Our algorithms are grounded in peer-reviewed research and validated on real quantum hardware. We publish our work to advance the field.
We demonstrate 53.3% token compression using quantum amplitude encoding on IBM ibm_fez (156 qubits) with 99.43% hardware fidelity. Our approach enables O(log n) memory complexity for AI agent context storage.
We present a quantum teleportation-based backpropagation algorithm achieving 97.78% gradient similarity compared to classical backprop. The protocol enables efficient gradient computation for quantum-classical hybrid neural networks.
VQE-based energy minimization with AMBER-like force fields, PDB structure loading, and CASP benchmarks (RMSD, GDT-TS, TM-score).
QAOA-based portfolio optimization with VaR/CVaR risk metrics, Sharpe ratio, and market regime detection.
NIST ML-KEM and ML-DSA implementation with quantum key distribution hybrid schemes.
VQE-based electronic structure calculations for battery and catalyst materials.
We're always looking for collaborators in academia and industry. Interested in joint research? Let's talk.
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