Challenges and Opportunities in Quantum Machine LearningDownload PDFOpen Website

Published: 01 Jan 2023, Last Modified: 13 May 2023CoRR 2023Readers: Everyone
Abstract: 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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