CUDA-Q Academic
NVIDIA's CUDA-Q Academic is a freely available collection of interactive Jupyter notebooks designed to prepare the next generation of quantum computing professionals by combining high-performance computing with quantum computing skills, developed in collaboration with universities and tested in real classroom settings. This repository contains Jupyter notebooks and supporting files for quantum computing training using CUDA-Q. These training materials are provided free of charge. Please see LICENSE for license details.
Instructions to install CUDA-Q can be found in the instructions.md file. If you do not have a local installation of CUDA-Q running on a GPU, the notebooks can be opened in qBraid Lab, CoCalc, or in Google Colab. Directions for this are found in the README.md files in the main folder for each set of notebooks.
Educational Resources and Modules
The sample syllabus is intended to assist faculty or students in identifying CUDA-Q resources that align with their quantum information science or quantum computing syllabi or learning path.
The Guide to CUDA-Q Backends is a one-stop resource for code snippets and descriptions of the CUDA-Q backend simulator and hardware options for executing CUDA-Q kernels.
This repository contents are detailed below. To get started, we recommend beginning with Quick Start to Quantum Computing with CUDA-Q to grasp the fundamentals of CUDA-Q and quantum computing. Once you have the basics, you can proceed with the remaining modules in any order. This repository is actively being developed, so be sure to check back regularly for new modules and notebooks.
Quick Start to Quantum Computing with CUDA-Q
The Quick Start to Quantum Computing with CUDA-Q module aims to take a learner from no knowledge of quantum computation to programming a variational algorithm in CUDA-Q. This material, which includes Jupyter notebooks, is organized into labs that build upon one another.
Pre-requisites: Learners should have familiarity with Jupyter notebooks and programming in Python. Additionally, pre-requisite knowledge includes complex numbers, linear algebra, and statistics. In particular, we assume experience computing and understanding of arithmetic of complex numbers, probabilities, expectation values, vectors, dot products, and matrix multiplication. Knowledge of eigenvalues and eigenvectors will be helpful, but not necessarily a requirement.
Quantum Information Science Examples
The qis-examples folder contains example code and explanations of foundational quantum algorithms often appearing in Quantum Information Science courses. It is intended to complement quantum information science textbooks and courses, rather than being self-contained.
Pre-requisites:
Familiarity with Python with enough comfort to refer to Python package documentation, specifically NetworkX, as needed
Completion of the Quick Start to Quantum Computing with CUDA-Q
QAOA for Max Cut Module
The Divide-and-Conquer QAOA for Max Cut module takes a learner from the implementation of QAOA to solve a small max problem to an application of a divide-and-conquer QAOA algorithm to a large max cut problem using parallel computation. This visual introduction to QAOA and circuit cutting provides the learner the background to understand more advanced topics such as ADAPT-QAOA, Adaptive Circuit Knitting, and QAOA-GPT.
Prerequisites:
Familiarity with Python with enough comfort to refer to Python package documentation, specifically NetworkX, as needed
Completion of the Quick Start to Quantum Computing with CUDA-Q course or equivalent familiarity with variational quantum algorithms (e.g. VQE or QAOA).
Quantum Applications for Finance
This collection of tutorials provides practical examples of quantum algorithms applied to key challenges in finance. You will explore the use of quantum walks for modeling financial data and see how they compare to classical methods. The collection also offers a deep dive into investment portfolio optimization, demonstrating solutions built with the Quantum Approximate Optimization Algorithm (QAOA), quantum annealing, and the novel QChop algorithm from Infleqtion
Pre-requisites: Learners should have familiarity with Jupyter notebooks and programming in Python and CUDA-Q. It is assumed the reader has some familiarity already with quantum computation and is comfortable with braket notation and the concepts of qubits, quantum circuits, measurement, and circuit sampling. The CUDA-Q Academic course entitled "Quick Start to Quantum Computing with CUDA-Q" provide a walkthrough of this prerequisite knowledge if the reader is new to quantum computing and CUDA-Q or needs refreshing.
Quantum Error Correction (QEC) 101
Whether you're a beginner or looking to deepen your understanding, this series will provide you with the skills and motivation to explore the cutting-edge field of quantum error correction.
Pre-requisites: Learners should have familiarity with Jupyter notebooks and programming in Python and CUDA-Q. It is assumed the reader has some familiarity already with quantum computation and is comfortable with braket notation and the concepts of qubits, quantum circuits, measurement, and circuit sampling. The CUDA-Q Academic course entitled "Quick Start to Quantum Computing with CUDA-Q" provide a walkthrough of this prerequisite knowledge if the reader is new to quantum computing and CUDA-Q or needs refreshing.
Dynamics 101
This advanced series of tutorials demonstrates GPU-accelerated time evolution of Schrodinger and Lindblad master equations for quantum systems. Prerequisite knowledge of open and closed quantum systems is assumed.
Chemistry Simulations
This collection of notebooks explores techniques for calculating molecular ground state energies, a fundamental problem in chemistry. The notebooks offer a deep dive into implementing and applying the ADAPT-VQE algorithm and various Krylov subspace methods (Coming soon!).