# Comparing concepts of quantum and classical neural network models for image classification task

arXiv: https://arxiv.org/abs/2108.08875 · HTML: https://mlsanity.com/paper/2108.08875/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 21 references that are in the map.
- First posted on arXiv 19 Aug 2021.

## Builds on (most cited first, 1 of 1)

- [TensorFlow Quantum: A Software Framework for Quantum Machine Learning](https://mlsanity.com/paper/2003.02989/) (2020)

## Built on by (most cited first, 5 of 5)

- [Quantum-classical convolutional neural networks in radiological image classification](https://mlsanity.com/paper/2204.12390/) (2022)
- [Bridging Classical and Quantum Machine Learning: Knowledge Transfer From Classical to Quantum Neural Networks Using Knowledge Distillation](https://mlsanity.com/paper/2311.13810/) (2023)
- [Traffic Sign Classification Using Deep and Quantum Neural Networks](https://mlsanity.com/paper/2209.15251/) (2022)
- [Embedding-Aware Quantum-Classical SVMs for Scalable Quantum Machine Learning](https://mlsanity.com/paper/2508.00024/) (2025)
- [FRQI Pairs method for image classification using Quantum Recurrent Neural Network](https://mlsanity.com/paper/2512.11499/) (2025)

## Abstract (arXiv)

While quantum architectures are still under development, when available, they will only be able to process quantum data when machine learning algorithms can only process numerical data. Therefore, in the issues of classification or regression, it is necessary to simulate and study quantum systems that will transfer the numerical input data to a quantum form and enable quantum computers to use the available methods of machine learning. This material includes the results of experiments on training and performance of a hybrid quantum-classical neural network developed for the problem of classification of handwritten digits from the MNIST data set. The comparative results of two models: classical and quantum neural networks of a similar number of training parameters, indicate that the quantum network, although its simulation is time-consuming, overcomes the classical network (it has better convergence and achieves higher training and testing accuracy).