  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
(https://github.com/Arif-PhyChem/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 XACS cloud computing platform
(https://XACScloud.com) via the interface to the MLatom package
(http://MLatom.com).
