Learning thermodynamic master equations for open quantum systems

• URL: http://arxiv.org/abs/2506.01882v1
• Date: Mon, 02 Jun 2025 17:11:16 GMT
• Title: Learning thermodynamic master equations for open quantum systems
• Authors: Peter Sentz, Stanley Nicholson, Yujin Cho, Sohail Reddy, Brendan Keith, Stefanie Günther,
• Abstract summary: We present a data-driven model for open quantum systems that includes learnable, thermodynamically consistent terms.<n>The trained model is interpretable, as it directly estimates the system Hamiltonian and linear components of coupling to the environment.
• Score: 0.1884913108327873
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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