Quantum-Inspired Acceleration for Image Reconstruction on Ising Machines
The study investigates a quantum-inspired approach to image reconstruction using Ising machines and demonstrates its significant improvements over the contrastive divergence method in Restricted Boltzmann Machines training and the quality of image reconstruction on the MINST digits and fashion datasets.
Authors
- Lien-Po Yu
- Ching‐Ray Chang (ORCID: https://orcid.org/0000-0003-1974-9583)
- Hao-Yuan Chen (ORCID: https://orcid.org/0009-0003-3006-9156)
- Yen-Jui Chang (ORCID: https://orcid.org/0009-0009-7400-9183)
- Chao‐Ming Fu
- Y. C. Lu
Institutions
- University of London (GB)
- Chung Yuan Christian University (TW)
- National Taiwan University (TW)
- Institute for Information Industry (TW)
Publication Details
- Published
- 2024-09-15
- DOI
- https://doi.org/10.1109/qce60285.2024.10398
- Primary Topic
- Quantum Computing Algorithms and Architecture
- Type
- article
- Field-Weighted Citation Impact
- 0.00