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A hybrid classical-quantum approach for multi-class classification

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Abstract

Quantum machine learning recently gained prominence due to the computational ability of quantum computers in solving machine learning problems that are intractable on a classical computer. However, achieving a quantum advantage on present-day quantum computers remains an open challenge. In this work, we primarily focus on solving machine learning problems using a hybrid model based on both quantum and classical computers together for the classification task. We propose the quantum multi-class classifier (QMCC) as a variational circuit with a hybrid classical-quantum approach using quantum mechanical properties such as superposition and entanglement. A unitary operation on a single qubit for the state preparation is designed and also demonstrated using a real quantum computer on the IBMQX platform. The entire variational circuit for the classification task is implemented on a quantum simulator. We performed our quantum simulations on three benchmark datasets: Iris dataset, Banknote Authentication (BNA) dataset, and Wireless Indoor Localization (WIL) dataset for machine learning algorithms. Our simulation results show that the proposed QMCC model classified Iris dataset with an accuracy of 92.10%, BNA dataset with an accuracy of 89.50%, and WIL dataset with an accuracy of 91.73%. The proposed model can also be extended to multiple class classifiers.

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Acknowledgements

We thank Maria Schuld and PennyLane team of Xanadu Inc., Kaushik Mukherjee of Indian Institute of Space Science and Technology and Indranil Ghosh from Jadavpur University for the useful discussions.

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  1. Indian Institute of Space Science and Technology, Trivandrum, India

    Avinash Chalumuri & B. S. Manoj

  2. Advanced Data Processing Research Institute, Secunderabad, India

    Raghavendra Kune

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  1. Avinash Chalumuri
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Correspondence to B. S. Manoj.

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Chalumuri, A., Kune, R. & Manoj, B.S. A hybrid classical-quantum approach for multi-class classification. Quantum Inf Process 20, 119 (2021). https://doi.org/10.1007/s11128-021-03029-9

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