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.