Quantum Embedding with Transformer for High-dimensional Data

arXiv:2402.12704v1 Announce Type: cross
Abstract: 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 …

abstract advance architecture arxiv capability classifier cs.lg data devices embedding machine machine learning near novel quant-ph quantum qubit research transformer transformers type vision vit