A hybrid classical-quantum approach for multi-class classification

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...

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Bibliographic Details
Published in:Quantum information processing 2021-03, Vol.20 (3), Article 119
Main Authors: Chalumuri, Avinash, Kune, Raghavendra, Manoj, B. S.
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Banknotes
Circuits
Classification
Classifiers
Computers
Data Structures and Information Theory
Datasets
Machine learning
Mathematical Physics
Mechanical properties
Model accuracy
Physics
Physics and Astronomy
Quantum computers
Quantum Computing
Quantum entanglement
Quantum Information Technology
Quantum mechanics
Quantum Physics
Qubits (quantum computing)
Simulation
Spintronics
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Summary: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.
ISSN:1570-0755
1573-1332
DOI:10.1007/s11128-021-03029-9