Quantum SafeML is built on the concept of defending AI systems using quantum-enhanced data processing and post-quantum encryption techniques. Below is a step-by-step flow of how the system operates:
Convert to Quantum Dataset
Map classical features into a quantum-ready representation using encoding schemes like amplitude or angle encoding.
Train Quantum ML Model
Use quantum circuits (like QNNs or quantum kernels) to learn from encoded data.
Fit to Test Data
Apply trained quantum models to unseen data points.
Store Predictions
Securely store outputs using quantum-resistant encryption methods.
Create Comparison Sets
Form sets of similar samples for adversarial defense and confidence validation.
Apply Quantum Distance Metrics
Compute fidelity or trace distance to evaluate consistency and noise resilience.
Check Confidence Threshold
Compare prediction certainty against a predefined quantum-aware threshold.
Notify Human Agent
If confidence is low, flag the sample for human review or intervention.