Quantum embedding with transformers is a novel and promising architecture for quantum machine learning to deliver exceptional capability on near-term devices or simulators. The research incorporated a vision transformer (ViT) to advance quantum significantly embedding ability and results for a single qubit classifier with around 3 percent in the median F1 score on the BirdCLEF-2021, a challenging high-dimensional dataset. The study showcases and analyzes empirical evidence that our transformer-based architecture is a highly versatile and practical approach to modern quantum machine learning problems.
@article{arxiv.2402.12704,
title = {Quantum Embedding with Transformer for High-dimensional Data},
author = {Hao-Yuan Chen and Yen-Jui Chang and Shih-Wei Liao and Ching-Ray Chang},
journal= {arXiv preprint arXiv:2402.12704},
year = {2024}
}