4 papers
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…
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…
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…
Single Qubit State Estimation on NISQ Devices with Limited Resources and SIC-POVMs
Cristian A. Galvis-Florez, Daniel Reitzner, Simo Särkkä
Current quantum computers have the potential to overcome classical computational methods, however, the capability of the algorithms that can be executed on noisy intermediate-scale…