In partially observable autonomous systems, decisions rely on beliefs rather than raw sensor events. QANTIS treats the quantum processor as a calibrated belief-update service: 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 investigates whether that service can be reused across a sequential Tiger POMDP horizon on present IBM Heron hardware without corrupting the planner-facing posterior.
We respond 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, and 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, while the 20-step and 32-step controls remain within 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.
Blogger's Review: The QANTIS research showcases the potential of quantum computing in decision support systems, particularly in handling complex belief updates. By comparing different amplification techniques, the study provides profound insights into how quantum processors can operate effectively in practical applications, especially in addressing uncertainty and rare events. This research lays an important reference framework for future quantum algorithm design.