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Lecture notes in electrical engineering

Quantum Machine Learning Algorithms for Diagnostic Applications: A Review

Shruti S. Pophale, Amit Gadekar

Published Jan 1, 2023• 3 citations
Publisher
DOI
10.1007/978-981-19-9989-5_3

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QML, short for quantum machine learning, is experiencing rapid growth as it combines the power of quantum computers with machine learning methods to enhance pattern identification and prediction. QML has demonstrated significant promise in biomedical research, since it has been used for the analysis of massive data sets from diverse sources, such as proteomics, genomics, and medical imaging. This chapter gives a broad overview of QML’s present status in biomedical applications, including both the theoretical underpinnings of the language and real-world implementations. First, we go over the fundamental ideas of quantum computing and machine learning, such as quantum gates, quantum circuits, and traditional machine learning techniques. The main difficulties in applying QML to biomedical applications are then discussed, including handling noisy quantum hardware and creating effective quantum algorithms for large, complicated data sets. We also go over some of the most exciting directions for this field’s research, such as the use of QML for illness diagnostics, personalized medicine, and drug development. In conclusion, we offer an array of case examples that demonstrate QML’s potential for use in biomedical research. These case studies address a variety of subjects, such as identifying protein-ligand binding affinities and using medical picture analysis to diagnose cancer. All things considered, this chapter offers a thorough review of QML’s current status in biomedical research, stressing both its promise and the obstacles that must be removed in order to fully realize it.