Quantum Recurrent Architectures for Text Classification
Wenduan Xu, Stephen Clark, Douglas Brown, Gabriel Matos, Konstantinos Meichanetzidis
Abstract
We develop quantum RNNs with cells based on Parametrised Quantum Circuits (PQCs). PQCs can provide a form of hybrid quantum-classical computation where the input and the output is in the form of classical data. The previous “hidden” state is the quantum state from the previous time-step, and an angle encoding is used to define a (non-linear) mapping from a classical word embedding into the quantum Hilbert space. Measurements of the quantum state provide classical statistics which are used for classification. We report results which are competitive with various RNN baselines on the Rotten Tomatoes dataset, as well as emulator results which demonstrate the feasibility of running such models on quantum hardware.
BibTeX
@inproceedings{xu-etal-2024-quantum,
title = "Quantum Recurrent Architectures for Text Classification",
author = "Xu, Wenduan and
Clark, Stephen and
Brown, Douglas and
Matos, Gabriel and
Meichanetzidis, Konstantinos",
editor = "Al-Onaizan, Yaser and
Bansal, Mohit and
Chen, Yun-Nung",
booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.emnlp-main.1000/",
doi = "10.18653/v1/2024.emnlp-main.1000",
pages = "18020--18027"
}