Shipped: Quantum Circuit Anomaly Detection — Adversarial Security

An open-source quantum computing security proof of concept investigating how anomaly detection can identify simulated adversarial disturbances in quantum circuits.

The framework uses Google's Cirq and PennyLane to construct and simulate quantum circuits, encode MNIST image data into quantum rotations, and introduce controlled perturbations through randomized qubit rotations. By analyzing quantum measurement outcomes and applying configurable anomaly thresholds, the system explores how abnormal circuit behavior can be detected.

Implemented in Python, the project includes quantum circuit visualizations, experimental comparisons, and adjustable parameters for noise probability, circuit configuration, and detection thresholds.

The research explores the intersection of quantum computing, cybersecurity, and anomaly detection, providing a foundation for future work in quantum machine learning, adversarial robustness, and quantum circuit security.

This is an educational and experimental simulation, not a validated quantum hardware intrusion detection system.