Quantum Machine Learning for Computer Vision: A Survey
This research delves into quantum machine learning (QML) in the context of computer vision analysis by exploring the progress made in quantum computing and its impact on machine learning applications such as managing datasets and improving large-scale data processing efficiency through QML techniques specialised for tasks like image segmentation and classification in computer vision projects, along with findings, from trials conducted using the EMNIST benchmark dataset.Our tests reached an accuracy level above 90% successfully categorising tasks, with precision. This study explores the uses of quantum machine learning (QML) in areas like identification medical scans and distant monitoring. It also delves into the existing constraints and hurdles linked to quantum computer technologies.
Authors
- Md Majedul Islam
- Jing He (ORCID: https://orcid.org/0000-0001-6488-1052)
Institutions
- Kennesaw State University (US)
Publication Details
- Published
- 2024-12-18
- DOI
- https://doi.org/10.1109/icmla61862.2024.00282
- Citations
- 4
- Primary Topic
- Quantum Computing Algorithms and Architecture
- Type
- article
- Field-Weighted Citation Impact
- 1.39
Funders
- National Science Foundation