These Results Are Real.

Constant-Depth, Noise-Resilient Target-State Preparation

Preparing a specific target quantum state with high fidelity is a core primitive for quantum algorithms — and it degrades quickly as hardware noise grows. Our method prepares the target state at constant circuit depth — a shallow circuit that stays the same size regardless of the target, limiting its exposure to decoherence. On real quantum hardware at a short coherence time of T1 = 5 µs, it reaches 0.97 fidelity versus 0.41 for a standard preparation.

Coherent Superposition QRAM Query on Real Hardware

A quantum RAM fans out classical data into a superposed address register and answers a query over that superposition. Verified on IBM quantum hardware, our query holds 0.84 fidelity against the standard bucket-brigade design's 0.29 on the same hardware — using roughly 95% fewer entangling gates.

Verified on IBM quantum hardware.

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 — Memory Pool Allocator (GPU + CPU)

Systems-level memory pool allocator with per-device slab buckets, golden-ratio block growth, and NVML-aware throttling. Measured on NVIDIA H100: 2,706× faster than cudaMalloc on 1 GB block reuse; 1,500× average across 8× H100s in parallel (driver-lock contention amplifies the advantage). CPU pool: 22.6× faster than libc malloc on 1 GB block reuse. Targets reuse-heavy GPU workloads — LLM KV caches, attention scratch buffers, gradient buffers, embedding tables. 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 behind our quantum-hardware results 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.