Quantum Embedding with Transformer for High-dimensional Data
<|reference_start|>Quantum Embedding with Transformer for High-dimensional Data: 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.<|reference_end|>
@article{chen2024quantum,
    title={Quantum Embedding with Transformer for High-dimensional Data},
    author={Hao-Yuan Chen, Yen-Jui Chang, Shih-Wei Liao, Ching-Ray Chang},
    journal={arXiv preprint arXiv:2402.12704},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.12704},
    primaryClass={quant-ph cs.LG}
}