SLIQ: Quantum Image Similarity Networks on Noisy Quantum Computers
DOI:
https://doi.org/10.1609/aaai.v37i8.26175Keywords:
ML: Quantum Machine LearningAbstract
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 ef- forts have yet to solve unsupervised similarity detection tasks due to the challenge of porting them to run on quantum com- puters. To overcome this challenge, we propose SLIQ, the first open-sourced work for resource-efficient quantum sim- ilarity detection networks, built with practical and effective quantum learning and variance-reducing algorithms.Downloads
Published
2023-06-26
How to Cite
Silver, D., Patel, T., Ranjan, A., Gandhi, H., Cutler, W., & Tiwari, D. (2023). SLIQ: Quantum Image Similarity Networks on Noisy Quantum Computers. Proceedings of the AAAI Conference on Artificial Intelligence, 37(8), 9846-9854. https://doi.org/10.1609/aaai.v37i8.26175
Issue
Section
AAAI Technical Track on Machine Learning III