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

arXiv: https://arxiv.org/abs/2603.00785 · HTML: https://mlsanity.com/paper/2603.00785/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 36 references that are in the map.
- First posted on arXiv 28 Feb 2026.

## Builds on (most cited first, 5 of 5)

- [DESPOT: Online POMDP Planning with Regularization](https://mlsanity.com/paper/1609.03250/) (2016)
- [Quantum Annealing Applied to De-Conflicting Optimal Trajectories for Air Traffic Management](https://mlsanity.com/paper/1711.04889/) (2017)
- [Enhancing Multiple Object Tracking Accuracy via Quantum Annealing](https://mlsanity.com/paper/2403.18908/) (2024)
- [Quantum POMDPs](https://mlsanity.com/paper/1406.2858/) (2014)
- [Quantum Bayesian Networks Can Speed up Reinforcement Learning in Partially Observable Environments](https://mlsanity.com/paper/2507.18606/) (2025)

## Built on by (most cited first, 1 of 1)

- [QANTIS: Hardware-Calibrated Sequential POMDP Belief Updates on IBM Heron](https://mlsanity.com/paper/2607.06760/) (2026)

## Abstract (arXiv)

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 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 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×; 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 ≈ 100 and harmful above ISA gtrsim 1,000; FPC-QAOA is meaningful at ≤ 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.