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

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 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 (https://XACScloud.com) via the interface to the MLatom package (http://MLatom.com).

机器学习已成为研究开放量子系统量子耗散动力学的极具前景的研究范式。为便于使用我们近期发表的基于机器学习的量子耗散动力学方法，本文开源了一款Python软件包MLQD（https://github.com/Arif-PhyChem/MLQD），目前该工具支持三类基于机器学习的量子动力学方法：(1) 基于核岭回归（Kernel Ridge Regression, KRR）的递归动力学方法；(2) 基于人工智能的非递归量子动力学（AIQD）方法；(3) 极速单轨迹学习（One-shot Trajectory Learning, OSTL）方法，其中AIQD与OSTL均采用卷积神经网络（Convolutional Neural Networks, CNN）。本文详细介绍了MLQD软件包的功能特性、技术细节、超参数优化方案、结果可视化方法，以及MLQD在两类经典研究体系——自旋玻色子模型与Fenna–Matthews–Olson（FMO）复合物——上的适用性验证。为提升MLQD的易用性与可及性，我们已将其上传至Python软件包索引（Python Package Index, PyPI）平台，用户可通过`pip install mlqd`命令完成安装。此外，用户还可通过MLatom软件包（http://MLatom.com）提供的接口，在XACS云计算平台（https://XACScloud.com）上使用MLQD。