Paper ID: 2402.12704

Quantum Embedding with Transformer for High-dimensional Data

Hao-Yuan Chen, Yen-Jui Chang, Shih-Wei Liao, Ching-Ray Chang

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.

Submitted: Feb 20, 2024