[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124667-en":3,"doc-seo-124667-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124667,962075006959,"Anda","https://ap-avatar.wpscdn.com/avatar/e0002397efbe92a78e?_k=1776741047341049297",6,"Technology","MLQD - A package for machine learning-based quantum dissipative dynamics","Machine learning is leveraged to study quantum dissipative dynamics of open quantum systems, where environment-induced dephasing and dissipation are essential. The document introduces MLQD, an open-source Python package designed to support three ML-based quantum dynamics approaches: recursive dynamics with kernel ridge regression, AIQD based on convolutional neural networks, and one-shot trajectory learning. It details package features, implementation and optimization of hyperparameters, result visualization, and demonstrations on the spin-boson model and the Fenna–Matthews–Olson (FMO) complex, with installation via pip and access through XACS cloud.","arXiv :2303 .01264v2 [physics .chem-ph] 20 Sep 2023  \nMLQD: A package for machine learning-based quantum dissipative dynamics  \nArif Ullah1 and Pavlo O. Dral2  \n1) School of Physics and Optoelectronic Engineering, Anhui University, Hefei 230601, China  \n2) State Key Laboratory of Physical Chemistry of Solid Surfaces, College of Chemistry and Chemical Engineering, Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, and Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen University, Xiamen 361005,  \nChina  \n(*Electronic mail: [arif@ahu.edu.cn](arif@ahu.edu.cn))  \n(*Electronic mail: [dral@xmu.edu.cn](dral@xmu.edu.cn))  \n(Dated: 21 September 2023)  \nMachine 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](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](https://XACScloud.com))  \nvia the interface to the MLATOM package ([http://MLatom.com](http://MLatom.com)).  \nProgram summary  \nProgram Title: MLQD  \nDeveloper’s repository link: [https://github.com/Arif-PhyChem/MLQD](https://github.com/Arif-PhyChem/MLQD)  \nCode Ocean capsule: [https://codeocean.com/capsule/5563143/tree](https://codeocean.com/capsule/5563143/tree)  \n[Licensing provisions](Licensing provisions: Apache Software License 2.0)[: Apache Software License 2.0](Licensing provisions: Apache Software License 2.0)  \n[Programming language](Programming language: Python 3.0)[: Python 3.0](Programming language: Python 3.0)  \nSupplementary material: Jupyter Notebook-based tutorials  \nExternal routines/libraries:: Tensorflow, Scikit-learn, Hyperopt, Matplotlib, MLatom  \nNature of problem: Fast propagation of quantum dissipative dynamics with machine learning approaches.  \nSolution 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 intelligencebased quantum dynamics (AIQD), and (3) one-shot trajectory learning (OSTL) .  \nUnusual or notable features:  \n1. Users can train a machine learning (ML) model following one of the ML-based approaches: KRR, AIQD and OSTL.  \n2. Users have the option to propagate dynamics with the existing trained ML models.  \n3. MLQD also provides the transformation of trajectories into the training data.  \n4. MLQD also supports hyperparameter optimization using MLATOM’s grid search functionality for KRR and Bayesian methods with Tree-structured Parzen Estimator (TPE) for CNN models via the HYPEROPT package.  \n5. MLQD also facilitates the visualization of resul","cbCain7peZ1ZU7HR","https://ap.wps.com/l/cbCain7peZ1ZU7HR","pdf",1561805,1,24,"English","en",105,"# Introduction\n## Background: Open quantum systems and challenges\n## MLQD package overview\n## Supported ML-based quantum dynamics approaches\n# Program summary\n## Licensing and programming language\n## Solution method and framework scope\n## Notable features\n## Future outlook","[{\"question\":\"What problem does MLQD address in quantum dissipative dynamics?\",\"answer\":\"MLQD targets fast propagation of quantum dissipative dynamics for open quantum systems using machine learning approaches, avoiding the intractability of exact solutions for large environments.\"},{\"question\":\"Which three ML-based approaches does the MLQD package currently support?\",\"answer\":\"It supports (1) recursive dynamics with kernel ridge regression (KRR), (2) AIQD using convolutional neural networks (CNN), and (3) one-shot trajectory learning (OSTL), where AIQD and OSTL rely on CNNs.\"},{\"question\":\"How can users install and run MLQD?\",\"answer\":\"MLQD is available as a pip package installable via “pip install mlqd” and can also be accessed through the XACS cloud computing platform via an interface to the MLATOM package.\"}]","MLQD - A package for machine learning-based quantum dissipative dynamics | PDF",1785893789,60,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"mlqd-a-package-for-machine-learning-based-quantum-dissipative-dynamics","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/technology/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/mlqd-a-package-for-machine-learning-based-quantum-dissipative-dynamics/124667/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does MLQD address in quantum dissipative dynamics?","Question",{"text":75,"@type":76},"MLQD targets fast propagation of quantum dissipative dynamics for open quantum systems using machine learning approaches, avoiding the intractability of exact solutions for large environments.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which three ML-based approaches does the MLQD package currently support?",{"text":80,"@type":76},"It supports (1) recursive dynamics with kernel ridge regression (KRR), (2) AIQD using convolutional neural networks (CNN), and (3) one-shot trajectory learning (OSTL), where AIQD and OSTL rely on CNNs.",{"name":82,"@type":73,"acceptedAnswer":83},"How can users install and run MLQD?",{"text":84,"@type":76},"MLQD is available as a pip package installable via “pip install mlqd” and can also be accessed through the XACS cloud computing platform via an interface to the MLATOM package.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,109,112,117,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":29,"slug":108},5,"Comic","comic",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":110,"slug":111},50,"technology",{"id":113,"doc_module":4,"doc_module_name":46,"category_name":114,"show_sort_weight":115,"slug":116},7,"Healthcare",40,"healthcare",{"id":118,"doc_module":4,"doc_module_name":46,"category_name":119,"show_sort_weight":120,"slug":121},8,"Research & Report",30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]