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Quantum Machine Learning Algorithms for Diagnostic Applications: A Review

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International Virtual Conference on Industry 4.0 (IVCI 2021)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 1003))

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Abstract

Big data analytics is a huge information investigation in the utilization of cutting edge scientific procedures against extremely enormous, various in-formational collections that incorporate organized, semi-organized and unstructured information, from various sources, and in various sizes from terabytes to zettabytes. Big data is in many formats such as text, photographs, videos, and so on. Big data goes beyond the storage and processing capability of a traditional computer. Accordingly, distinguishing designs in enormous information is extremely challenging. A classical computing measures a lot of information over a long measure of time. Quantum computing is a new research area where information and data are computed in a short measure of time. This is a survey study that glances at how enormous information and quantum machine learning can be utilized to distinguish information designs for a collection of utilizations. The advantages of quantum guided and unsupervised machine learning are contrasted to those of classical computing. Quantum computing in machine learning algorithms: challenges and techniques are also discussed. Despite the fact that quantum machine learning is a promising area, several real-world applications have been discussed.

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Author information

Authors and Affiliations

  1. Department of CSE, Sandip University, Nashik, Maharashtra, India

    Shruti S. Pophale

  2. School of Computer Science and Engineering, Sandip University, Nashik, Maharashtra, India

    Amit Gadekar

Authors
  1. Shruti S. Pophale
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  2. Amit Gadekar
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Corresponding author

Correspondence to Shruti S. Pophale .

Editor information

Editors and Affiliations

  1. School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, India

    R. Jagadeesh Kannan

  2. School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, India

    S. Geetha

  3. Department of Science and Engineering, Manchester Metropolitan University, Manchester, UK

    Sravanthi Sashikumar

  4. Academic Lead Industry 4.0, Manchester Metropolitan University, Manchester, UK

    Carl Diver

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© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Pophale, S.S., Gadekar, A. (2023). Quantum Machine Learning Algorithms for Diagnostic Applications: A Review. In: Kannan, R.J., Geetha, S., Sashikumar, S., Diver, C. (eds) International Virtual Conference on Industry 4.0. IVCI 2021. Lecture Notes in Electrical Engineering, vol 1003. Springer, Singapore. https://doi.org/10.1007/978-981-19-9989-5_3

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  • DOI: https://doi.org/10.1007/978-981-19-9989-5_3

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-19-9988-8

  • Online ISBN: 978-981-19-9989-5

  • eBook Packages: EngineeringEngineering (R0)

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