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Topic

piQture: A Quantum Machine Learning Library for Image Processing

Department of Computer Science

Date & location

• Friday, June 14, 2024

• 9:00 A.M.

• Engineering and Computer Science Building

• Room 468

Reviewers

Supervisory Committee

• Dr. Hausi Muller, Department of Computer Science, ·¬ÇÑÉçÇø (Co-Supervisor)

• Dr. Ulrike Stege, Department of Computer Science, UVic (Co-Supervisor) 

External Examiner

• Dr. Nikitas Dimopoulous, Department of Electrical and Computer Engineering, UVic 

Chair of Oral Examination

• Dr. Mihai Sima, Department of Electrical and Computer Engineering, UVic

   

Abstract

Quantum Machine Learning (QML) is a discipline of research at the intersection of quantum information and machine learning that leverages quantum mechanical properties to enhance computational capabilities. With its emergence, there is a need to integrate QML models into machine learning pipelines for real-life applications such as image processing. While standalone programs exist to demonstrate the performance of QML models, a well-defined model workflow is noticeably absent. This thesis thoroughly explores various existing QML models and their practical utility in image processing tasks, with the aim of constructing a robust QML library.

Throughout this thesis, we develop piQture, an open-source Python and Qiskit based library that streamlines the development, training, and evaluation of QML models. Its design and structure prioritize usability among users familiar with classical machine learning without prior QML experience. Further, piQture is augmented with automated building, testing, and packaging workflows that enhance software reliability and reproducibility. Finally, we provide strategies to facilitate model management and storage within piQture for practical adoption and future analysis of pre-trained QML models.