Neural Network Approach for Non-Markovian Dissipative Dynamics of Many-Body Open Quantum Systems

Many-body open quantum systems archical equations of motion (HEOM) [20-24], dissipaton (OQS) have gained wide attention and found applications equation of motion (DEOM) [25, 26], and pseudomode in various fields including physics, chemistry, materials theory [27-30]; and stochastic methods, including quantum science, and life sciences. These applications cover state diffusion (QSD) [31-35], stochastic equation various fields such as coherent energy transfer in biological of motion (SEOM) [36-39], hierarchy of stochastic pure photosystems [1-3], charge transfer in molecular states (HOPS) [40], and quantum Monte Carlo (QMC) aggregates [4, 5], electron transport in single molecular [41, 42]. However, the computational cost of these methods junctions [6, 7], multidimensional coherent spectroscopy grows rapidly as the complexity of OQS increases. of condensed phase materials [8, 9], correlated quantum Here, complexity refers to the size of the system, the matter for quantum information and computation strength of many-body correlations, and the level of non- [10, 11], and precise measurement and control of local Markovianity.