Training of quantum circuits on a hybrid quantum computer.

Autor:	Zhu D; Joint Quantum Institute, Department of Physics, and Joint Center for Quantum Information and Computer Science, University of Maryland, College Park, MD 20742, USA., Linke NM; Joint Quantum Institute, Department of Physics, and Joint Center for Quantum Information and Computer Science, University of Maryland, College Park, MD 20742, USA., Benedetti M; Department of Computer Science, University College London, WC1E 6BT London, UK.; Cambridge Quantum Computing Limited, CB2 1UB Cambridge, UK., Landsman KA; Joint Quantum Institute, Department of Physics, and Joint Center for Quantum Information and Computer Science, University of Maryland, College Park, MD 20742, USA., Nguyen NH; Joint Quantum Institute, Department of Physics, and Joint Center for Quantum Information and Computer Science, University of Maryland, College Park, MD 20742, USA., Alderete CH; Joint Quantum Institute, Department of Physics, and Joint Center for Quantum Information and Computer Science, University of Maryland, College Park, MD 20742, USA., Perdomo-Ortiz A; Department of Computer Science, University College London, WC1E 6BT London, UK.; Zapata Computing Inc., 439 University Avenue, Office 535, Toronto, ON, M5G 1Y8, Canada., Korda N; Mind Foundry Limited, OX2 7DD Oxford, UK., Garfoot A; Mind Foundry Limited, OX2 7DD Oxford, UK., Brecque C; Mind Foundry Limited, OX2 7DD Oxford, UK., Egan L; Joint Quantum Institute, Department of Physics, and Joint Center for Quantum Information and Computer Science, University of Maryland, College Park, MD 20742, USA., Perdomo O; Department of Mathematics, Central Connecticut State University, New Britain, CT 06050, USA., Monroe C; Joint Quantum Institute, Department of Physics, and Joint Center for Quantum Information and Computer Science, University of Maryland, College Park, MD 20742, USA.; IonQ Inc., College Park, MD 20740, USA.
Jazyk:	angličtina
Zdroj:	Science advances [Sci Adv] 2019 Oct 18; Vol. 5 (10), pp. eaaw9918. Date of Electronic Publication: 2019 Oct 18 (Print Publication: 2019).
DOI:	10.1126/sciadv.aaw9918
Abstrakt:	Generative modeling is a flavor of machine learning with applications ranging from computer vision to chemical design. It is expected to be one of the techniques most suited to take advantage of the additional resources provided by near-term quantum computers. Here, we implement a data-driven quantum circuit training algorithm on the canonical Bars-and-Stripes dataset using a quantum-classical hybrid machine. The training proceeds by running parameterized circuits on a trapped ion quantum computer and feeding the results to a classical optimizer. We apply two separate strategies, Particle Swarm and Bayesian optimization to this task. We show that the convergence of the quantum circuit to the target distribution depends critically on both the quantum hardware and classical optimization strategy. Our study represents the first successful training of a high-dimensional universal quantum circuit and highlights the promise and challenges associated with hybrid learning schemes. (Copyright © 2019 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC).)
Databáze:	MEDLINE
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