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This repo contains CUDA-Q Academic materials, including self-paced Jupyter notebook modules for building and optimizing hybrid quantum-classical algorithms using CUDA-Q.
🚀 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.
- Stay Connected: Sign up for the CUDA-Q newsletter to get updates on tutorials, releases, and events.
NVIDIA's CUDA-Q Academic is a freely available, open-source collection of interactive educational resources that prepare the next generation of quantum computing professionals by combining high-performance computing with quantum computing. Designed to supplement university quantum computing courses, it enriches classroom instruction and textbook learning with hands-on, interactive modules built using CUDA-Q, NVIDIA's open-source platform for hybrid classical-quantum computing. Developed by NVIDIA in collaboration with universities and tested in real classroom settings, CUDA-Q Academic is organized in modules with topics 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. 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 |
| CUDA-Q Newsletter (updates, tutorials, events) | https://www.nvidia.com/en-us/solutions/quantum-computing/cuda-q-newsletter/ |
| Machine-readable curriculum catalog | curriculum.json |
| Guide to CUDA-Q Backends | Guide-to-cuda-q-backends.ipynb |
| Instructor Guide | Instructor-Guide.md |
| Contributing | CONTRIBUTING.md |
| Install CUDA-Q | https://nvidia.github.io/cuda-quantum/latest/using/quick_start.html |
The CUDA-Q Hello World visualization shows how a quantum circuit diagram translates into CUDA-Q code — a good starting point before you open your first notebook.
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.
| Resource | Folder | Description |
|---|---|---|
| Quantum Computing Group Project | quantum-ai-project-template/ | Role-based, agentic-AI group project template for deploying a quantum-GPU computing project in a course. See the Group Project section below. |
| Calibration | calibration/ | Classroom guide for using NVIDIA's Ising Calibration — an open vision-language model purpose-built for quantum hardware calibration plot analysis. Browser-based NIM playground (no setup, no API key) plus sample plots from the QCalEval dataset. Hands-on tutorial notebooks coming soon. |
The CUDA-Q Academic Quantum Computing Group Project is a course-ready template for running a hybrid quantum-GPU computing project as a team assignment. It is designed around agentic AI: each student takes one of four defined roles — Project Lead, Performance Optimization, Quality Assurance, and Technical Marketing — and configures an AI coding agent (Claude Code, Cursor, Windsurf, or any chat-based LLM) from a role-specific starter prompt. Students retain accountability for architecture, classical benchmarking, and honest reporting while delegating implementation to their agents.
Teams (or solo students taking all roles sequentially) move through four milestones — plan, research, build, and showcase — with explicit deliverables at each step. The template ships with:
It is not a self-paced module. See quantum-ai-project-template/Teaching-Guide.md for deployment instructions and the folder README for the file inventory.
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:
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.