This repository implements a Hybrid Partition-and-Routing Heuristic that combines classical machine learning with quantum-inspired optimization (QUBO) to coordinate drone fleets for industrial gas leak detection.
The framework optimizes for Time-to-Detection (TTD), significantly outperforming standard coverage-based methods in emergency response scenarios.
The system follows a modular hybrid pipeline:
- Quantum-Inspired Classifier: Uses Angle Encoding and variational circuits to identify leak severity from raw sensor data.
- Quantum Voronoi Partitioning: Formulates a Quadratic Unconstrained Binary Optimization (QUBO) problem to assign drone deployment sites based on the severity heatmap.
- Classical Path Generation: Utilizes a 2-Opt TSP heuristic to plan the exact flight path for each drone within its assigned partition.
This project utilizes a two-tier validation strategy to transition from theoretical optimization to industrial application.
Initial benchmarking was performed using a synthetic "Hazard Topology" in a simulated
- Purpose: To isolate the performance of the QUBO solver and verify convergence across different fleet sizes (5–20 drones).
- Generation: Multi-modal Gaussian distributions create "ground truth" leak intensities.
Final validation uses the GPLA-12 Acoustic Signal Dataset to provide realistic hazard inputs.
- Source: Collected by researchers at the University of Adelaide. [Reference: Li & Yao, 2021]
- Composition: 684 samples across 12 classes simulating various pipeline pressures and leakage types.
- Role: Acoustic signals are processed through the hybrid ensemble to generate the severity scores that drive the spatial optimization.
The framework was benchmarked against three classical baselines. Method 3 (M3) is a high-performance, intensity-weighted K-Means algorithm used to ensure a fair comparison with the proposed model.
| Metric | M1 (Random) | M2 (K-Means) | M3 (IW K-Means) | Hybrid Q-C (Ours) |
|---|---|---|---|---|
| Mean TTD (s) | 66.2s | 45.1s | 43.9s | 10.0s |
| High-Risk TTD (s) | 64.8s | 42.1s | 40.7s | 9.4s |
| Compute Time (s) | 0.02s | 1.80s | 2.67s | 0.21s |
| Speedup vs M3 | - | - | 1.0x | 12.7x |
Statistical Note: Results are averaged over numpy=42, tensorflow=42). The speedup is statistically significant (
The system provides several layers of visualization to interpret the optimization results:
- Hazard Heatmaps: Visualizes the density of the GPLA-12 derived leak spots.
- Voronoi Cells: Displays the QUBO-derived boundaries for drone responsibility.
- Mission Animation: A real-time
.gifshowing drones traversing optimized paths.
(Note: In the provided source code, animations are saved as drone_mission_animation.gif for mission debriefing.)
git clone https://github.com/yourusername/hybrid-gas-leakage-detection.git
cd hybrid-gas-leakage-detectionpip install -r requirements.txtThe full end-to-end pipeline (data processing → optimization → animation) is contained in the main script:
python hybrid_gas_leakage_system.pyIf you use the GPLA-12 data, please cite:
Li, J., & Yao, L. (2021). GPLA-12: An Acoustic Signal Dataset of Gas Pipeline Leakage. arXiv preprint arXiv:2106.10277. Source
This project is an algorithmic proof-of-concept. All "Quantum" components are currently executed via classical simulation of quantum heuristics. It is intended for academic research and is not certified for deployment in safety-critical industrial infrastructure.
This project is licensed under the MIT License. See the LICENSE file for details.