AI 中文总结
该研究提出RL-Trotter强化学习框架，将量子模拟的近似误差视为校正资源，通过优化长时间演化发现自校正序列，提升精度并降低测量开销，可推广迁移至更大系统。
AI 中文摘要
长时间的精确数字量子模拟受限于近似模拟固有的误差累积。本文提出RL-Trotter，一种强化学习框架，将不可避免的近似误差视为误差校正的资源，而非仅需抑制的缺陷。研究表明，来自守恒律的低维信息（如能量和能量方差）提供了充足的学习信号来引导智能体，该智能体无需访问目标波函数即可学习调整单一标量——下一个Trotter步长。通过优化整个长时间演化而非单个步骤，RL-Trotter发现自校正序列，其中后续误差补偿早期累积的误差，提升长时间动力学的精度。所学策略本质上对测量噪声具有鲁棒性，大幅降低测量开销；还可推广至先前未见过的物理相似初态，并从可经典模拟的小系统迁移至大一个数量级的系统。这实现了基于经典预训练后直接部署或在量子硬件上有限微调的实用方案。本研究为量子算法确立了更广阔视角：近似演化中的误差可被编排为精确且资源高效量子动力学的资源。
英文摘要
Accurate digital quantum simulation at long times is limited by the accumulation of errors inherent to approximate simulation. Here we introduce RL-Trotter, a reinforcement-learning framework that treats unavoidable approximation errors as resources for error correction rather than merely imperfections to suppress. We show that low-dimensional information from conservation laws, such as the energy and energy variance, provides a sufficient learning signal to guide the agent, which learns to adapt a single scalar---the next Trotter step size---without access to the target wave function. By optimizing the entire long-time evolution rather than individual steps, RL-Trotter discovers self-correcting sequences in which later errors compensate for those accumulated earlier, increasing the accuracy of the long-time dynamics. The learned policies are intrinsically robust to measurement noise, substantially reducing measurement overhead. They also generalize to previously unseen, physically similar initial states and transfer from small, classically simulable systems to systems an order of magnitude larger. This enables a practical protocol based on classical pretraining followed by direct deployment or limited fine-tuning on quantum hardware. Our results establish a broader perspective for quantum algorithms: errors in approximate evolution can be orchestrated into resources for accurate and resource-efficient quantum dynamics.
Comments12+12 pages, 6+6 figures
