Simulating Non-Markovian Open Quantum Dynamics with Neural Quantum States

TL;DR

This paper introduces a neural quantum state framework with dissipatons to efficiently simulate complex non-Markovian open quantum systems, improving scalability and interpretability over existing methods.

Contribution

The authors develop the NQS-DQME framework that encodes environmental memory via dissipatons, enabling compact and scalable simulation of non-Markovian quantum dynamics.

Findings

NQS-DQME maintains accuracy comparable to hierarchical equations of motion.

The framework enhances scalability for large quantum systems.

It offers improved interpretability of non-Markovian effects.

Abstract

Reducing computational scaling for simulating non-Markovian dissipative dynamics using artificial neural networks is both a major focus and formidable challenge in open quantum systems. To enable neural quantum states (NQSs), we encode environmental memory in dissipatons (quasiparticles with characteristic lifetimes), yielding the dissipaton-embedded quantum master equation (DQME). The resulting NQS-DQME framework achieves compact representation of many-body correlations and non-Markovian memory. Benchmarking against numerically exact hierarchical equations of motion confirms NQS-DQME maintains comparable accuracy while enhancing scalability and interpretability. This methodology opens new paths to explore non-Markovian open quantum dynamics in previously intractable systems.