Digital Identity
ReQAgnIze
ReQAgnIze applies quantum-enhanced algorithms to facial recognition and deduplication, matching and de-duplicating identities in real time across large biometric databases — on standard CPU hardware, and secured with post-quantum cryptography.
CPU-only · No GPU needed
Use Cases
- →BFSI: Login auth, KYC, transaction authorization
- →Enterprise: Access control, attendance, secure VPN
- →Government: Offline identity, welfare authentication
Benchmarks
| Feature | Classical AI (SOTA) | Quanverge Hybrid |
|---|---|---|
| Training Images | Millions | Few Hundred |
| Hardware | GPU Required | CPU Only |
| Training Speed (50k images, 100 epochs) | 69 Hours | 55 Minutes |
| Security | AES/TLS Only | Quantum-Secure (PQC) |
| Deployment | Weeks | Hours |
Estimate your ReQAgnIze training run
Classical training
Measured: 69 hours for 50,000 images over 100 epochs.
With ReQAgnIze, on a 16 GB CPU
Measured: 3,288.97 s for the same workload, with no GPU at any point.
Time saved
A 76x speedup, on hardware costing a fraction of a GPU cluster.
Scaled linearly from two measured runs on the same 50,000-image, 100-epoch workload, so treat it as an indication rather than a guarantee: throughput varies with image resolution, model size and available memory, and very large datasets may not stay in RAM.