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A Review of Machine Learning Classification Using Quantum Annealing for Real-World Applications

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Abstract

Optimizing the training of a machine learning pipeline helps in reducing training costs and improving model performance. One such optimizing strategy is quantum annealing, which is an emerging computing paradigm that has shown potential in optimizing the training of a machine learning model. The implementation of a physical quantum annealer has been realized by D-wave systems and is available to the research community for experiments. Recent experimental results on a variety of machine learning applications using quantum annealing have shown interesting results where the performance of classical machine learning techniques is limited by limited training data and high dimensional features. This article explores the application of D-wave’s quantum annealer for optimizing machine learning pipelines for real-world classification problems. We review the application domains on which a physical quantum annealer has been used to train machine learning classifiers. We discuss and analyze the experiments performed on the D-Wave quantum annealer for applications such as image recognition, remote sensing imagery, computational biology, and particle physics. We discuss the possible advantages and the problems for which quantum annealing is likely to be advantageous over classical computation.

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Authors and Affiliations

  1. University of Kentucky, Lexington, KY, USA

    Rajdeep Kumar Nath & Himanshu Thapliyal

  2. Oak Ridge National Laboratory, Oak Ridge, TN, USA

    Travis S. Humble

Authors
  1. Rajdeep Kumar Nath
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  2. Himanshu Thapliyal
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  3. Travis S. Humble
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Correspondence to Himanshu Thapliyal.

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This article is part of the topical collection “Hardware for AI, Machine Learning and Emerging Electronic Systems” guest edited by Himanshu Thapliyal, Saraju Mohanty and VS Kanchana Bhaaskaran.

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Nath, R.K., Thapliyal, H. & Humble, T.S. A Review of Machine Learning Classification Using Quantum Annealing for Real-World Applications. SN COMPUT. SCI. 2, 365 (2021). https://doi.org/10.1007/s42979-021-00751-0

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