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arXiv 2103.14490quant-ph
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用数据驱动的分析探测非马尔可夫量子动力学：超越“黑箱”机器学习模型

Probing non-Markovian quantum dynamics with data-driven analysis: Beyond "black-box" machine learning models

I. A. Luchnikov, E. O. Kiktenko, M. A. Gavreev, H. Ouerdane, S. N. Filippov, A. K. Fedorov

发表信息：
Phys. Rev. Research 4, 043002 (2022)
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摘要：

精确理解量子系统的环境对其动力学的影响是开放量子系统理论的核心，对于可控大规模量子系统的进一步发展至关重要。然而，现有的方法来考虑复杂系统-环境相互作用的记忆效应的存在下，要么是基于启发式和过于简化的原则，或引起计算困难。在实践中，人们可以利用现有的实验数据，用通常更简单的数据驱动分析来取代第一原理模拟。受数据分析和机器学习最新进展的启发，我们提出了一种数据驱动的方法来分析开放量子系统的非马尔可夫动力学。我们的方法允许，一方面，捕获开放量子系统的最重要的特征，如环境的有效维数和联合系统-环境量子动力学的频谱，另一方面，重建非马尔可夫量子动力学的预测模型，并对测量的量子轨迹进行去噪。我们证明了所提出的方法的性能与各种模型的开放量子系统，包括一个量子位耦合有限的环境，自旋玻色子模型，和阻尼Jaynes-Cummings模型。

英文摘要：

A precise understanding of the influence of a quantum system's environment on its dynamics, which is at the heart of the theory of open quantum systems, is crucial for further progress in the development of controllable large-scale quantum systems. However, existing approaches to account for complex system-environment interaction in the presence of memory effects are either based on heuristic and oversimplified principles or give rise to computational difficulties. In practice, one can leverage on available experimental data and replace first-principles simulations with a data-driven analysis that is often much simpler. Inspired by recent advances in data analysis and machine learning, we propose a data-driven approach to the analysis of the non-Markovian dynamics of open quantum systems. Our method allows, on the one hand, capturing the most important characteristics of open quantum systems such as the effective dimension of the environment and the spectrum of the joint system-environment quantum dynamics, and, on the other hand, reconstructing a predictive model of non-Markovian quantum dynamics, and denoising the measured quantum trajectories. We demonstrate the performance of the proposed approach with various models of open quantum systems, including a qubit coupled with a finite environment, a spin-boson model, and the damped Jaynes-Cummings model.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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