NVIDIA/cuda-q-academic

NVIDIA/cuda-q-academic

Description: This repo contains CUDA-Q Academic materials, including self-paced Jupyter notebook modules for building and optimizing hybrid quantum-classical algorithms using CUDA-Q.

Language: Jupyter Notebook

License: NOASSERTION

Stars: 349

Forks: 99

Open issues: 5

Created: 2024-07-10T18:48:27Z

Pushed: 2026-06-12T19:09:49Z

Default branch: main

Fork: no

Archived: no

README:

CUDA-Q Academic

> 🚀 Start Your Journey Here > > * Visit the [CUDA-Q Academic Learning Paths](https://nvidia.github.io/cuda-q-academic/learningpath.html) to launch the modules and build a custom curriculum. > * Browse the [CUDA-Q Academic Visualization Gallery](https://nvidia.github.io/cuda-q-academic/visualization-gallery.html) 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.

About

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](LICENSE).

Quick Links

| 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](curriculum.json) | | Guide to CUDA-Q Backends | [Guide-to-cuda-q-backends.ipynb](Guide-to-cuda-q-backends.ipynb) | | Instructor Guide | [Instructor-Guide.md](Instructor-Guide.md) | | Contributing | [CONTRIBUTING.md](CONTRIBUTING.md) | | Install CUDA-Q | https://nvidia.github.io/cuda-quantum/latest/using/quick_start.html |

Coming from another quantum framework?

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.

Repository Contents

The repository is organized into the learning path modules below. For machine-readable lesson and widget discovery, use [curriculum.json](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/](quick-start-to-quantum/) | From zero to a variational algorithm in CUDA-Q | | Quantum Algorithm Simulation 101 | [simulation/](simulation/) | Choosing between state vector, tensor network, MPS, Pauli propagation, and stabilizer simulation | | Quantum Information Science Examples | [qis-examples/](qis-examples/) | Foundational quantum algorithms to complement QIS courses | | Quantum Error Correction 101 | [qec101/](qec101/) | Classical and quantum codes, decoders, magic-state distillation | | Chemistry Simulations | [chemistry-simulations/](chemistry-simulations/) | VQE, ADAPT-VQE, QM/MM, Krylov methods, and more. | Quantum Applications for Finance | [quantum-applications-to-finance/](quantum-applications-to-finance/) | Quantum walks, portfolio optimization, QChop | | QAOA for Max Cut | [qaoa-for-max-cut/](qaoa-for-max-cut/) | Divide-and-conquer QAOA with circuit cutting | | AI for Quantum | [ai-for-quantum/](ai-for-quantum/) | Using AI models to enable quantum computing | | Dynamics 101 | [dynamics101/](dynamics101/) | GPU-accelerated Schrödinger and Lindblad time evolution | | Hybrid Workflows | [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.

Instructor Resources

| Resource | Folder | Description | |---|---|---| | Quantum Computing Group Project | [quantum-ai-project-template/](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](#quantum-computing-group-project) section below. | | Calibration | [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. |

Quantum Computing Group Project

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...