# A Quantum Convolutional Neural Network Approach for Object Detection and Classification

arXiv: https://arxiv.org/abs/2307.08204 · HTML: https://mlsanity.com/paper/2307.08204/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 20 references that are in the map.
- First posted on arXiv 17 Jul 2023.

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

- [An Introduction to Convolutional Neural Networks](https://mlsanity.com/paper/1511.08458/) (2015)
- [Advances in Quantum Deep Learning: An Overview](https://mlsanity.com/paper/2005.04316/) (2020)
- [Robustness Verification of Quantum Classifiers](https://mlsanity.com/paper/2008.07230/) (2020)
- [A Study of Complex Deep Learning Networks on High Performance, Neuromorphic, and Quantum Computers](https://mlsanity.com/paper/1703.05364/) (2017)
- [Quantum Optical Convolutional Neural Network: A Novel Image Recognition Framework for Quantum Computing](https://mlsanity.com/paper/2012.10812/) (2020)

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

- [ResQuNNs: Towards Enabling Deep Learning in Quantum Convolution Neural Networks](https://mlsanity.com/paper/2402.09146/) (2024)

## Abstract (arXiv)

This paper presents a comprehensive evaluation of the potential of Quantum Convolutional Neural Networks (QCNNs) in comparison to classical Convolutional Neural Networks (CNNs) and Artificial / Classical Neural Network (ANN) models. With the increasing amount of data, utilizing computing methods like CNN in real-time has become challenging. QCNNs overcome this challenge by utilizing qubits to represent data in a quantum environment and applying CNN structures to quantum computers. The time and accuracy of QCNNs are compared with classical CNNs and ANN models under different conditions such as batch size and input size. The maximum complexity level that QCNNs can handle in terms of these parameters is also investigated. The analysis shows that QCNNs have the potential to outperform both classical CNNs and ANN models in terms of accuracy and efficiency for certain applications, demonstrating their promise as a powerful tool in the field of machine learning.