SLIQ: Quantum Image Similarity Networks on Noisy Quantum Computers
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.
Cite
Text
Silver et al. "SLIQ: Quantum Image Similarity Networks on Noisy Quantum Computers." AAAI Conference on Artificial Intelligence, 2023. doi:10.1609/AAAI.V37I8.26175Markdown
[Silver et al. "SLIQ: Quantum Image Similarity Networks on Noisy Quantum Computers." AAAI Conference on Artificial Intelligence, 2023.](https://mlanthology.org/aaai/2023/silver2023aaai-sliq/) doi:10.1609/AAAI.V37I8.26175BibTeX
@inproceedings{silver2023aaai-sliq,
title = {{SLIQ: Quantum Image Similarity Networks on Noisy Quantum Computers}},
author = {Silver, Daniel and Patel, Tirthak and Ranjan, Aditya and Gandhi, Harshitta and Cutler, William and Tiwari, Devesh},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2023},
pages = {9846-9854},
doi = {10.1609/AAAI.V37I8.26175},
url = {https://mlanthology.org/aaai/2023/silver2023aaai-sliq/}
}