\textsc{MLQD}: a package for machine learning-based quantum dissipative dynamics

DOI10.1016/j.cpc.2023.108940arXiv2303.01264OpenAlexW4386913726MaRDI QIDQ6086773

Pavlo O. Dral, Arif Ullah

Publication date: 10 November 2023

Published in: Computer Physics Communications (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2303.01264

zbMATH Keywords

open quantum systemsmachine learningquantum dissipative dynamicsFMO complexspin-Boson modelexciton energy transfer

Mathematics Subject Classification ID

Computer science (68-XX) Statistical mechanics, structure of matter (82-XX)

Cites Work

• Many regression algorithms, one unified model: a review
• Multilayer feedforward networks are universal approximators
• Numerical path integral techniques for long time dynamics of quantum dissipative systems
• Matérn Cross-Covariance Functions for Multivariate Random Fields
• On Quantum Theory of Transport Phenomena
• Approximation by superpositions of a sigmoidal function