Learning thermodynamic master equations for open quantum systems

TL;DR

This paper introduces a thermodynamically consistent, data-driven model for open quantum systems that estimates Hamiltonians and environmental couplings, validated on synthetic and experimental data.

Contribution

It presents a novel nonlinear, physically grounded machine learning approach for characterizing open quantum systems, enhancing interpretability and physical consistency.

Findings

Model accurately estimates system Hamiltonians and couplings.

Validated on synthetic two- and three-level data.

Successfully applied to experimental quantum device data.

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.