Year: 2023
Type: dissertation
Abstract: In this thesis it has been chosen to test the performance of hybrid quantum classical algorithms in one of the most important ML sub-field: computer vision, in particular image classification and generation. Following the most promising results in recent literature two algorithms have been implemented and tested: the data re-uploading scheme, that has been adapted to image classification and the patch gan model, ideal for small scale quantum devices. The two algorithms reported performances comparable or better than classical algorithms with the same complexity, proving the importance of small scale quantum algorithms and hinting for bigger quantum advantages in future years.
Author: TIEZZI, NICCOLO' FRANCESCO
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7029014112