This repo contains CUDA-Q Academic materials, including self-paced Jupyter notebook modules for building and optimizing hybrid quantum-classical algorithms using CUDA-Q.
Policy-controlled NVIDIA CUDA-Q execution with resource preflight, target guardrails, diagnostics, backend comparison, and tamper-evident audit trails.
OpenQASM VS Code Extension
Minicurso de quantum Machine learning
Rust HAL for quantum hardware. One Python API across IBM, IQM, Quantinuum, AQT, IonQ, Quandela, Scaleway, AWS Braket, NVIDIA CUDA-Q, MQT DDSIM and a local simulator, with Qiskit / Cirq / Qrisp / PennyLane as input converters.
Quantum portfolio optimisation: NVIDIA Ising + CUDA-Q + Claude AI. 373x GPU speedup · +3.1% vs classical · 1.4M× sampling efficiency · QPU validated on Rigetti Cepheus-1-108Q.
Scaling a CUDA-Q GQE + QSCI pipeline to 40 qubits for EUV photoresist chemistry: generative quantum eigensolver circuits, quantum-selected configuration interaction, and real-QPU runs on tin-based resist molecules.
Bare-metal performance engineer — hand-written CUDA/Rust/C++ for physics, quantum & neuro. Benchmarks I can defend.
AI-driven generative quantum circuit design for molecular & materials discovery — GNN + Transformer + QD-GRPO reinforcement learning + CUDA-Q, validated on QPU hardware. GIC 2026.
VQE ground-state solver for the Jaynes-Cummings quantum optics model, built with NVIDIA CUDA-Q
MCP server serving NVIDIA CUDA-Q docs, API reference, and runnable examples to AI agents, version-pinned to your installed cudaq
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