A quantum-native architecture achieving 720x parameter compression.
GPT-4 intelligence running locally with no cloud and no data exposure.
The Problem
The industry's answer to every problem is more parameters, more compute, more cost. That path is breaking.
Training frontier models exceeds $100M. Inference costs grow faster than revenue.
Cloud dependency blocks compliance. Regulatory frameworks increasingly demand local data processing.
Classical models approximate discrete logic through over-parameterization. Hallucinations are baked into the architecture.
Regulated industries are blocked from cloud AI by compliance mandates. They represent the largest unserved segment in enterprise AI.
Technology
A fundamentally different approach to AI that uses the mathematics of quantum physics as architectural structure.
SU(2) wave-interference provides universal structure for exact algebraic reasoning. The physics does the heavy lifting that classical models need millions of parameters to approximate.
UQT scales as O(L × log V) versus classical O(d²). This is a complexity class difference, not a constant-factor improvement. The gap widens at scale.
The model converges to mathematically exact solutions with zero variance. Not stochastic approximation, but complete algebraic resolution of the underlying structure.
720x parameter compression, demonstrated
100% accuracy with 551 parameters vs. ~400,000 for classical transformers on structured algebraic tasks.
Results
UQT has been validated on three algebraic domains and real quantum hardware.
Demonstrated
Algebraic Reasoning
0x
parameter compression
100% accuracy with 551–690 parameters vs. ~400,000 for classical transformers. Validated on modular addition (Z₁₁), modular multiplication (Z*₁₁), and permutation composition (S₄).
96.7%
accuracy on IBM Heron r2 quantum hardware, no error correction
0
variance at convergence. Deterministic crystallization, not stochastic approximation.
3
algebraic domains validated: modular addition, multiplication, and permutation composition
Live Demo
UQT generating text and reconstructing sequences locally, with fewer than 700 parameters.
Character-level Shakespeare · under 10 qubits
Zero-loss reconstruction · Z₁₁ modular arithmetic
Team
The inventors of UQT, now building the company to bring it to market.
Co-Founder / CEO
Directed UQT research strategy and now leading commercialization
$20M+ in competitively awarded research funding (NSF, FHWA, USDOT, DOE, GM, SAE International)
Tenured UIUC Professor with 10+ years leading research programs at UIUC and Texas A&M, from proposal through delivery
Co-Founder / CTO
Conceived the UQT architecture and built it end-to-end, from JAX/PyTorch implementation to IBM hardware validation
Discovered Crystallization: zero-variance, deterministic convergence on exact mathematical tasks
Ph.D. candidate, Quantum AI researcher, UIUC Grainger College of Engineering
Introducing the Universal Quantum Transformer and the phenomenon of Crystallization. 720x parameter compression, validated on real quantum hardware.
Read article →The full paper by Chung & Talebpour, UIUC. Architecture, crystallization theory, and IBM hardware validation.
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