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MLQD: A package for machine learning-based quantum dissipative dynamics
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DOI：10.1016/j.cpc.2023.108940.png)
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Abstract
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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). Program summary Program Title: MLQD CPC Library link to program files: https://doi .org /10 .17632 /yxp37csy5x .1 Developer's repository link: https://github .com /Arif -PhyChem /MLQD Code Ocean capsule: https://codeocean .com /capsule /5563143 /tree Licensing provisions: Apache Software License 2.0 Programming language: Python 3.0 Supplementary material: Jupyter Notebook-based tutorials External routines/libraries: Tensorflow, Scikit-learn, Hyperopt, Matplotlib, MLatom Nature of problem: Fast propagation of quantum dissipative dynamics with machine learning approaches. Solution method: We have developed MLQD as a comprehensive framework that streamlines and supports the implementation of our recently published machine learning-based approaches for efficient propagation of quantum dissipative dynamics. This framework encompasses: (1) the recursive dynamics with kernel ridge regression (KRR) method, as well as the non-recursive approaches utilizing convolutional neural networks (CNN), namely (2) artificial intelligence-based quantum dynamics (AIQD), and (3) oneshot trajectory learning (OSTL). Additional comments including restrictions and unusual features:
Keywords:
Quantum dissipative dynamics
Open quantum systems
Machine learning
FMO complex
Exciton energy transfer
Spin-boson model
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