QANTIS: A Hardware-Validated Quantum Platform for POMDP Planning and Multi-Target Data Association

Autonomous navigation under uncertainty requires solving partially observable Markov decision processes (POMDPs) for planning and assigning sensor measurements to tracked targets--a task known as multi-target data association (MTDA). Both problems become computationally demanding at scale: belief conditioning costs mathcal{O}(P(e)^-1) per node under rare evidence, while MTDA is NP-hard. Quantum amplitude amplification can quadratically reduce the belief-update query cost to mathcal{O}(P(e)^-1/2), while QUBO reformulations expose MTDA to quantum and quantum-inspired optimisation heuristics. We present QANTIS, a modular platform that integrates quantum belief update (Grover amplitude amplification and BIQAE), QUBO-based data association via FPC-QAOA, and composable error mitigation, and we report a 45-experiment hardware study on three IBM Heron backends. On hardware, a single Grover iterate applied to a Tiger belief oracle amplifies a rare observation probability from 0.179 to 0.907 $5.1times$; ISA 18 while preserving the Bayesian posterior (Hellinger 0.0015), increasing usable-shot yield from 1,463 to 7,429. We interpret this as a hardware validation of the quadratic query-complexity mechanism at k=1 with posterior preservation, rather than a wall-clock advantage claim. We further demonstrate, to our knowledge, the first closed-loop hybrid quantum-classical Tiger POMDP on superconducting hardware $T=8$, max Hellinger below $0.015$, and empirically characterise NISQ feasibility boundaries: ZNE-based error mitigation is beneficial below ISA approx 100 and harmful above ISA gtrsim 1{,}000; FPC-QAOA is meaningful at leq 15 QUBO variables ISA $lesssim 450$. These results characterise practical operating regimes on current superconducting hardware rather than wall-clock quantum advantage at today's problem scales.