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Challenges and Opportunities in Quantum Machine Learning

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arxiv 2303.09491 v1 pith:F7OKCERL submitted 2023-03-16 quant-ph cs.LGstat.ML

M. Cerezo , Guillaume Verdon , Hsin-Yuan Huang , Lukasz Cincio , Patrick J. Coles This is my paper
classification quant-phcs.LGstat.ML
keywords quantumlearningmachineapplicationschallengesdataopportunitiesaccelerating
verification ladder T0 review T1 audit T2 compute T3 formal
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At the intersection of machine learning and quantum computing, Quantum Machine 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 remain regarding the trainability of QML models. Here we review current methods and applications for QML. 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 QML.

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    A hybrid quantum-classical graph transformer for sentiment classification reports higher accuracy and better sample efficiency than a classical graph transformer on five small benchmark datasets.

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