Classical Implementations

GPU-Native Vector Search — Faster Than FAISS

VOIS is our patented GPU-native similarity search engine. Built on the same mathematical framework as our quantum algorithms. Benchmarked head-to-head against Meta's FAISS on the same machine, same dataset, same ground truth.

Head-to-Head at Matched Recall — SIFT-1M (1 Million Vectors)

Scaling — VOIS QPS at ~97% Recall

VOIS — Recall vs Throughput Sweep (1M Vectors)

FAISS Comparison Data — 1M Vectors

SIFT-1M dataset: 128 dimensions, 1,000 queries, K=10 nearest neighbors. All methods on the same RTX 4060 (8GB), same data, same ground truth. VOIS uses a single GPU. FAISS HNSW tested at 1 and 16 CPU threads. Best of 5 runs.

CUDA-Accelerated Computation — Framework-Derived Performance

A CUDA-accelerated computation library applying our patented mathematical framework to general-purpose GPU workloads. Drop-in acceleration for standard NVIDIA hardware through novel mathematical operations — not hardware tricks, but better math.

Knapsack Optimization — 100% Optimal, Up to 24x GPU Speedup

CUDA on RTX 4060 (8GB). 20 standard benchmark instances across all types (uncorrelated, weakly/strongly correlated, subset sum). CPU and GPU solve the identical dynamic programming problem — GPU values match CPU exactly on all 20 instances. Zero memory leaks. Average speedup: 14.6x.

60+ Identified Application Domains

Because the framework operates at a fundamental mathematical level, it applies across computational domains. We have identified over 60 specific application areas spanning search, security, optimization, signal processing, defense, energy, medical imaging, telecommunications, and autonomous systems. These are covered by our patent claims.

Detailed application analysis and domain-specific benchmarks available under NDA.