Applied quantum kernels for anomaly detection. Low-data anomaly detection on manifold-structured telemetry, benchmarking entanglement kernels vs classical baselines with geometric diagnostics.

Adaptive quantum networks in practice: superposed graph topologies and operator-space spatialization, with reproducible hardware-relevant demos and figures.

Controlled interpolation between classical and quantum learning. Binarized Quantum Neural Network benchmark harness for systematic sweeping a quantumness parameter to map learning phase transitions.

Frontier AI research through rigorous systems-level experimentation — applying physics-inspired experimental methods and information-theoretic tools to measure scaling, robustness, and alignment properties of frontier AI systems.

🛰 Enhance quantum telemetry analysis by detecting anomalies in quantum-kernel geometry with this reproducible framework for insightful research.