mind Foundry Limited, OX2 7DD Oxford, UK.

6. Department of Mathematics, Central Connecticut State University, New Britain, CT 06050, USA.

7. IonQ Inc., College Park, MD 20740, USA.

Abstract

We train generative modeling circuits on a quantum hybrid computer showing an optimization strategy and a resource trade-off.

Funder

National Science Foundation

Air Force Office of Scientific Research

Army Research Office

Intelligence Advanced Research Projects Activity

Publisher

American Association for the Advancement of Science (AAAS)

Subject

Multidisciplinary

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