Chemistry Simulations

This collection of notebooks explores techniques for calculating molecular ground state energies, a fundamental problem in the field. The notebooks offer a deep dive into implementing several approaches including VQE, ADAPT-VQE, Krylov subspace methods, and QM/MM. It also considers examples of using AI for quantum with the Generative Quantum Eigensolver (GQE). The notebooks can be completed in any order, though there are a couple of exercises that assume you are completing them in the suggested order. Learners new to quantum chemistry should start with the notebook covering VQE and GQE.

Pre-requisites: Learners should have familiarity with Jupyter notebooks and programming in Python and CUDA-Q. Since these notebooks cover chemistry and materials science simulations, domain knowledge is helpful. 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 CUDA-Q knowledge if the reader is new to quantum computing and CUDA-Q or needs refreshing.

Notebooks

The Jupyter notebooks in this folder are designed to run on GPUs in an environment with CUDA-Q and Python. For instructions on how to install CUDA-Q on your machine, check out this guide. A Dockerfile and requirements.txt are also included in the main directory of the repository to help get you set up.

Otherwise, if you have set up an account in Google CoLab, simply log in to the account, then click on the icons below to run the notebooks on the listed platform.

Notebook	Google Colab
Lab 1 - Solving the Ground State Problem with VQE and AI (Generative Quantum Eigensolver)
Lab 2 - ADAPT VQE
Lab 3 - Krylov Quantum Subspace Diagonalization
Lab 4 - QM/MM: Combining VQE with a Polarizeable Embedding Framework