Quantum Autonomous
Navigation & Intelligence
QANTIS is a hardware-validated quantum platform for POMDP planning and multi-target data association. Validated across 45 experiments on IBM Heron QPUs, it demonstrates quadratic speedup in belief conditioning and solves NP-hard tracking problems via QAOA. A follow-on sequential belief-updating manuscript is currently under IEEE review.
Quantum research operations
Hardware-validated research needs infrastructure, experiment lineage, and publication-ready evidence.
QANTIS is presented with a professional research context: IBM Heron validation, sequential POMDP review work, and a clear path from experiment to product decision systems.
Research validation path
Live modelProblem
arXiv
Simulation
45 experiments
Hardware
IEEE review
Review
arXiv
Signal
arXiv
Signal
45 experiments
Signal
IEEE review
Community Edition
You are looking at the public, didactic surface of QANTIS.
This page describes the open-source Community Edition distributed under MIT — a clean, citable surface for researchers, students, and partners to integrate at the API level. The full Collaborator Edition (production-grade modules, hardened mitigation pipelines, internal experimental harness, additional applications including Quantum-Bio Intelligence and CRISPR) is maintained in a private workspace reserved for Neura Parse partners. Real benchmark runs, hardware campaign results, and superior performance figures are disclosed only through peer-reviewed publications and formal engagements.
Reviewers and programme committees evaluating QANTIS-related submissions can request artefact access through the corresponding author or directly through Neura Parse Ltd.
Decision Engine
Infer → Risk → Optimise → Verify.
QANTIS is built around an opinionated decision loop. Deterministic optimisers solve deterministic models. QANTIS optimises uncertain belief-to-action loops — the harder online problem in which noisy observations must become calibrated beliefs, calibrated risk estimates, feasible actions, and verifiable decisions under a fixed compute budget.
Turn noisy, partial, or rare observations into a calibrated posterior belief — the single source of truth that every downstream step depends on.
Grover-AA on POMDP belief
O(P(e)⁻¹) → O(P(e)⁻¹ᐟ²) quadratic speedup on belief conditioning
BIQAE
Boundary-aware Bayesian quantum amplitude estimation under bounded depth
Hellinger distance ≤ 0.0149
vs ideal distribution across T=8 hardware steps (Tiger POMDP)
Inputs
- Raw sensor stream
- Prior model
- Observation noise model
Outputs
- Posterior belief
- Confidence intervals
- Calibration diagnostics
Stack
Compiled through qmesh → ed25519-signed run manifest → offline-verifiable hash chain.
Click a step above to inspect inputs, outputs, and the techniques QANTIS uses at that stage.
Every QANTIS application — POMDP planning, multi-target tracking, sensor fusion, future Quantum-Bio modules — is a specialisation of this four-part loop. The Community Edition exposes a basic surface; the calibrated production-grade variant lives in the Collaborator Edition.
Editions
Community vs Collaborator — what's in each.
The public repository ships a clean, didactic surface intended for evaluation, citation, and integration testing. Production modules, hardened mitigation pipelines, and full benchmarks are reserved for the Collaborator Edition — accessed through formal engagement.
| Feature | Community Edition · public · MIT | Collaborator Edition · private · partners only |
|---|---|---|
| Backend abstraction layerframework | Public connectors | Hardened, multi-vendor, optimised |
| Configuration & reproducibilityframework | Included | Included |
| Error mitigation pipelineframework | Baseline (ZNE, Pauli twirling) | Full mitigation & calibration stack |
| Benchmarking infrastructureframework | Illustrative | Full experimental harness |
| Infer — calibrated beliefengine | Basic surface | Calibrated, production-grade |
| Risk — event & tail-riskengine | Basic surface | Calibrated, production-grade |
| Optimise — feasible decisionsengine | Basic surface | Calibrated, production-grade |
| Verify — trust & diagnosticsengine | Basic surface | Calibrated, production-grade |
| POMDP planning (Tiger reference)applications | Included | Included |
| Multi-Hypothesis Tracking (MHT)applications | Included | Included |
| Quantum-Bio Intelligenceapplications | Not included | Included |
| CRISPR moduleapplications | Not included | Included |
| Sensor fusion · adversarial robustness · mission orchestrationapplications | Not included | Included |
| Real hardware resultsops | Not published | Reserved for partners |
| Comparative benchmarks vs classical SOTAops | Not included | Included |
| Confidential datasets & mission profilesops | Not included | Included |
| Supportops | Community, best-effort | Dedicated engineering |
| Licenceops | MIT | Commercial / partner agreement |
Abstract
Research Summary
We introduce QANTIS (Quantum Autonomous Navigation, Tracking & Intelligence System), a hardware-validated quantum platform that addresses autonomous navigation under uncertainty using quantum methods. The framework targets two core challenges: POMDP planning under partial observability and NP-hard multi-target data association in tracking scenarios.
