Nvidia is excited to announce the release of the first set of notebooks for CUDA-Q Academic

This initiative aims to provide practical, hands-on experience with quantum algorithms and hybrid quantum-classical computing, leveraging the powerful CUDA-Q platform.

Description of the Notebooks

The initial set of modules includes:

• Textbook Example of QAOA for Max-Cut: Students start with a fundamental example to understand the basics of the QAOA algorithm.
• Circuit Cutting: Techniques for breaking down large quantum circuits into smaller, more manageable pieces.
• Parallel QPU/GPU Workflows: Execution of quantum algorithms on quantum processing units (QPUs) and graphical processing units (GPUs) in parallel.
• Adaptations of QAOA: Exploring modifications to the QAOA algorithm to enhance performance and scalability.

Target Audience

These modules are suitable for:

• Students with a basic understanding of quantum algorithms and experience with Python.
• Majors such as data science, computer science, and physics.
• Individuals familiar with variational algorithms, though this is not a strict requirement.

The notebooks can serve as a week-long module or a project at the end of an introductory quantum computing course.

Future Materials and Collaboration

NVIDIA is actively developing additional materials to expand the repository. We welcome collaboration from the community to create new notebooks and enhance the existing ones.

Getting Started

To access the notebooks:

1. Visit the NVIDIA/cuda-q-academic GitHub repository
2. Download the notebooks and run them locally or on Google Colab.