Quantum-Assisted Hilbert-Space Gaussian Process Regression

Gaussian processes are probabilistic models that are commonly used as functional priors in machine learning. Due to their probabilistic nature, they can be used to capture the prior information on the statistics of noise, smoothness of the functions, and training data uncertainty. However, their computational complexity quickly becomes intractable as the size of the data set grows. We propose a Hilbert space approximation-based quantum algorithm for Gaussian process regression to overcome this limitation. Our method consists of a combination of …

complexity computational cs.lg data functional functions gaussian processes information machine machine learning nature noise prior process processes quant-ph quantum regression space stat.co statistics training training data uncertainty