QSTToolkit Documentation
QSTToolkit is an open-source package for performing optical quantum state tomography (QST) research using both traditional statistical and novel deep learning-powered methods in Python. Key functionality includes:
Fast, compute-efficient and customisable generation of realistic synthetic data for a variety of optical quantum states using the QuTiP package.
Maximum Likelihood Estimation quantum state tomography.
A variety of deep learning powered methods for quantum state discrimination and tomography.
The key aim of QSTToolkit is to create a standard framework for researching, designing and comparing methods for quantum state tomography in noisy, high-dimensional, continuous quantum systems. Recently proposed models are implemeted, using standardized synthetic data allowing for fully valid comparison between approaches.
This page contains documentation for QSTToolkit and its subpackages. Detailed usage instructions with example notebooks can be found in the package’s GitHub repository. This work is the culmination of a physics masters project by George FitzGerald (gwfitzg@hotmail.com) at Durham University’s Department of Physics.