MLQD: A package for machine learning-based quantum dissipative dynamics

21 April 2023, Version 2
This content is an early or alternative research output and has not been peer-reviewed by Cambridge University Press at the time of posting.


Machine learning has emerged as a promising paradigm to study the quantum dissipative dynamics of open quantum systems. To facilitate the use of our recently published ML-based approaches for quantum dissipative dynamics, here we present an open-source Python package MLQD (, which currently supports the three ML-based quantum dynamics approaches: (1) the recursive dynamics with kernel ridge regression (KRR) method, (2) the non-recursive artificial-intelligence-based quantum dynamics (AIQD) approach and (3) the blazingly fast one-shot trajectory learning (OSTL) approach, where both AIQD and OSTL use the convolutional neural networks (CNN). This paper describes the features of the MLQD package, the technical details, optimization of hyperparameters, visualization of results, and the demonstration of the MLQD's applicability for two widely studied systems, namely the spin-boson model and the Fenna--Matthews--Olson (FMO) complex. To make MLQD more user-friendly and accessible, we have made it available on the Python Package Index (PyPi) platform and it can be installed via pip install mlqd. In addition, it is also available on the XACS cloud computing platform ( via the interface to the MLATOM package (


Machine Learning
Quantum Dissipative Dynamics
FMO Complex
Spin-boson Model
Open Quantum Systems
Kernel Ridge Regression
Neural Networks

Supplementary materials

Supporting Information
This is supporting information for our main text "MLQD: A package for machine learning-based quantum dissipative dynamics

Supplementary weblinks


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