# Traffic Sign Classification Using Deep and Quantum Neural Networks

arXiv: https://arxiv.org/abs/2209.15251 · HTML: https://mlsanity.com/paper/2209.15251/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 22 references that are in the map.
- First posted on arXiv 30 Sep 2022.

## Builds on (most cited first, 5 of 5)

- [Quantum generative adversarial networks](https://mlsanity.com/paper/1804.08641/) (2018)
- [Transfer learning in hybrid classical-quantum neural networks](https://mlsanity.com/paper/1912.08278/) (2019)
- [Hybrid Classical-Quantum Deep Learning Models for Autonomous Vehicle Traffic Image Classification Under Adversarial Attack](https://mlsanity.com/paper/2108.01125/) (2021)
- [Comparing concepts of quantum and classical neural network models for image classification task](https://mlsanity.com/paper/2108.08875/) (2021)
- [Image Classification via Quantum Machine Learning](https://mlsanity.com/paper/2011.02831/) (2020)

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

- [HyQuRP: Hybrid quantum-classical neural network with rotational and permutational equivariance](https://mlsanity.com/paper/2602.06381/) (2026)
- [Layered Quantum Architecture Search for 3D Point Cloud Classification](https://mlsanity.com/paper/2603.20024/) (2026)

## Abstract (arXiv)

Quantum Neural Networks (QNNs) are an emerging technology that can be used in many applications including computer vision. In this paper, we presented a traffic sign classification system implemented using a hybrid quantum-classical convolutional neural network. Experiments on the German Traffic Sign Recognition Benchmark dataset indicate that currently QNN do not outperform classical DCNN (Deep Convolutuional Neural Networks), yet still provide an accuracy of over 90% and are a definitely promising solution for advanced computer vision.