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Quantum Machine Learning
SLIQ: Quantum Image Similarity Networks on Noisy Quantum Computers
arXiv
Authors: Daniel Silver, Tirthak Patel, Aditya Ranjan, Harshitta Gandhi, William Cutler, Devesh Tiwari
Year
2023
Paper ID
54406
Status
Preprint
Abstract Read
~2 min
Abstract Words
73
Citations
N/A
Abstract
Exploration into quantum machine learning has grown tremendously in recent years due to the ability of quantum computers to speed up classical programs. However, these efforts have yet to solve unsupervised similarity detection tasks due to the challenge of porting them to run on quantum computers. To overcome this challenge, we propose SLIQ, the first open-sourced work for resource-efficient quantum similarity detection networks, built with practical and effective quantum learning and variance-reducing algorithms.
Why This Paper Matters
- This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
- It adds a 2023 reference point for readers tracking recent quantum research.
- Exploration into quantum machine learning has grown tremendously in recent years due to the ability of quantum computers to speed up classical programs.
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