TY - JOUR
T1 - Quantum Gaussian process state
T2 - A kernel-inspired state with quantum support data
AU - Rath, Yannic
AU - Booth, George H.
N1 - Funding Information:
The authors are thankful for valuable discussions with Aldo Glielmo, Andrew Green and Gábor Csányi relating to this work. G.H.B. gratefully acknowledges support from the Royal Society via a University Research Fellowship, and funding from the Air Force Office of Scientific Research via Grant No. FA9550-18-1-0515. The project has also received funding from the European Union's Horizon 2020 research and innovation programme under Grant Agreement No. 759063. We are grateful to the UK Materials and Molecular Modelling Hub for computational resources, which is partially funded by EPSRC (Grants No. EP/P020194/1 and No. EP/T022213/1).
Publisher Copyright:
© 2022 authors. Published by the American Physical Society.
PY - 2022/6
Y1 - 2022/6
N2 - We introduce the quantum Gaussian process state, motivated via a statistical inference for the wave function supported by a data set of unentangled product states. We show that this condenses down to a compact and expressive parametric form, with a variational flexibility shown to be competitive or surpassing established alternatives. The connections of the state to its roots as a Bayesian inference machine as well as matrix product states, also allow for efficient deterministic training of global states from small training data with enhanced generalization, including on application to frustrated spin physics.
AB - We introduce the quantum Gaussian process state, motivated via a statistical inference for the wave function supported by a data set of unentangled product states. We show that this condenses down to a compact and expressive parametric form, with a variational flexibility shown to be competitive or surpassing established alternatives. The connections of the state to its roots as a Bayesian inference machine as well as matrix product states, also allow for efficient deterministic training of global states from small training data with enhanced generalization, including on application to frustrated spin physics.
UR - http://www.scopus.com/inward/record.url?scp=85131868045&partnerID=8YFLogxK
U2 - 10.1103/PhysRevResearch.4.023126
DO - 10.1103/PhysRevResearch.4.023126
M3 - Article
AN - SCOPUS:85131868045
SN - 2643-1564
VL - 4
JO - Physical Review Research
JF - Physical Review Research
IS - 2
M1 - 023126
ER -