NVIDIA’s open teaching materials for CUDA-Q, built as Jupyter notebooks with a lesson planner. The GitHub README (updated September 2026) lists tracks from Quick Start to Quantum Computing through QIS algorithms, QEC 101, chemistry simulations, finance, QAOA for Max-Cut, quantum machine learning, AI for quantum, dynamics, hybrid workflows and calibration. Beginners are told to start with Quick Start, which goes from a single qubit to a first variational algorithm.
You can run the notebooks on a local CUDA-Q install or in the browser via qBraid, CoCalc, Google Colab, NVIDIA Brev or Amazon Braket. The curriculum file is public. Materials are free for educational use (Apache-2.0 and CC-BY-NC-4.0; see their LICENSE). This is not an NVIDIA-proctored certificate.
This site already has a CUDA-Q Hello World tutorial. The Academic repo is the full classroom path, not that one notebook.
Who it is for
Students and self-learners who want GPU-accelerated hybrid code, not a first Qiskit course. If you only need IBM hardware, stay on Qiskit Hello World.
Topics covered
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CUDA-Q Academic (NVIDIA) is free, at beginner level, and runs Self-paced.
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