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.
%0 Generic
%1 zhao2015quantum
%A Zhao, Zhikuan
%A Fitzsimons, Jack K.
%A Fitzsimons, Joseph F.
%D 2015
%K machinelearn quantumcomputing
%R 10.1103/PhysRevA.99.052331
%T Quantum assisted Gaussian process regression
%U http://arxiv.org/abs/1512.03929
%X 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.
@misc{zhao2015quantum,
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.},
added-at = {2019-06-23T18:10:11.000+0200},
author = {Zhao, Zhikuan and Fitzsimons, Jack K. and Fitzsimons, Joseph F.},
biburl = {https://www.bibsonomy.org/bibtex/276642ee45e8945c4143b6f68516f0e0c/cmcneile},
description = {Quantum assisted Gaussian process regression},
doi = {10.1103/PhysRevA.99.052331},
interhash = {13f9f0e93534e78445e699a206e96cc3},
intrahash = {76642ee45e8945c4143b6f68516f0e0c},
keywords = {machinelearn quantumcomputing},
note = {cite arxiv:1512.03929Comment: 4 pages. Comments welcome},
timestamp = {2019-06-23T18:10:11.000+0200},
title = {Quantum assisted Gaussian process regression},
url = {http://arxiv.org/abs/1512.03929},
year = 2015
}