用多任务强化学习自动优化量子控制脉冲与时间，提升实际设备中的控制鲁棒性。
Adaptive Reinforcement Learning for Robust Open Quantum System Control: A Multi-Task Framework with Temporal Optimization
- 多任务SAC框架同步学习最优脉冲序列、作用时间与分段数
- 在51种哈密顿量上实现超99%保真度的状态转移，噪声下仍稳定
- 适合需高鲁棒性的量子硬件控制研究者，尤其关注实用化系统
我们提出一种多任务软演员-评论家（SAC）强化学习框架，用于在不同哈密顿量下的开放量子系统控制，能够同时学习最优脉冲序列、演化时间T及控制脉冲段数N。在51种哈密顿量变体上的实验表明，该模型生成的控制脉冲可在环境噪声下将系统从初始态精准驱动至目标态，保真度极高，为适用于真实噪声量子设备的通用量子控制奠定基础。通过逐步扩展训练哈密顿量集合，我们验证了单个多任务模型能否在未见过的同空间哈密顿量上完成状态转移任务。此外，鲁棒性误码率（RIM）分析显示，SAC训练策略相比GRAPE优化控制，在脉冲幅度扰动和退相干率变化下表现出更强的鲁棒性。
原文摘要 · Abstract (English)
We present a Multi-task Soft Actor-Critic (SAC) Reinforcement Learning framework designed for open-system quantum control across diverse Hamiltonians, which learns optimal pulse sequences while simultaneously discovering problem-specific evolution time T and number of control pulse segments N. Experimental results across 51 Hamiltonian variations demonstrate that the multi-task SAC model is able to generate control pulses that can drive a system, under environment noise, from its initial state to its target state with high fidelities, establishing essential foundations for universal quantum control applicable to realistic noisy quantum devices. Through progressive expansion of the training Hamiltonian set, we investigate if a single multi-task model trained using a given number of sample Hamiltonians can successfully accomplish state-transfer tasks for Hamiltonians drawn from the same Hamiltonian space but not encountered during training. In addition, our Robustness Infidelity Measure (RIM) analysis reveals that SAC trained policies exhibit superior robustness to pulse amplitude perturbations and decoherence rate variations compared to GRAPE-optimized controls.
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