QANTIS: Hardware-Calibrated Sequential POMDP Belief Updates on IBM Heron
Quick Answer
The QANTIS framework leverages IBM's Heron quantum processor for hardware-calibrated belief updates in sequential POMDPs, demonstrating that all-step fixed-point amplification preserves decision-making accuracy across multiple steps.
Quick Take
The QANTIS framework leverages IBM's Heron quantum processor for hardware-calibrated belief updates in sequential POMDPs, demonstrating that all-step fixed-point amplification preserves decision-making accuracy across multiple steps. A controlled study shows that the hardware posterior aligns with the exact Bayes posterior, ensuring consistent action selection in autonomous systems under partial observability.
Key Points
- QANTIS uses IBM Heron for calibrated belief updates in autonomous systems.
- All-step fixed-point amplification maintains posterior accuracy across 8 to 32 steps.
- Hardware posterior matches exact Bayes posterior in decision-making checks.
- Boundary-aware BIQAE stabilizes amplitude estimation near critical values.
- Study focuses on hardware case rather than end-to-end autonomy claims.
Paper Resources
📖 Reader Mode
~2 min readAbstract: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.
| Comments: | 10 pages, 6 figures |
| Subjects: | Artificial Intelligence (cs.AI); Quantum Physics (quant-ph) |
| Cite as: | arXiv:2607.06760 [cs.AI] |
| (or arXiv:2607.06760v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2607.06760 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Bayram Yuksel Eker [view email]
[v1]
Tue, 7 Jul 2026 19:43:32 UTC (42 KB)
— Originally published at arxiv.org
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