Adaptive Hamiltonian learning is central to calibrating and characterizing quantum devices. In an adaptive controller, choosing the next experiment is itself a computation. Bayesian design rules are recomputed after every posterior update, and that step can take seconds. Across hundreds of shots, those seconds become a significant wall-clock cost for adaptivity. We introduce SymQNet, an amortized reinforcement-learning approach for low-latency adaptive Hamiltonian learning. SymQNet learns a posterior-conditioned acquisition policy offline, then uses a fast policy forward pass online while retaining Bayesian posterior feedback. On transverse-field Ising benchmarks, SymQNet substantially reduces acquisition latency relative to bounded Fisher-information search and bounded two-step Bayesian active learning by disagreement (BALD). At five qubits, it reduces acquisition-only decision latency by $47.1\times$ and $72.6\times$ relative to these online baselines; at twelve qubits, full simulated steps take $1.02$ s for SymQNet versus $13.27$ s for bounded two-step BALD. Overall, we show that learned acquisition can make adaptive Hamiltonian learning practical for repeated low-latency workloads.

翻译：自适应哈密顿学习是标定与表征量子器件的核心环节。在自适应控制器中，选择下一次实验本身即构成一次计算。贝叶斯设计规则在每次后验更新后均需重新计算，此步骤耗时可达数秒。在数百次射击场景下，这些秒级延迟将累积为自适应过程中显著的时钟周期成本。我们提出SymQNet——一种面向低延迟自适应哈密顿学习的摊销式强化学习方法。SymQNet在离线阶段学习基于后验条件的采集策略，在线阶段则通过快速策略前向传播实现操作，同时保留贝叶斯后验反馈机制。在横向场伊辛基准测试中，相较于有界费舍尔信息搜索与有界两步贝叶斯主动学习（BALD），SymQNet显著降低了采集延迟。针对五量子位系统，其采集决策延迟较上述在线基线方法分别降低47.1倍与72.6倍；在十二量子位系统中，完整模拟步骤耗时SymQNet为1.02秒，而有界两步BALD需13.27秒。总体而言，我们证实学习型采集策略可使自适应哈密顿学习在重复性低延迟工作负载下具备实际可行性。