MLQD: A package for machine learning-based quantum dissipative dynamics
arXiv:2303.01264 · doi:10.1016/j.cpc.2023.108940
Abstract
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).
References in corpus (36)
- The density-matrix renormalization group
- QuTiP: An open-source Python framework for the dynamics of open quantum systems
- Environment-Assisted Quantum Walks in Photosynthetic Energy Transfer
- Environment-Assisted Quantum Transport
- Maintaining coherence in Quantum Computers
- Realization of Three-Qubit Quantum Error Correction with Superconducting Circuits
- Keldysh Field Theory for Driven Open Quantum Systems
- Efficient simulation of strong system-environment interactions
- Real-time dynamics in Quantum Impurity Systems: A Time-dependent Numerical Renormalization Group Approach
- Exact mapping between system-reservoir quantum models and semi-infinite discrete chains using orthogonal polynomials
- Neural-Network Approach to Dissipative Quantum Many-Body Dynamics
- Exact c-number Representation of Non-Markovian Quantum Dissipation
- Non-Markovian Dynamical Maps: Numerical Processing of Open Quantum Trajectories
- Real-Time Diagrammatic Monte Carlo for Nonequilibrium Quantum Transport
- A hybrid stochastic hierarchy equations of motion approach to treat the low temperature dynamics of non-Markovian open quantum systems
- Machine Learning Exciton Dynamics
- Spin-mapping approach for nonadiabatic molecular dynamics
- The Ginzburg-Landau theory in application
- Non-Markovian Dissipative Semiclassical Dynamics
- Kondo effect of impurity moments in d-wave superconductors: Quantum phase transition and spectral properties
- Modelling Non-Markovian Quantum Processes with Recurrent Neural Networks
- Unified Formulation of Phase Space Mapping Approaches for Nonadiabatic Quantum Dynamics
- Open Quantum Systems for Quarkonia
- Driven dissipative dynamics and topology of quantum impurity systems
- Exact propagation of open quantum systems in a system-reservoir context
- Stochastic Equation of Motion Approach to Fermionic Dissipative Dynamics. I. Formalism
- Partition-free approach to open quantum systems in harmonic environments: an exact stochastic Liouville equation
- A quantum-classical decomposition of Gaussian quantum environments: a stochastic pseudomode model
- Learning quantum dynamics with latent neural ODEs
- Fewest-Switches Surface Hopping with Long Short-Term Memory Networks
- Probing non-Markovian quantum dynamics with data-driven analysis: Beyond "black-box" machine learning models
- Green's functions perspective on nonequilibrium thermodynamics of open quantum systems strongly coupled to baths
- Stochastic Equation of Motion Approach to Fermionic Dissipative Dynamics. II. Numerical Implementation
- A Variance Reduction Technique for the Stochastic Liouville-von Neuman Equation
- Machine learning for excitation energy transfer dynamics
- QD3SET-1: A Database with Quantum Dissipative Dynamics Data Sets