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Challenges and opportunities in quantum machine learning

Abstract

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.

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Fig. 1: QML tasks.
Fig. 2: Key applications for QML.
Fig. 3: Classification with QML.
Fig. 4: Examples of QNN architectures.
Fig. 5: Challenges for QML.

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Acknowledgements

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 (www.x.company). H.-Y.H. is supported by a Google PhD fellowship.

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Cerezo, M., Verdon, G., Huang, HY. et al. Challenges and opportunities in quantum machine learning. Nat Comput Sci 2, 567–576 (2022). https://doi.org/10.1038/s43588-022-00311-3

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