基于物理信息神经网络的单比特门的演化级量子最优控制
Evolution-Level Quantum Optimal Control of Single-Qubit Gates with Physics-Informed Neural Networks
AI总结：
研究利用物理信息神经网络在演化层面进行单比特门设计，共同学习控制场等，改变优化对象，对旋转门和几何门优化效果良好，不仅能合成门，还使量子控制具备可读、可诊断和可局部优化等特性，有助于适应多种实验约束。
AI中文摘要：
量子门设计通常表示为脉冲优化，而实现一个门的物理对象是由脉冲产生的完全受控演化。本文使用物理信息神经网络在这个演化层面来表示单比特门设计：在布洛赫方程下共同学习控制场、布洛赫态轨迹和总持续时间。这将优化对象从脉冲幅度变为一个可微的物理过程，其结构可被检查和优化。对于旋转门，优化后的演化恢复了有界单比特控制预期的物理组织，无需规定脉冲假设或持续时间扫描。对于几何门，该表示识别出维持几何条件时的局部瓶颈，并将此诊断转化为反馈，在保持高保真度的同时减少残余路径误差。因此，物理信息学习不仅用于合成门，还使优化的量子控制在物理上可读、可诊断且可局部优化。这种过程层面的观点对于使门适应特定硬件、特定任务和局部变化的实验约束可能特别有用。
英文摘要：
Quantum gate design is often represented as pulse optimization, although the physical object that implements a gate is the full controlled evolution generated by the pulse. Here we use physics-informed neural networks to represent single-qubit gate design at this evolution level: the control fields, the Bloch-state trajectories, and the total duration are learned together under the Bloch equation. This changes the optimized object from pulse amplitudes to a differentiable physical process whose structure can be inspected and refined. For rotation gates, the optimized evolutions recover the physical organization expected for bounded single-qubit control, with no prescribed pulse ansatz or duration scan. For a geometric gate, the representation identifies localized bottlenecks in maintaining the geometric condition and turns this diagnosis into feedback, reducing the residual path error while preserving high fidelity. Thus physics-informed learning is used not only to synthesize gates, but also to make optimized quantum controls physically readable, diagnosable, and locally refinable. This process-level view may be especially useful for adapting gates to hardware-specific, task-specific, and locally varying experimental constraints.
