Open Access

ARTICLE

Quantum Fuzzy Support Vector Machine for Binary Classification

by Xi Huang^1,2, Shibin Zhang^1,2,*, Chen Lin^1,2, Jinyue Xia³

1 School of Cybersecurity, Chengdu University of Information Technology, Chengdu, 610225, China
2 Sichuan Key Laboratory of Advanced Cryptography and System Security, Chengdu, 610225, China
3 International Business Machines Corporation (IBM), New York, 14201, USA

* Corresponding Author: Shibin Zhang. Email:

Computer Systems Science and Engineering 2023, 45(3), 2783-2794. https://doi.org/10.32604/csse.2023.032190

Received 10 May 2022; Accepted 01 August 2022; Issue published 21 December 2022

Abstract

In the objective world, how to deal with the complexity and uncertainty of big data efficiently and accurately has become the premise and key to machine learning. Fuzzy support vector machine (FSVM) not only deals with the classification problems for training samples with fuzzy information, but also assigns a fuzzy membership degree to each training sample, allowing different training samples to contribute differently in predicting an optimal hyperplane to separate two classes with maximum margin, reducing the effect of outliers and noise, Quantum computing has super parallel computing capabilities and holds the promise of faster algorithmic processing of data. However, FSVM and quantum computing are incapable of dealing with the complexity and uncertainty of big data in an efficient and accurate manner. This paper research and propose an efficient and accurate quantum fuzzy support vector machine (QFSVM) algorithm based on the fact that quantum computing can efficiently process large amounts of data and FSVM is easy to deal with the complexity and uncertainty problems. The central idea of the proposed algorithm is to use the quantum algorithm for solving linear systems of equations (HHL algorithm) and the least-squares method to solve the quadratic programming problem in the FSVM. The proposed algorithm can determine whether a sample belongs to the positive or negative class while also achieving a good generalization performance. Furthermore, this paper applies QFSVM to handwritten character recognition and demonstrates that QFSVM can be run on quantum computers, and achieve accurate classification of handwritten characters. When compared to FSVM, QFSVM’s computational complexity decreases exponentially with the number of training samples.

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Keywords

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