An All-Pair Quantum SVM Approach for Big Data Multiclass Classification

TL;DR

This paper proposes a quantum all-pair SVM method for big data multiclass classification, achieving exponential speedup over classical approaches by leveraging quantum computing's logarithmic runtime complexity.

Contribution

It introduces a quantum all-pair multiclass SVM approach that significantly reduces runtime complexity compared to classical methods.

Findings

Achieves logarithmic runtime complexity on quantum computers.

Provides exponential speedup over classical multiclass SVMs.

Applicable to other classification algorithms for speed improvements.

Abstract

In this paper, we have discussed a quantum approach for the all-pair multiclass classification problem. We have shown that the multiclass support vector machine for big data classification with a quantum all-pair approach can be implemented in logarithm runtime complexity on a quantum computer. In an all-pair approach, there is one binary classification problem for each pair of classes, and so there are k (k-1)/2 classifiers for a k-class problem. As compared to the classical multiclass support vector machine that can be implemented with polynomial run time complexity, our approach exhibits exponential speed up in the quantum version. The quantum all-pair algorithm can be used with other classification algorithms, and a speed up gain can be achieved as compared to their classical counterparts.