Quantum support vector machine for big data classification

TL;DR

This paper demonstrates that quantum computers can implement support vector machines with exponential speed-up for big data classification by efficiently performing matrix operations.

Contribution

It introduces a quantum algorithm for SVMs that achieves logarithmic complexity in data size, significantly improving over classical methods.

Findings

Quantum SVM implementation with exponential speed-up

Efficient matrix inversion of kernel matrices on quantum computers

Potential for handling large-scale data classification tasks

Abstract

Supervised machine learning is the classification of new data based on already classified training examples. In this work, we show that the support vector machine, an optimized binary classifier, can be implemented on a quantum computer, with complexity logarithmic in the size of the vectors and the number of training examples. In cases when classical sampling algorithms require polynomial time, an exponential speed-up is obtained. At the core of this quantum big data algorithm is a non-sparse matrix exponentiation technique for efficiently performing a matrix inversion of the training data inner-product (kernel) matrix.