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|Title:||Kinetic Samplers for Neural Quantum States|
|Authors:||Bagrov, A. A.|
Iliasov, A. A.
|Publisher:||American Physical Society|
American Physical Society (APS)
|Citation:||Bagrov A. A. Kinetic Samplers for Neural Quantum States / A. A. Bagrov, A. A. Iliasov, T. Westerhout // Physical Review B. — 2021. — Vol. 104. — Iss. 10. — 104407.|
|Abstract:||Neural quantum states are a recently introduced class of variational many-body wave functions that are very flexible in approximating diverse quantum states. Optimization of an NQS ansatz requires sampling from the corresponding probability distribution defined by squared wave function amplitude. For this purpose, we propose to use kinetic sampling protocols and demonstrate that in many important cases such methods lead to much smaller autocorrelation times than the Metropolis-Hastings sampling algorithm while still allowing to easily implement lattice symmetries (unlike autoregressive models). We also use uniform manifold approximation and projection algorithm to construct two-dimensional isometric embedding of Markov chains and show that kinetic sampling helps attain a more homogeneous and ergodic coverage of the Hilbert space basis. © 2021 authors.|
AUTO REGRESSIVE MODELS
MANY BODY WAVE FUNCTIONS
|metadata.dc.description.sponsorship:||The authors thank Olle Eriksson, Mikhail Katsnelson, and Danny Thonig for useful discussions. The work of T.W. was supported by European Research Council via Synergy Grant 854843—FASTCORR. A.A.I. acknowledges financial support from Dutch Science Foundation NWO/FOM under Grant No. 16PR1024. A.A.B. acknowledges support from the Russian Science Foundation, Grant No. 18-12-00185. This work was partially supported by Knut and Alice Wallenberg Foundation through Grant No. 2018.0060. This work was carried out on the Dutch national e-infrastructure with the support of SURF Cooperative.|
|RSCF project card:||18-12-00185|
|Appears in Collections:||Научные публикации, проиндексированные в SCOPUS и WoS CC|
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