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Quantum Driven Machine Learning

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Abstract

Quantum computing is proving to be very beneficial for solving complex machine learning problems. Quantum computers are inherently excellent in handling and manipulating vectors and matrix operations. The ever increasing size of data has started creating bottlenecks for classical machine learning systems. Quantum computers are emerging as potential solutions to tackle big data related problems. This paper presents a quantum machine learning model based on quantum support vector machine (QSVM) algorithm to solve a classification problem. The quantum machine learning model is practically implemented on quantum simulators and real-time superconducting quantum processors. The performance of quantum machine learning model is computed in terms of processing speed and accuracy and compared against its classical counterpart. The breast cancer dataset is used for the classification problem. The results are indicative that quantum computers offer quantum speed-up.

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Acknowledgments

Authors are highly grateful to IBMQ for providing access to their software and hardware computational resources through cloud network.

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Authors and Affiliations

  1. Centre for Development of Advanced Computing (C-DAC), Mohali, Ministry of Electronics & Information Technology (MeitY), Mohali, 160071, India

    Shivani Saini, PK Khosla, Manjit Kaur & Gurmohan Singh

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  1. Shivani Saini
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  2. PK Khosla
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  3. Manjit Kaur
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  4. Gurmohan Singh
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Correspondence to Gurmohan Singh.

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Saini, S., Khosla, P., Kaur, M. et al. Quantum Driven Machine Learning. Int J Theor Phys 59, 4013–4024 (2020). https://doi.org/10.1007/s10773-020-04656-1

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  • DOI: https://doi.org/10.1007/s10773-020-04656-1

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