Hybrid quantum-classical facial recognition achieved 97% accuracy with an 8-qubit VQC (72 parameters), outperforming classical FaceNet (85.1%) while training 267× faster on identical hardware. Real-world deployment achieved 0.2-0.5s inference time on CPU.
