DOI: 10.1038/s43588-022-00311-3

Challenges and Opportunities in Quantum Machine Learning

M. CerezoGuillaume VerdonHsin-Yuan HuangLukasz CincioPatrick J. Coles

Mar 2023

摘要

At the intersection of machine learning and quantum computing, QuantumMachine Learning (QML) has the potential of accelerating data analysis,especially for quantum data, with applications for quantum materials,biochemistry, and high-energy physics. Nevertheless, challenges remainregarding the trainability of QML models. Here we review current methods andapplications for QML. We highlight differences between quantum and classicalmachine learning, with a focus on quantum neural networks and quantum deeplearning. Finally, we discuss opportunities for quantum advantage with QML.

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