# Hybrid Quantum Neural Network in High-dimensional Data Classification

arXiv: https://arxiv.org/abs/2312.01024 · HTML: https://mlsanity.com/paper/2312.01024/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 12 references that are in the map.
- First posted on arXiv 2 Dec 2023.

## Builds on (most cited first, 4 of 4)

- [Quantum Entanglement in Deep Learning Architectures](https://mlsanity.com/paper/1803.09780/) (2018)
- [Quantum Deep Learning](https://mlsanity.com/paper/1412.3489/) (2014)
- [Classification with Quantum Machine Learning: A Survey](https://mlsanity.com/paper/2006.12270/) (2020)
- [Advances in Quantum Deep Learning: An Overview](https://mlsanity.com/paper/2005.04316/) (2020)

## Built on by (most cited first, 1 of 1)

- [Large-Scale Benchmarking of Quantum Neural Network Configurations for Financial Time Series Forecasting](https://mlsanity.com/paper/2610.12148/) (2026)

## Abstract (arXiv)

The research explores the potential of quantum deep learning models to address challenging machine learning problems that classical deep learning models find difficult to tackle. We introduce a novel model architecture that combines classical convolutional layers with a quantum neural network, aiming to surpass state-of-the-art accuracy while maintaining a compact model size. The experiment is to classify high-dimensional audio data from the Bird-CLEF 2021 dataset. Our evaluation focuses on key metrics, including training duration, model accuracy, and total model size. This research demonstrates the promising potential of quantum machine learning in enhancing machine learning tasks and solving practical machine learning challenges available today.