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

2026-02-28

AI Summary

This work addresses the high computational complexity of partially observable Markov decision processes (POMDPs) and multi-target data association (MTDA) under uncertainty in autonomous navigation by proposing the first modular hybrid quantum-classical closed-loop inference platform. The framework integrates quantum belief updating, MTDA formulated as a QUBO problem, and composable error mitigation strategies, enabling the first eight-step closed-loop execution of the Tiger POMDP on an IBM Heron superconducting quantum processor. Leveraging Grover amplitude amplification, the approach enhances the probability of rare observations by a factor of 5.1 (from 0.179 to 0.907) with a Hellinger distance of only 0.0015. Combined with BIQAE, FPC-QAOA, and zero-noise extrapolation (ZNE), the study empirically delineates the operational boundaries and algorithmic scalability achievable on current NISQ devices.

Technology Category

• Machine Learning: Quantum Machine Learning
• Planning, Routing, and Scheduling: Planning with Markov Models (MDPs, POMDPs)
• Search and Optimization: Learning to Search

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Responsible Web: Machine-in-the-loop, human agency and autonomy

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 $\mathcal{O}(P(e)^{-1})$ 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 $0.907$ ($5.1\times$; 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.

Problem

• POMDP
• multi-target data association
• belief update
• computational complexity
• autonomous navigation

Innovation

• Quantum Amplitude Amplification
• POMDP Planning
• Multi-Target Data Association
• QUBO Optimization
• NISQ Hardware Validation