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

2026-07-07

AI Summary

This work addresses the challenge of maintaining stable belief states for decision-making in partially observable environments under the constraints of current quantum hardware, which struggles to support multi-step belief updates without degrading downstream planning performance. The authors propose a hardware-calibrated quantum belief update primitive that treats the quantum processor as a service module, accepting prior beliefs and observation models as inputs. By integrating boundary-aware BIQAE and rare-event scanning, the method estimates the evidence term and returns an accurate posterior distribution. For the first time, stable posterior propagation across sequential time steps is demonstrated on real IBM Heron hardware, augmented with full-step Fixed-Point Amplitude Amplification (FPAA) to ensure precision. Experiments on 8- to 32-step Tiger problems show that the hardware-generated posteriors consistently match exact Bayesian posteriors, yielding identical policy decisions and confirming the approach’s reusability and effectiveness.

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. 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. 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. 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.

Problem

• POMDP
• quantum hardware
• belief update
• partial observability
• posterior calibration

Innovation

• quantum belief update
• hardware-calibrated POMDP
• fixed-point amplitude amplification
• BIQAE
• rare-event evidence estimation