3 papers
stat.CO2025
Quantum-assisted Gaussian process regression using random Fourier features
Cristian A. Galvis-Florez, Ahmad Farooq, Simo Särkkä
Probabilistic machine learning models are distinguished by their ability to integrate prior knowledge of noise statistics, smoothness parameters, and training data uncertainty. A c…
stat.CO2025
Provable Quantum Algorithm Advantage for Gaussian Process Quadrature
Cristian A. Galvis-Florez, Ahmad Farooq, Simo Särkkä
The aim of this paper is to develop novel quantum algorithms for Gaussian process quadrature methods. Gaussian process quadratures are numerical integration methods where Gaussian…
stat.CO2024
Quantum-Assisted Hilbert-Space Gaussian Process Regression
Ahmad Farooq, Cristian A. Galvis-Florez, Simo Särkkä
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 prio…