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Learning thermodynamic master equations for open quantum systems

arXiv 2506.01882 ↗First posted 2 Jun 2025quant-ph cs.LGcited by 0

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Abstract (arXiv)

The characterization of Hamiltonians and other components of open quantum dynamical systems plays a crucial role in quantum computing and other applications. Scientific machine learning techniques have been applied to this problem in a variety of ways, including by modeling with deep neural networks. However, the majority of mathematical models describing open quantum systems are linear, and the natural nonlinearities in learnable models have not been incorporated using physical principles. We present a data-driven model for open quantum systems that includes learnable, thermodynamically consistent terms. The trained model is interpretable, as it directly estimates the system Hamiltonian and linear components of coupling to the environment. We validate the model on synthetic two and three-level data, as well as experimental two-level data collected from a quantum device at Lawrence Livermore National Laboratory.

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References · 14

  1. 2022Scientific Machine Learning through Physics-Informed Neural Networks: Where we are and What's nextcited by 608
  2. 2020Universal Differential Equations for Scientific Machine Learningcited by 353
  3. 2021Machine learning structure preserving brackets for forecasting irreversible processescited by 52
  4. 2021GFINNs: GENERIC Formalism Informed Neural Networks for Deterministic and Stochastic Dynamical Systemscited by 49
  5. 2020Deep learning of thermodynamics-aware reduced-order models from datacited by 38
  6. 2022Learning to predict arbitrary quantum processescited by 35
  7. 2022Thermodynamics-informed graph neural networkscited by 33
  8. 2023Reversible and irreversible bracket-based dynamics for deep graph neural networkscited by 26
  9. 2022Partial Differential Equations Meet Deep Neural Networks: A Surveycited by 25
  10. 2022Physics-Guided, Physics-Informed, and Physics-Encoded Neural Networks in Scientific Computingcited by 24
  11. 2023Improved machine learning algorithm for predicting ground state propertiescited by 22
  12. 2022Port-metriplectic neural networks: thermodynamics-informed machine learning of complex physical systemscited by 11
2 more
  1. 2022Thermodynamics of learning physical phenomenacited by 10
  2. 2024Solving The Quantum Many-Body Hamiltonian Learning Problem with Neural Differential Equationscited by 7

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