AI 中文总结
研究针对非马尔可夫开放量子系统模拟与控制难题，扩展分叉PINN框架，通过选择性梯度流解耦优化目标，实现联合优化。在两比特海森堡XXX模型上验证，该方法能再现非马尔可夫动力学特征，在状态制备任务中保真度更高，脉冲更平滑，提供统一可微范式。
AI 中文摘要
物理信息神经网络（PINNs）为统一量子系统的模拟和控制提供了途径，而在传统策略中这两个任务通常是解耦的。然而，大多数工作仍局限于马尔可夫环境。当应用于非马尔可夫系统时，标准PINN架构因耦合微分方程产生的多目标优化冲突而无法可靠收敛。为解决这一基本限制，我们通过纳入专用控制分支扩展了之前提出的分叉PINN（FPINN）。通过选择性梯度流在梯度层面解耦优化目标，我们的方法将之前难以处理的多任务优化转变为条件良好的优化，使模拟和控制能够联合优化而不妥协。在两比特海森堡XXX模型上的数值模拟证实，我们的框架忠实地再现了非马尔可夫动力学的特征，包括退相干和信息回流。以同一模型上的状态制备任务为例，我们的FPINN比梯度上升脉冲工程、截断随机基和标准PINN实现了更高的保真度，随着环境变得更具耗散性和更接近马尔可夫性，优势更加明显。生成的脉冲也明显更平滑，有利于实验实现。我们的框架为开放量子系统的模拟和控制提供了一个统一的、端到端可微的范式，对量子计算、模拟和控制具有潜在意义。
英文摘要
Physics-informed neural networks (PINNs) provide a pathway to reunify the simulation and control of quantum systems, in which these two tasks are typically decoupled in traditional strategies. However, most work remains confined to Markovian environments. When applied to non-Markovian systems, standard PINN architectures fail to converge reliably due to multi-objective optimization conflicts arising from the coupled differential equations. To address this fundamental limitation, we extend our previously proposed forked PINN (FPINN) by incorporating a dedicated control branch. By decoupling the optimization objectives at the gradient level via selective gradient flow, our method turns a previously intractable multi-task optimization into a well-conditioned one, allowing simulation and control to be optimized jointly without compromise. Numerical simulations on a two-qubit Heisenberg XXX model confirm that our framework faithfully reproduces the features of non-Markovian dynamics, including decoherence and information backflow. Taking a state-preparation task on the same model as an example, our FPINN achieves higher fidelity than gradient ascent pulse engineering, chopped random basis, and standard PINNs, with the advantage becoming more pronounced as the environment becomes more dissipative and more Markovian. The generated pulses are also noticeably smoother, which is advantageous for experimental implementation. Our framework thus provides a unified, end-to-end differentiable paradigm for simulation and control of open quantum systems, with potential implications for quantum computing, simulation, and control.
