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Bulletin of the American Physical Society

Quantum machine learning: Challenges and Opportunities

Leonard Wossnig, Simone Severini B

Published Mar 6, 2019• 1 citation
OpenAlex

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The explosive growth of data generation in recent years has pushed the boundaries of classical machine learning algorithms, creating a pressing need for more efficient and scalable solutions. Quantum machine learning (QML), an emerging interdisciplinary field combining quantum computing and machine learning, offers the potential to address these challenges. This paper provides a comprehensive overview of QML for big data processing, exploring its opportunities, inherent limitations, and future directions. We delve into key QML algorithms like Quantum Principal Component Analysis (QPCA), Quantum Support Vector Machines (QSVM), and Quantum Neural Networks (QNNs), analyzing their computational complexities and potential speedups over classical counterparts. We also discuss the challenges associated with practical implementation, including hardware limitations, data encoding, and the development of robust error correction techniques. Finally, we outline promising research avenues that could unlock the full potential of QML for big data processing, ultimately revolutionizing fields like drug discovery, finance, and materials science