发表机构
North Carolina State University（北卡罗来纳州立大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种原始时间序列Transformer，直接处理泡利期望值轨迹，联合学习未知哈密顿参数和多个耗散通道，在测量噪声下稳健重建Lindblad动力学，无需手工特征工程。
AI 中文摘要
Lindblad主方程是描述开放量子系统非幺正演化的标准框架，其中环境相互作用引起耗散和退相干。当系统哈密顿量和耗散率部分未知或显式依赖于时间时，传统的解析反演和系统辨识技术变得难以处理。近期研究表明，基于Transformer的模型可以从可观测量时间序列中推断未知耗散率，但这些方法通常依赖于在理想化和高度受限条件下手工设计的统计特征。在此，我们推进了这一范式，引入了一种原始时间序列Transformer，直接输入泡利期望值$\langle\sigma_x(t)\rangle$、$\langle\sigma_y(t)\rangle$和$\langle\sigma_z(t)\rangle$的完整轨迹，从而充分利用自注意力机制进行时间建模。该架构进一步扩展，以联合学习未知哈密顿参数、处理多个耗散通道，并在现实测量噪声下稳健运行。在所有测试场景中，该模型均实现了一致的高重建精度，同时消除了手工特征工程。这为现实开放量子系统中的量子环境感知提供了一个可扩展、稳健且通用的框架。
英文摘要
The Lindblad master equation is the standard framework for describing the non-unitary evolution of open quantum systems, where environmental interactions induce dissipation and decoherence. When both the system Hamiltonian and the dissipation rates are partially unknown or explicitly time-dependent, traditional analytical inversion and system-identification techniques become intractable. Recent works have demonstrated that Transformer-based models can infer unknown dissipation rates from observable time series, yet these approaches typically rely on hand-crafted statistical features under idealized and highly restricted conditions. Here we advance the paradigm by introducing a raw time-series Transformer that directly ingests the full trajectories of Pauli expectation values $\langleσ_x(t)\rangle$, $\langleσ_y(t)\rangle$, and $\langleσ_z(t)\rangle$, thereby fully exploiting the self-attention mechanism for temporal modeling. The architecture is further extended to jointly learn unknown Hamiltonian parameters, handle multiple dissipation channels, and operate robustly under realistic measurement noise. Across all tested scenarios the model achieves consistently high reconstruction accuracy while eliminating manual feature engineering. This provides a scalable, robust, and versatile framework for quantum environment sensing in realistic open quantum systems.
