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Related Experiment Videos

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

Long Cao1, Liwei Ge1, Daochi Zhang2

  • 1Hefei National Research Center for Physical Sciences at the Microscale, University of Science and Technology of China, Hefei, Anhui 230026, China.

The Journal of Chemical Physics
|May 15, 2026
PubMed
Summary
This summary is machine-generated.

Physics-informed neural networks (PINNs) integrated with neural quantum states offer a novel method for simulating open quantum systems. This approach accurately models quantum dissipative dynamics, especially at high temperatures, overcoming computational challenges.

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Area of Science:

  • Quantum mechanics
  • Computational physics
  • Machine learning in physics

Background:

  • Simulating open quantum systems is computationally demanding.
  • Conventional variational methods often rely on the expensive time-dependent variational principle.
  • Developing efficient numerical methods for quantum dynamics is crucial.

Purpose of the Study:

  • To introduce a new computational framework for simulating open quantum system dynamics.
  • To integrate physics-informed neural networks (PINNs) into the neural quantum state (NQS) approach.
  • To bypass the computational cost associated with traditional variational methods.

Main Methods:

  • The proposed PINN-DQME method utilizes time-encoded neural networks.
  • A time-domain decomposition strategy is employed to represent system evolution.
  • The method models dynamics governed by the dissipaton-embedded quantum master equation (DQME).

Main Results:

  • The PINN-DQME method was implemented and validated using the single-impurity Anderson model.
  • Results were benchmarked against the numerically exact hierarchical equations of motion.
  • High accuracy was achieved for quantum dissipative dynamics at high temperatures (weak non-Markovian effects).

Conclusions:

  • The PINN-DQME method provides an accurate approach for simulating quantum dissipative dynamics under specific conditions.
  • Challenges with error accumulation were observed for strongly non-Markovian dynamics at low temperatures.
  • Future work should focus on refining PINN applications for complex quantum dynamical systems.