Simulating non-Markovian open quantum dynamics by exploiting physics-informed neural network

• URL: http://arxiv.org/abs/2603.08081v1
• Date: Mon, 09 Mar 2026 08:21:59 GMT
• Title: Simulating non-Markovian open quantum dynamics by exploiting physics-informed neural network
• Abstract summary: This work integrates the physics-informed neural network (PINN) approach into the neural quantum state framework to simulate open quantum system dynamics.<n>The proposed PINN-DQME method employs time-encoded neural networks within a time-domain decomposition strategy to represent the evolution governed by the dissipaton-embedded quantum master equation (DQME)<n>The PINN-DQME method demonstrates high accuracy in simulating quantum dissipative dynamics at high temperatures, where non-Markovian effects are weak.
• Score: 10.049898018843265
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This work integrates the physics-informed neural network (PINN) approach into the neural quantum state framework to simulate open quantum system dynamics, to circumvent the computationally expensive time-dependent variational principle required in conventional variational methods. The proposed PINN-DQME method employs time-encoded neural networks within a time-domain decomposition strategy to represent the evolution governed by the dissipaton-embedded quantum master equation (DQME). We implement and validate this approach in the single-impurity Anderson model, benchmarking the PINN-DQME results against the numerically exact hierarchical equations of motion. The PINN-DQME method demonstrates high accuracy in simulating quantum dissipative dynamics at high temperatures, where non-Markovian effects are weak. However, for strongly non-Markovian dynamics at low temperatures, it encounters challenges with error accumulation during time propagation, highlighting an area for future refinement in applying PINNs to complex quantum dynamical settings.

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