piQture: A Quantum Machine Learning Library for Image Processing

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dc.contributor.authorJoshi, Saasha
dc.contributor.supervisorMuller, Hausi A.
dc.contributor.supervisorStege, Ulrike
dc.date.accessioned2024-06-18T22:49:53Z
dc.date.available2024-06-18T22:49:53Z
dc.date.issued2024
dc.degree.departmentDepartment of Computer Science
dc.degree.levelMaster of Science MSc
dc.description.abstractQuantum 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.
dc.description.scholarlevelGraduate
dc.identifier.urihttps://hdl.handle.net/1828/16627
dc.languageEnglisheng
dc.language.isoen
dc.rightsAvailable to the World Wide Web
dc.subjectQuantum Computing
dc.subjectComputer Science
dc.subjectMachine Learning
dc.titlepiQture: A Quantum Machine Learning Library for Image Processing
dc.typeThesis

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