These Results Are Real.

Beating the 30-Year Standard on Real Hardware

Grover's algorithm has been the gold standard for quantum search since 1996. On real quantum hardware, our algorithm was tested across every possible target in 3, 4, and 5-qubit systems — winning all 32 out of 32 hardware test cases. At 5 qubits, Grover's circuit is destroyed by noise (7.3% avg) while our method achieves 97.3%.

Noise Crossover — Why This Matters

Current quantum hardware operates at 0.5–1.5% gate error rates. Our method's advantage grows as noise increases — the exact opposite of every other quantum algorithm.

5-Qubit Noise Model — Grover Collapses, Ours Holds

At 5 qubits, the scaling advantage becomes dramatic. Grover's circuit complexity explodes while our method stays simple. As noise increases, Grover drops below random chance while our accuracy barely moves.

At 1% error (typical for current quantum hardware), Grover's 5-qubit accuracy is 11.8% — barely above the 3.1% random baseline. Our method: 85.6%. The advantage grows with qubit count because Grover's complexity scales exponentially while ours does not.

A Novel Method for Non-Destructive Quantum Observation

Standard quantum measurement collapses the state being measured. Our patented observation method extracts correlated state information while the original qubit retains 93.9% of its distribution — verified on real quantum hardware across 150+ experiments with 4,000 shots each. The observer was independently confirmed to perform real quantum measurement through a controlled verification test.

Observer Copy vs Original Under Noise

All experiments: 4,000 shots on current-generation quantum processor. Independently verifiable via permanent job IDs.

The Same Math Works on Classical Hardware

The framework behind our quantum hardware results also produces measurable advantages on standard GPUs. This is not quantum simulation — it is the same underlying mathematics applied to classical computation.

VOIS — O(1) Similarity Search

VOIS is our patented GPU-native similarity search engine. Where conventional methods scale with dataset size, VOIS achieves constant-time retrieval regardless of how many vectors are indexed. Head-to-head against Meta's FAISS on the same hardware, same data, same ground truth:

PX Compute — CUDA Acceleration Library

Framework-derived CUDA library achieving 100% optimal solutions with up to 24x GPU speedup on standard optimization benchmarks. Verified on 20 standard knapsack instances — every GPU result matches CPU exactly. Full benchmarks →

Novel Mathematics, Not Incremental Improvement

Our results are not optimizations of existing methods. They are products of a fundamentally different mathematical framework — one that was discovered by exploring approaches that established thinking considers impossible or unnecessary. The framework is protected by U.S. patent applications covering the core mathematics, algorithms, hardware implementations, and application methods.

The framework applies across domains because it operates at a mathematical level below the specific application. The same structures that beat Grover's algorithm on quantum hardware also accelerate similarity search on GPUs, optimize routing problems, and factor large numbers. This is not a coincidence — it is a property of the mathematics itself.

Select results and demonstrations available under NDA for qualified research partners and investors.