For POMDP planning, belief conditioning traditionally costs O(P(e)-1). QANTIS leverages quantum amplitude amplification through a Grover-based belief oracle to reduce this to O(P(e)-1/2), achieving a quadratic speedup. A single Grover iterate amplifies observation probability from 0.179 to 0.907 — a 5.1x improvement validated on real hardware. The first closed-loop hybrid quantum-classical Tiger POMDP is demonstrated on superconducting hardware over T=8 decision steps with a maximum Hellinger distance of just 0.0149.
For multi-target data association (MTDA), the NP-hard assignment problem is formulated as a QUBO and solved via QAOA with fixed-parameter circuits. Hardware experiments across 45 runs on three IBM Heron QPUs (ibm_torino, ibm_fez, ibm_marrakesh) establish NISQ feasibility boundaries: ZNE is beneficial below ~100 ISA gates and harmful above ~1000, while FPC-QAOA produces meaningful results at up to 15 QUBO variables.
Published Paper
arXiv publication remains the primary QANTIS paper.
The IEEE-review manuscript is a follow-on work. The original QANTIS arXiv paper stays listed here with its citation, authors, and public source links.
QANTIS: Quantum Autonomous Navigation, Tracking & Intelligence System
Hardware-validated quantum platform for POMDP planning and multi-target data association, validated across IBM Heron QPUs.
New Manuscript
Under IEEE review.
The follow-on QANTIS manuscript is listed here at a high level only while review is in progress. Full technical details will remain limited until the review process is complete.
Hardware-Validated Sequential POMDP Belief Updating on IBM Heron
Extends QANTIS from single-step belief inference toward sequential decision support under partial observability.
Key Contributions
What QANTIS delivers.
Grover-AA on POMDP Belief Oracle
Quantum amplitude amplification reduces belief conditioning cost from O(P(e)⁻¹) to O(P(e)⁻¹˲), quadratically accelerating observation updates in partially observable environments.
Closed-Loop Hybrid POMDP
First closed-loop hybrid quantum-classical Tiger POMDP executed on superconducting hardware (T=8 steps, maximum Hellinger distance 0.0149), proving real-time quantum decision-making viability.
FPC-QAOA for Multi-Target Data Association
NP-hard multi-target data association is cast as a QUBO and solved via QAOA with fixed-parameter circuits. Meaningful results demonstrated at up to 15 QUBO variables on NISQ hardware.
Composable Error Mitigation
Systematic NISQ feasibility boundary established: ZNE beneficial below ~100 ISA gates, harmful above ~1000. Composable mitigation pipeline validated across 45 experiments on 3 IBM Heron backends.
Hardware Results
Validated on real quantum hardware.
45 experiments executed across three IBM Heron backends — ibm_torino, ibm_fez, and ibm_marrakesh — with composable error mitigation.
Architecture
Three-package composable design.
QANTIS is structured as three composable Python packages — shared primitives, POMDP planning, and multi-hypothesis tracking — all released under Apache 2.0.
quantum-common
Shared utilities, circuit primitives, error mitigation (ZNE, Pauli twirling), and backend abstraction layer for IBM Qiskit Runtime.
quantum-pomdp
POMDP belief-state oracle construction, Grover amplitude amplification, closed-loop hybrid planning loop, and Tiger POMDP reference implementation.
quantum-mht
Multi-target data association via QUBO formulation, FPC-QAOA solver, classical MHT baseline, and cost-matrix construction for tracking scenarios.
Paper Details
Publication information.
Authors
Keywords
Hardware Backends
ibm_torinoIBM Heronibm_fezIBM Heronibm_marrakeshIBM HeronNISQ Boundaries
In the Neura Parse stack
QANTIS compiles through qmesh.
Hardware runs flow through qmesh, the modality-agnostic IR that handles backend abstraction, error mitigation, and ed25519-signed run manifests. The same provenance chain that satisfies regulated industries also gives reviewers and partners offline-verifiable hash chains for every reported result.