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

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.

References in corpus (5)

• Quantum algorithm for solving linear systems of equations
• Quantum random access memory
• Strong Coupling of a Spin Ensemble to a Superconducting Resonator
• Architectures for a quantum random access memory
• Magnetic strong coupling in a spin-photon system and transition to classical regime

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