用机器学习加速量子系统校准，决策速度提升70倍以上。
SymQNet: Amortized Acquisition for Low-Latency Adaptive Hamiltonian Learning
- 训练神经网络预先学习实验选择策略，线上推理极快
- 五比特时决策延迟降低47.1倍，十二比特仅需1.02秒
- 适合需要频繁快速校准的量子计算任务
自适应哈密顿量学习是校准和表征量子设备的核心。在自适应控制器中，选择下一个实验本身就是一个计算过程。每次后验更新后，贝叶斯设计规则都需要重新计算，这一过程可能耗时数秒。在数百次测量中，这些延迟累积成显著的实时开销。我们提出 SymQNet，一种用于低延迟自适应哈密顿量学习的可摊销强化学习方法。SymQNet 离线学习一个依赖后验的实验选择策略，线上仅需一次快速前向传播即可完成决策，同时保留贝叶斯后验反馈。在横向场伊辛模型基准测试中，SymQNet 显著降低了采集延迟：相较于有界费舍尔信息搜索和有界两步贝叶斯主动学习（BALD），五量子比特下分别降低47.1倍和72.6倍；十二量子比特下，完整模拟步骤仅需1.02秒，而有界两步BALD需13.27秒。结果表明，学习型采集策略可使自适应哈密顿量学习适用于重复性的低延迟应用场景。
原文摘要 · Abstract (English)
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 47.1x and 72.6x relative to these online baselines; at twelve qubits, full simulated steps take 1.02 seconds for SymQNet versus 13.27 seconds for bounded two-step BALD. Overall, we show that learned acquisition can make adaptive Hamiltonian learning practical for repeated low-latency workloads.
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