无需假设的开放量子系统动力学在 situ 学习
Ansatz-Free Learning of Lindbladian Dynamics In Situ
- ETH Zürich（苏黎世联邦理工学院）
- Harvard University（哈佛大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出无需假设的样本高效协议，用于学习稀疏的李纳布拉德生成器，以表征开放量子系统动力学。
AI中文摘要：
对开放量子系统在微观相互作用和误差机制层面的动力学进行表征，对于校准量子硬件、设计鲁棒的模拟协议以及开发定制化的误差校正方法至关重要。在马尔可夫噪声/耗散条件下，一种自然的表征方法是识别完全的李纳布拉德生成器，该生成器导致相干（哈密顿量）和耗散动力学。以往从动态数据中学习李纳布拉德生成器的协议假定了预定义的相互作用结构，当相关的噪声通道或控制缺陷在事先未知时，这会变得限制性。在本文中，我们提出了首个样本效率高的协议，用于学习稀疏的李纳布拉德生成器，而无需假设任何先验结构或局部性。我们的协议无需辅助粒子，仅使用产品态准备和泡利基测量，并实现了接近最优的时间分辨率，使其与近期实验能力兼容。最终的样本复杂度取决于线性系统的条件数，我们实证发现对于广泛物理动机的模型，其条件数是适度的。共同，这提供了一条系统的方法，用于可扩展的开放系统量子动力学表征，尤其是在感兴趣的误差机制未知的情况下。
英文摘要：
Characterizing the dynamics of open quantum systems at the level of microscopic interactions and error mechanisms is essential for calibrating quantum hardware, designing robust simulation protocols, and developing tailored error-correction methods. Under Markovian noise/dissipation, a natural characterization approach is to identify the full Lindbladian generator that gives rise to both coherent (Hamiltonian) and dissipative dynamics. Prior protocols for learning Lindbladians from dynamical data assumed pre-specified interaction structure, which can be restrictive when the relevant noise channels or control imperfections are not known in advance. In this paper, we present a sample-efficient protocol for learning sparse Lindbladians without assuming any a priori structure. Our protocol is ancilla-free, uses only product-state preparations and Pauli-basis measurements, admits provably stable coefficient reconstruction, and achieves near-optimal time resolution, making it compatible with near-term experimental capabilities. Together, this provides a systematic route to scalable characterization of open-system quantum dynamics, especially in settings where the error mechanisms of interest are unknown.
