Simulating Non-Markovian Open Quantum Dynamics with Neural Quantum States
This work addresses the computational scaling problem for simulating dissipative quantum systems, offering a method that could enable studies of previously intractable systems, though it appears incremental as it builds on existing neural quantum state approaches.
The paper tackled the challenge of simulating non-Markovian open quantum dynamics by developing a neural quantum states framework that encodes environmental memory in dissipatons, achieving comparable accuracy to exact methods while improving scalability and interpretability.
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.