Implementing variational quantum algorithms in Q#

This sample demonstrates:

• How variational quantum algorithms use classical and quantum computation together to solve problems.
• How to use Q# and QuTiP together to estimate the energy of different quantum states.
• Using Q# to implement the variational quantum eigensolver.

Prerequisites

• The Microsoft Quantum Development Kit.

This sample is designed to work with the conda environment specified by environment.yml. To create this environment:

conda env create -f environment.yml

Running the Sample

This sample can be run as a Jupyter Notebook:

conda activate variational
jupyter notebook

Manifest

• Variational Quantum Algorithms.ipynb: Main Jupyter Notebook for this sample.
• Optimization.qs: Q# implementation of the SPSA algorithm.
• VariationalAlgorithms.csproj: Main Q# project for this sample.
• enviornment.yml: Specification of a conda environment for this sample.
• .iqsharp-config.json: Preferences for Q# visualization in Jupyter Notebooks.

References

• arXiv:1304.3061

Further resources

• For an example of using variational quantum eigensolvers in chemistry, see the azure-quantum/chemistry sample.
• For an example of using iterative phase estimation to more efficiently learn energies, see the characterization/phase-estimation sample.