Challenges and opportunities in quantum machine learning.

Autor: Cerezo M; Information Sciences, Los Alamos National Laboratory, Los Alamos, NM, USA.; Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, NM, USA.; Quantum Science Center, Oak Ridge, TN, USA., Verdon G; X, Mountain View, CA, USA.; Institute for Quantum Computing, University of Waterloo, Waterloo, Ontario, Canada.; Department of Applied Mathematics, University of Waterloo, Waterloo, Ontario, Canada., Huang HY; Institute for Quantum Information and Matter, California Institute of Technology, Pasadena, CA, USA.; Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, CA, USA., Cincio L; Quantum Science Center, Oak Ridge, TN, USA.; Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM, USA., Coles PJ; Quantum Science Center, Oak Ridge, TN, USA. pcoles@lanl.gov.; Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM, USA. pcoles@lanl.gov.
Jazyk: angličtina
Zdroj: Nature computational science [Nat Comput Sci] 2022 Sep; Vol. 2 (9), pp. 567-576. Date of Electronic Publication: 2022 Sep 15.
DOI: 10.1038/s43588-022-00311-3
Abstrakt: 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.
(© 2022. Springer Nature America, Inc.)
Databáze: MEDLINE
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