AAAI 2023technical7 citations
SLIQ: Quantum Image Similarity Networks on Noisy Quantum Computers
Daniel Silver, Tirthak Patel, Aditya Ranjan, Harshitta Gandhi, William Cutler, Devesh Tiwari
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 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.
BibTeX
@article{Silver_Patel_Ranjan_Gandhi_Cutler_Tiwari_2023, title={SLIQ: Quantum Image Similarity Networks on Noisy Quantum Computers}, volume={37}, url={https://ojs.aaai.org/index.php/AAAI/article/view/26175}, DOI={10.1609/aaai.v37i8.26175}, abstractNote={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.}, number={8}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Silver, Daniel and Patel, Tirthak and Ranjan, Aditya and Gandhi, Harshitta and Cutler, William and Tiwari, Devesh}, year={2023}, month={Jun.}, pages={9846-9854} }