Work

Built an interactive 3D quantum circuit builder in Python at BTQ Technologies to accelerate error correction research, integrating surface code construction, LDPC code visualization, and belief propagation decoding into a single Qiskit-compatible toolkit with guided tutorials.

Developed topological quantum neural networks in Python to improve generalization in deep learning and boost quantum algorithm efficiency. Employed TQFT-inspired spin-network encoding with a real-time tensor network simulator and interactive classification GUI for robustness testing.

Created modular consensus networks in Python at BTQ Technologies for cryptographic proof in blockchain, leveraging topological data analysis to strengthen post-quantum consensus protocols and characterize autonomous network evolution in quantum key distribution systems.

Built a quantum random number generation toolkit in Python at BTQ Technologies for consensus protocols, employing quantum key distribution through Quantum Consensus Networks to produce certifiable randomness with honest-majority verification, enhancing security in cryptographic and blockchain systems.

Developed two quantum-specialized LLM assistants in TypeScript and Next.js at BTQ Technologies to streamline research and education workflows. BasiQ serves as a domain-tuned tutor for quantum theory, computing, and machine learning. Qonductor provides an internal research assistant for academic and engineering teams.

Built a quantum industry analytics platform in TypeScript at BTQ Technologies to quantify cryptographic risk timelines and educate stakeholders on quantum threats to public-key infrastructure. Features a quantum risk calculator, threat timeline modeling, and post-quantum migration resources.