This 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.
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.
The Jupyter notebooks in this folder are designed to run in an environment with CUDA-Q and Python. For instructions on how to install CUDA-Q on your machine, check out this guide.
Otherwise, explore our Learning Pathways page for additional cloud-based options to run these notebooks.