QANTIS: A Hardware-Validated Quantum Platform for POMDP Planning and Multi-Target Data Association
Bayram Y\"uksel Eker, Suayb S. Arslan, \"Ozg\"ur Nazl{\i}, Mustafa Serhat Demirgil, Furkan Delig\"oz

TL;DR
This paper introduces QANTIS, a modular quantum platform that validates quantum algorithms for POMDP planning and multi-target data association on IBM superconducting hardware, demonstrating hardware feasibility and practical operating regimes.
Contribution
QANTIS integrates quantum belief updates and QUBO-based data association, providing the first hardware validation of quadratic query complexity and hybrid quantum-classical POMDP implementation.
Findings
Quantum belief amplification increases rare observation probability from 0.179 to 0.907.
Hardware validation of quadratic query complexity at k=1 with posterior preservation.
First hybrid quantum-classical POMDP on superconducting hardware with practical error mitigation insights.
Abstract
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 per node under rare evidence, while MTDA is NP-hard. Quantum amplitude amplification can quadratically reduce the belief-update query cost to , 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…
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Taxonomy
TopicsQuantum Computing Algorithms and Architecture · AI-based Problem Solving and Planning · Logic, Reasoning, and Knowledge
