piQture: A QML Library for Image Processing
Quantum Machine Learning (QML) 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. We present piQture, an open-source Python and Qiskit-based library that streamlines the implementation and training of QML models tailored for image processing. Its design and structure prioritize usability among users familiar with classical machine learning without prior QML experience.
Authors
- Saasha Joshi (ORCID: https://orcid.org/0000-0001-9928-9796)
- Daniel Justice (ORCID: https://orcid.org/0000-0001-5450-2207)
- Ulrike Stege (ORCID: https://orcid.org/0000-0001-9466-7196)
Institutions
- University of Victoria (CA)
- Carnegie Mellon University (US)
Publication Details
- Published
- 2024-09-15
- DOI
- https://doi.org/10.1109/qce60285.2024.10416
- Citations
- 1
- Primary Topic
- Image Retrieval and Classification Techniques
- Type
- article
- Field-Weighted Citation Impact
- 0.25