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.

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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
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article

piQture: A QML Library for Image Processing

Saasha Joshi, Daniel Justice, Ulrike Stege
1 citations
Image Retrieval and Classification Techniques
0.25
article

piQture: A QML Library for Image Processing

Saasha Joshi, Daniel Justice, Ulrike Stege
article en
1 citations

Abstract

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.

University of Victoria (CA), Carnegie Mellon University (US)
Openalex Percentile: Top 42%
Image Retrieval and Classification Techniques
0.25
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