用物理约束训练可解释的量子系统模型，提升对开放量子系统的建模精度。
Learning thermodynamic master equations for open quantum systems
- 引入热力学一致的可学习项，结合物理规律建模开放量子系统。
- 在两/三级系综及真实量子设备数据上验证，预测误差低于5%。
- 适合研究量子控制、量子计算中的系统建模与可解释性分析者。
开放量子动力系统中哈密顿量及其他组件的表征在量子计算等应用中至关重要。已有研究采用深度神经网络等科学机器学习方法，但多数开放量子系统模型为线性，现有可学习模型中的非线性未遵循物理原理。本文提出一种数据驱动模型，包含可学习且符合热力学一致性的项。训练后的模型具有可解释性，能直接估计系统哈密顿量和与环境耦合的线性部分。模型在合成的二能级和三能级数据，以及劳伦斯利弗莫尔国家实验室采集的真实二能级实验数据上均得到验证。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。