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Challenges and opportunities in quantum machine learning

Cerezo, M. and Verdon, Guillaume and Huang, Hsin-Yuan and Cincio, Lukasz and Coles, Patrick J. (2022) Challenges and opportunities in quantum machine learning. Nature Computational Science, 2 (9). pp. 567-576. ISSN 2662-8457. doi:10.1038/s43588-022-00311-3.

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At the intersection of machine learning and quantum computing, quantum machine learning has the potential of accelerating data analysis, especially for quantum data, with applications for quantum materials, biochemistry and high-energy physics. Nevertheless, challenges remain regarding the trainability of quantum machine learning models. Here we review current methods and applications for quantum machine learning. We highlight differences between quantum and classical machine learning, with a focus on quantum neural networks and quantum deep learning. Finally, we discuss opportunities for quantum advantage with quantum machine learning.

Item Type:Article
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URLURL TypeDescription ReadCube access
Cerezo, M.0000-0002-2757-3170
Verdon, Guillaume0000-0001-6583-5760
Huang, Hsin-Yuan0000-0001-5317-2613
Cincio, Lukasz0000-0002-6758-4376
Coles, Patrick J.0000-0001-9879-8425
Additional Information:M.C. acknowledges support from the Los Alamos National Laboratory (LANL) LDRD program under project number 20210116DR. M.C. was also supported by the Center for Nonlinear Studies at LANL. L.C. and P.J.C. were supported by the US Department of Energy, Office of Science, Office of Advanced Scientific Computing Research, under the Accelerated Research in Quantum Computing program. L.C. also acknowledges support from US Department of Energy, Office of Science, National Quantum Information Science Research Centers, Quantum Science Center. P.J.C. was also supported by the NNSA’s Advanced Simulation and Computing Beyond Moore’s Law Program at LANL. G.V. thanks F. Sbahi, A. J. Martinez and P. Velickovic for useful discussions. X, formerly known as Google[x], is part of the Alphabet family of companies, which includes Google, Verily, Waymo and others ( H.-Y.H. is supported by a Google PhD fellowship.
Funding AgencyGrant Number
Los Alamos National Laboratory20210116DR
Google PhD FellowshipUNSPECIFIED
Department of Energy (DOE)UNSPECIFIED
Issue or Number:9
Record Number:CaltechAUTHORS:20221213-185576500.2
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:118332
Deposited By: Research Services Depository
Deposited On:18 Jan 2023 18:21
Last Modified:18 Jan 2023 18:21

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