Quantum Sensor Fusion (QSFM) — a public, non-export-controlled, simulation repository demonstrating anomaly detection and fusion across heterogeneous signals with entropy/coherence features and learning-based policy shaping.
⚠️ Safety & Scope: This repo is simulation-only, no operational deployment code, no vendor data, and no export-controlled content. It is intended for research discussion and portfolio review.
The accelerating complexity of transnational smuggling operations driven by sophisticated concealment techniques, synthetic drug proliferation, and exploitative use of global trade routes demands a new class of interdiction technology. Existing inspection methods, while effective in isolated cases, are often constrained by limited sensor modalities, high false-negative rates, and manual bottlenecks that allow illicit cargo to bypass detection. To counter this, the Department of Homeland Security (DHS) requires a fusion-driven solution capable of synthesizing disparate signals, learning from evolving threats, and scaling across diverse port environments without disrupting legitimate commerce. QSFM-Fusion (Quantum Sensor Fusion for Maritime Interdiction) addresses this need by integrating quantum-informed signal processing, real-time machine learning, and multi-domain sensor arrays into a single operational platform. Unlike legacy inspection systems reliant on visual or chemical triggers, QSFM-Fusion dynamically evaluates entropy patterns, manifest discrepancies, and signal anomalies across magnetic, gravitational, radar, and THz domains. This allows the system to detect subtle deviations associated with black-market logistics such as spoofed weights, chemical shadowing, or modular trafficking compartments while adapting to new smuggling tactics through embedded Q-learning algorithms. This document presents the operational design, integration roadmap, simulation validation, and measurable impact projections for QSFM-Fusion. Built to align with DHS strategic priorities including automation, evidence integrity, and cross-border intelligence collaboration the system offers a low-friction deployment path via existing infrastructure (ACE, AIS) and edge-compatible platforms (Python, C++, FPGA). What follows is a detailed technical and operational breakdown of the QSFM-Fusion system, highlighting its readiness for pilot deployment and its transformative potential in securing U.S. ports and maritime borders.
- Critical infrastructure & border security: Detect and triage anomalous patterns across simulated sensor streams.
- Supply-chain integrity: Flag manifest/routing anomalies in a controlled sandbox.
- Hardware-aware ideas: Entropy/coherence features and neuromorphic/learning hooks align with modern quantum-inspired sensing research.
- Reproducibility: Seeded runs, deterministic pipelines, and simple artifacts for evaluation.
python -m venv .venv && source .venv/bin/activate
pip install -r requirements.txt # create if not present; see minimal list below
python QSFM_FusionSimulation.py # runs a short seeded simulationnumpy
scipy
pandas
matplotlib
- QSFM_FusionSimulation.py — main simulation harness (seeded).
- QSFM_Fusion.py / QSFM_Main.py — fusion and orchestration helpers.
- QSFM TestSuite.py — smoke tests / assertions (rename to
QSFM_TestSuite.pyfor consistent naming). - QSFM Entropy Demo.py — small demo of entropy/coherence features (rename to
QSFM_Entropy_Demo.py). - QSFM_ASIC_Fusion.v — verilog sketch (illustrative only).
- QSFM_Integration.cpp / QSFM_Scanner.cpp — C++ stubs for future acceleration.
- build_script.sh — helper script for local builds.
- SimulationResults.txt — sample output.
src/qsfm_fusion/
__init__.py
fusion_sim.py
scripts/
demo.py
tests/
test_smoke.py
.github/workflows/
ci.yml
results/ (generated)
Add a tiny demo script (example):
# scripts/demo.py
import numpy as np, json, os
import matplotlib.pyplot as plt
os.makedirs("results", exist_ok=True)
rng = np.random.default_rng(42)
x = rng.normal(0, 1, 200)
score = np.clip(np.abs(x)/3, 0, 1)
json.dump({"seed":42,"mean_score":float(score.mean())}, open("results/demo_metrics.json","w"))
plt.plot(score); plt.title("QSFM demo score (seed=42)"); plt.savefig("results/demo_plot.png", dpi=160)
print("Wrote results/demo_metrics.json & results/demo_plot.png").
├── QSFM_FusionSimulation.py
├── QSFM_Fusion.py
├── QSFM_Main.py
├── QSFM TestSuite.py
├── QSFM Entropy Demo.py
├── QSFM_ASIC_Fusion.v
├── QSFM_Integration.cpp
├── QSFM_Scanner.cpp
├── build_script.sh
├── SimulationResults.txt
└── README.md
MIT (recommended for public research code). Add a LICENSE file if not already present.
quantum-computing · sensor-fusion · anomaly-detection · critical-infrastructure · python · research
Issues and PRs welcome. For introductions/collaboration, connect on LinkedIn.
Tip: rename files to avoid spaces (e.g., QSFM_Entropy_Demo.py), standardize to snake_case in future updates, and add CI (ruff/black/pytest) for a professional touch.
