This project’s aim is to develop the computational infrastructure required to support distributed quantum algorithms research, in particular implementations of quantum machine learning.

The SUDS Scholar will help design an emulation framework of quantum processing units to manage the large volumes of data produced by distributed quantum algorithms. The framework will integrate GPU acceleration and inter-QPU communication to model how quantum information is shared across a quantum network. By introducing configurable hardware information, the environment aims to substitute physical distribution of quantum systems. The resulting datasets will be used to test and refine distributed QML workflows, focusing on how model information propagates through interconnected systems. During the project, the student will implement and experiment with components of the emulation environment. This involves data analysis of experimental quantum information; logging, visualization, and reproducibility within the environment. This work will support the development of scalable infrastructure for future quantum machine learning and algorithms research.