QUILT: Effective Multi-Class Classification on Quantum Computers Using an Ensemble of Diverse Quantum Classifiers
Abstract
Quantum computers can theoretically have significant acceleration over classical computers; but, the near-future era of quantum computing is limited due to small number of qubits that are also error prone. QUILT is a framework for performing multi-class classification task designed to work effectively on current error-prone quantum computers. QUILT is evaluated with real quantum machines as well as with projected noise levels as quantum machines become more noise free. QUILT demonstrates up to 85% multi-class classification accuracy with the MNIST dataset on a five-qubit system.
Cite
Text
Silver et al. "QUILT: Effective Multi-Class Classification on Quantum Computers Using an Ensemble of Diverse Quantum Classifiers." AAAI Conference on Artificial Intelligence, 2022. doi:10.1609/AAAI.V36I8.20807Markdown
[Silver et al. "QUILT: Effective Multi-Class Classification on Quantum Computers Using an Ensemble of Diverse Quantum Classifiers." AAAI Conference on Artificial Intelligence, 2022.](https://mlanthology.org/aaai/2022/silver2022aaai-quilt/) doi:10.1609/AAAI.V36I8.20807BibTeX
@inproceedings{silver2022aaai-quilt,
title = {{QUILT: Effective Multi-Class Classification on Quantum Computers Using an Ensemble of Diverse Quantum Classifiers}},
author = {Silver, Daniel and Patel, Tirthak and Tiwari, Devesh},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2022},
pages = {8324-8332},
doi = {10.1609/AAAI.V36I8.20807},
url = {https://mlanthology.org/aaai/2022/silver2022aaai-quilt/}
}