🚀 Start Your Journey Here
- Visit the CUDA-Q Academic Learning Paths to launch the modules and build a custom curriculum.
- Browse the CUDA-Q Academic Visualization Gallery to experiment with the interactive tools featured in the lessons.
NVIDIA's CUDA-Q Academic is a freely available, open-source collection of interactive Jupyter notebooks that prepare the next generation of quantum computing professionals by combining high-performance computing with quantum computing. Developed by NVIDIA in collaboration with universities and tested in real classroom settings, CUDA-Q Academic is organized as a modular curriculum of topic areas ranging from a Quick Start to Quantum Computing through Quantum Error Correction, Quantum Algorithm Simulation 101, Dynamics 101, AI for Quantum, Chemistry Simulations, and more. Each is built using CUDA-Q, NVIDIA's open-source platform for hybrid classical-quantum computing. Materials are free to use for educational purposes under Apache-2.0 and CC-BY-NC-4.0; see LICENSE.
| Resource | Link |
|---|---|
| Learning Paths (launch modules, build a curriculum) | https://nvidia.github.io/cuda-q-academic/learningpath.html |
| Visualization Gallery (interactive widgets) | https://nvidia.github.io/cuda-q-academic/visualization-gallery.html |
| Machine-readable curriculum catalog | curriculum.json |
| Guide to CUDA-Q Backends | Guide-to-cuda-q-backends.ipynb |
| Sample Syllabus | Sample-Syllabus.md |
| Contributing | CONTRIBUTING.md |
| Install CUDA-Q | https://nvidia.github.io/cuda-quantum/latest/using/quick_start.html |
The repository is organized into the learning path modules below. For machine-readable lesson and widget discovery, use curriculum.json. For hosted module overviews, prerequisites, and a curriculum builder, visit the Learning Paths page.
| Module | Folder | Topic |
|---|---|---|
| Quick Start to Quantum Computing | quick-start-to-quantum/ | From zero to a variational algorithm in CUDA-Q |
| Quantum Algorithm Simulation 101 | simulation/ | Choosing between state vector, tensor network, MPS, Pauli propagation, and stabilizer simulation |
| Quantum Information Science Examples | qis-examples/ | Foundational quantum algorithms to complement QIS courses |
| Quantum Error Correction 101 | qec101/ | Classical and quantum codes, decoders, magic-state distillation |
| Chemistry Simulations | chemistry-simulations/ | VQE, ADAPT-VQE, QM/MM, Krylov methods, and more. |
| Quantum Applications for Finance | quantum-applications-to-finance/ | Quantum walks, portfolio optimization, QChop |
| QAOA for Max Cut | qaoa-for-max-cut/ | Divide-and-conquer QAOA with circuit cutting |
| AI for Quantum | ai-for-quantum/ | Using AI models to enable quantum computing |
| Dynamics 101 | dynamics101/ | GPU-accelerated Schrödinger and Lindblad time evolution |
| Hybrid Workflows | hybrid-workflows/ | Hybrid classical–quantum workflow examples |
Each module folder contains student notebooks, a module-local README.md, a solutions/ subfolder, and an images/ subfolder with figures.
Recommended: launch on NVIDIA Brev. Brev provisions a pre-built CPU or GPU instance with CUDA-Q and all notebook prerequisites already installed.
Launch: https://brev.nvidia.com/launchable/deploy/now?launchableID=env-39dN1v7RucHHgj97LILUlnXjnk5
See brev-instructions.pdf for a step-by-step walkthrough.
Other entry points:
- qBraid — hosted Jupyter environment with CUDA-Q support. Visit qbraid.com.
- Google Colab — each notebook includes a commented-out install cell. Uncomment it, run it to install CUDA-Q and download supporting assets, restart the kernel, and run the notebook.
- Local — install CUDA-Q directly following the CUDA-Q install guide. Recommended for the largest GPU-accelerated examples.
Each module's README.md contains module-specific run notes.
New notebook content follows the schema defined by notebook_template.ipynb: the first markdown cell contains the title plus labeled sections for What You Will Do, Prerequisites, Key Terminology, CUDA-Q Syntax, and a Solutions link. Agents and tooling can rely on this schema to programmatically discover what each notebook covers. AI coding agents working in this repository should read AGENTS.md first.
CUDA-Q Academic is released under Apache-2.0 and CC-BY-NC-4.0. Developed by NVIDIA in collaboration with university partners; freely available for educational use. This project downloads and installs additional third-party open-source software; review the licenses of those projects before use.