用神经网络直接模拟量子系统演化算符，突破传统方法局限。
Neural Quantum Propagators for Driven-Dissipative Quantum Dynamics
- 用神经网络直接逼近时间演化算符，而非波函数或密度矩阵
- 可处理任意初态、不同外场，训练时长仅需短时间窗口
- 模型可迁移至不同哈密顿量系统，适用于强激光驱动开放系统
强激光驱动的开放量子系统动力学描述极为复杂，需求解高度耦合的运动方程。尽管机器学习已成功用于单个量子态的时间演化模拟，但对随时间变化的算符（可作用于多种状态）的近似仍基本未被探索。本文提出驱动型神经量子传播子（NQP），一种通用神经网络框架，通过近似传播子而非波函数或密度矩阵来求解驱动-耗散量子动力学。NQP 可处理任意初始量子态，适应多种外部场，并能模拟长时间演化，即使训练时仅使用较短时间窗口。此外，通过合理配置外部场，训练好的 NQP 可迁移至由不同哈密顿量支配的系统。我们通过自旋-玻色子模型和三态跃迁伽马模型验证了该方法的有效性。
原文摘要 · Abstract (English)
Describing the dynamics of strong-laser driven open quantum systems is a very challenging task that requires the solution of highly involved equations of motion. While machine learning techniques are being applied with some success to simulate the time evolution of individual quantum states, their use to approximate time-dependent operators (that can evolve various states) remains largely unexplored. In this work, we develop driven neural quantum propagators (NQP), a universal neural network framework that solves driven-dissipative quantum dynamics by approximating propagators rather than wavefunctions or density matrices. NQP can handle arbitrary initial quantum states, adapt to various external fields, and simulate long-time dynamics, even when trained on far shorter time windows. Furthermore, by appropriately configuring the external fields, our trained NQP can be transferred to systems governed by different Hamiltonians. We demonstrate the effectiveness of our approach by studying the spin-boson and the three-state transition Gamma models.
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