# Quantum Embedding with Transformer for High-dimensional Data

arXiv: https://arxiv.org/abs/2402.12704 · HTML: https://mlsanity.com/paper/2402.12704/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 16 references that are in the map.
- First posted on arXiv 20 Feb 2024.

## Builds on (most cited first, 7 of 7)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Self-Attention with Relative Position Representations](https://mlsanity.com/paper/1803.02155/) (2018)
- [Supervised quantum machine learning models are kernel methods](https://mlsanity.com/paper/2101.11020/) (2021)
- [Towards Robust Vision Transformer](https://mlsanity.com/paper/2105.07926/) (2021)
- [Training Quantum Embedding Kernels on Near-Term Quantum Computers](https://mlsanity.com/paper/2105.02276/) (2021)
- [Trainable Discrete Feature Embeddings for Variational Quantum Classifier](https://mlsanity.com/paper/2106.09415/) (2021)
- [Quantum Neural Machine Learning - Backpropagation and Dynamics](https://mlsanity.com/paper/1609.06935/) (2016)

## Built on by (most cited first, 2 of 2)

- [Superpositional Gradient Descent: Harnessing Quantum Principles for Model Training](https://mlsanity.com/paper/2511.01918/) (2025)
- [Investigating Quantum-Embedded Transformers on Classical Datasets for Cross-Modality Classification](https://mlsanity.com/paper/2608.06846/) (2026)

## Abstract (arXiv)

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.