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Quantum Machine Learning: A Review and Current Status

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Data Management, Analytics and Innovation

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1175))

Abstract

Quantum machine learning is at the intersection of two of the most sought after research areas—quantum computing and classical machine learning. Quantum machine learning investigates how results from the quantum world can be used to solve problems from machine learning. The amount of data needed to reliably train a classical computation model is evergrowing and reaching the limits which normal computing devices can handle. In such a scenario, quantum computation can aid in continuing training with huge data. Quantum machine learning looks to devise learning algorithms faster than their classical counterparts. Classical machine learning is about trying to find patterns in data and using those patterns to predict further events. Quantum systems, on the other hand, produce atypical patterns which are not producible by classical systems, thereby postulating that quantum computers may overtake classical computers on machine learning tasks. Here, we review the previous literature on quantum machine learning and provide the current status of it.

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Acknowledgments

A.P.D. acknowledges the support of KVPY fellowship. S.R. and S.C. acknowledge the support of DST Inspire fellowship. B.K.B acknowledges the support of IISER-K Institute fellowship.

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Indian Institute of Information Technology, Kalyani, West Bengal, India

    Nimish Mishra

  2. Department of Physics, Indian Institute of Technology Guwahati, Guwahati, 781039, Assam, India

    Manik Kapil

  3. Department of Computer Science and Engineering, Nitte Meenakshi Institute of Technology, Yelahanka, Bangalore, India

    Hemant Rakesh

  4. Department of Mechanical Engineering, Indian Institute Of Engineering Science And Technology, Shibpur, 711103, West Bengal, India

    Amit Anand

  5. Department of Electronics and Telecommunication, International Institute of Information Technology, Bhubaneswar, 751003, Odisha, India

    Nilima Mishra

  6. Department of Physics, Bennett University, Greater Noida, 201310, Uttar Pradesh, India

    Aakash Warke & Valay Kumar Jain

  7. Department of Physics, National Institute of Technology Karnataka, Karnataka, 575025, India

    Soumya Sarkar & Rakshit Gharat

  8. Department of Electronics and Telecommunication, Jadavpur UniverGreater Noidasity, Kolkata, India

    Sanchayan Dutta

  9. Department of Physics, Panjab University, Chandigarh, 160036, Chandigarh, India

    Sabhyata Gupta

  10. Department of Physical Sciences, Indian Institute of Science Education and Research Berhampur, Berhampur, 760010, Odisha, India

    Aditya Prasad Dash & Shuvarati Roy

  11. Department of Physics and Astronomy, National Institute of Technology Rourkela, Odisha, 769008, India

    Yagnik Chatterjee

  12. School of Physical Sciences, National Institute of Science Education and Research, HBNI, Jatni, 752050, Odisha, India

    Shivam Raj

  13. Department of Chemical Engineering, MBM Engineering College, Jodhpur, India

    Shreeram Bagaria

  14. Department of Physics, Indian Institute Of Technology, Kanpur, Kalyanpur, Kanpur, India

    Smit Chaudhary

  15. Indian Institute of Technology (Indian School of Mines), Dhanbad, 826004, Jharkhand, India

    Vishwanath Singh

  16. Department of Physics, Central University of Karnataka, Karnataka, 585367, India

    Rituparna Maji

  17. Bikash’s Quantum (OPC) Private Limited, Balindi, Mohanpur, Nadia, 741246, West Bengal, India

    Priyanka Dalei & Bikash K. Behera

  18. Department of Physical Sciences, Indian Institute of Science Education and Research Kolkata, Mohanpur, 741246, West Bengal, India

    Bikash K. Behera & Prasanta K. Panigrahi

  19. BIMS Kolkata, Kolkata, 700 097, West Bengal, India

    Sabyasachi Mukhopadhyay

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  20. Priyanka Dalei
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  21. Bikash K. Behera
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  22. Sabyasachi Mukhopadhyay
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  23. Prasanta K. Panigrahi
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Corresponding author

Correspondence to Bikash K. Behera .

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Editors and Affiliations

  1. Society for Data Science, Pune, Maharashtra, India

    Neha Sharma

  2. A.K. Choudhury School of Information Technology, University of Calcutta, Kolkata, West Bengal, India

    Amlan Chakrabarti

  3. Department of Automatics and Applied Software, Faculty of Engineering, University of Arad, Arad, Romania

    Valentina Emilia Balas

  4. IT4Innovations, VSB-Technical University of Ostrava, Ostrava, Czech Republic

    Jan Martinovic

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Mishra, N. et al. (2021). Quantum Machine Learning: A Review and Current Status. In: Sharma, N., Chakrabarti, A., Balas, V.E., Martinovic, J. (eds) Data Management, Analytics and Innovation. Advances in Intelligent Systems and Computing, vol 1175. Springer, Singapore. https://doi.org/10.1007/978-981-15-5619-7_8

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