Quantum assisted Gaussian process regression

TL;DR

This paper demonstrates how quantum algorithms can significantly accelerate Gaussian process regression, reducing computational complexity from cubic to potentially exponential or polynomial, thus enhancing machine learning efficiency.

Contribution

It introduces a quantum approach to Gaussian process regression, leveraging the quantum linear systems algorithm for substantial speedups over classical methods.

Findings

Quantum linear systems algorithm enables exponential speedup in GPR

Polynomial efficiency gains are possible even in less ideal cases

Potential for significant reduction in regression computation time

Abstract

Gaussian processes (GP) are a widely used model for regression problems in supervised machine learning. Implementation of GP regression typically requires $O(n^3)$ logic gates. We show that the quantum linear systems algorithm [Harrow et al., Phys. Rev. Lett. 103, 150502 (2009)] can be applied to Gaussian process regression (GPR), leading to an exponential reduction in computation time in some instances. We show that even in some cases not ideally suited to the quantum linear systems algorithm, a polynomial increase in efficiency still occurs.