QANTIS: Hardware-Calibrated Sequential POMDP Belief Updates on IBM Heron

QANTIS：基于 IBM Heron 硬件校准的序列化 POMDP 信念更新

Abstract: Autonomous systems under partial observability act on beliefs, not raw sensor events. QANTIS treats the quantum processor as a calibrated belief-update service in that loop: it receives a prior and an observation model, estimates the rare-event evidence term, and returns an ordinary posterior to a classical planner. 摘要： 在部分可观测环境下，自主系统是基于“信念”（beliefs）而非原始传感器事件进行决策的。QANTIS 将量子处理器视为该循环中的一种校准信念更新服务：它接收先验概率和观测模型，估计稀有事件的证据项，并将常规的后验概率返回给经典规划器。

This paper asks whether that service can be reused across a sequential Tiger POMDP horizon on present IBM Heron hardware without corrupting the planner-facing posterior. We answer with a controlled hardware case study rather than an end-to-end autonomy or wall-clock speedup claim. 本文探讨了在当前的 IBM Heron 硬件上，是否可以在序列化 Tiger POMDP（部分可观测马尔可夫决策过程）的时间跨度内重复使用该服务，且不破坏面向规划器的后验概率。我们通过受控的硬件案例研究来回答这一问题，而非宣称实现了端到端的自主性或实际运行时间的加速。

The study compares no amplification, guarded Grover amplification, and all-step fixed-point amplification on the same trajectory, then checks whether the returned posterior would change the downstream action. All-step FPAA preserves the Tiger posterior across the reported 8-step and 12-step primary runs, and the 20-step and 32-step controls remain inside the same operating band. 该研究在同一轨迹上比较了无放大、受保护的 Grover 放大以及全步定点放大（fixed-point amplification），并检查返回的后验概率是否会改变后续的决策动作。全步 FPAA 在报告的 8 步和 12 步主要运行中保持了 Tiger 后验概率的准确性，且 20 步和 32 步的对照组也保持在相同的运行区间内。

In every reported decision check, the hardware posterior and the exact Bayes posterior select the same immediate action. Boundary-aware BIQAE stabilizes amplitude estimation near zero and near one, while a rare-event sweep maps the logical sample-complexity envelope for one-in-a-million evidence. The result is an operating envelope for a hardware-calibrated belief-update primitive, not a standalone hardware-advantage claim. 在每一次报告的决策检查中，硬件计算出的后验概率与精确的贝叶斯后验概率选择了相同的即时动作。边界感知型 BIQAE（边界感知振幅估计）在接近 0 和 1 时稳定了振幅估计，而稀有事件扫描则映射了百万分之一证据下的逻辑样本复杂度包络。该研究的结果是为硬件校准的信念更新原语提供了一个运行包络，而非宣称实现了独立的硬件优势。