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

arXiv: https://arxiv.org/abs/2607.06760 · HTML: https://mlsanity.com/paper/2607.06760/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 31 references that are in the map.
- First posted on arXiv 7 Jul 2026.

## Builds on (most cited first, 6 of 6)

- [Quantum Annealing Applied to De-Conflicting Optimal Trajectories for Air Traffic Management](https://mlsanity.com/paper/1711.04889/) (2017)
- [Benchmarking Quantum Reinforcement Learning](https://mlsanity.com/paper/2501.15893/) (2025)
- [Enhancing Multiple Object Tracking Accuracy via Quantum Annealing](https://mlsanity.com/paper/2403.18908/) (2024)
- [QANTIS: A Hardware-Validated Quantum Platform for POMDP Planning and Multi-Target Data Association](https://mlsanity.com/paper/2603.00785/) (2026)
- [Confidence Intervals for Rate Estimation with Importance Sampling in Autonomous Vehicle Evaluation](https://mlsanity.com/paper/2604.03827/) (2026)
- [Dequantizing Short-Path Quantum Algorithms](https://mlsanity.com/paper/2604.12131/) (2026)

## Abstract (arXiv)

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.