A Quantum Convolutional Neural Network Approach for Object Detection and Classification

TL;DR

This paper evaluates Quantum Convolutional Neural Networks (QCNNs) and finds they can outperform classical models in accuracy and efficiency for object detection and classification tasks, especially with large data.

Contribution

It introduces a comprehensive comparison of QCNNs with classical CNNs and ANNs, highlighting their potential advantages in quantum machine learning applications.

Findings

QCNNs can outperform classical CNNs and ANNs in accuracy.

QCNNs demonstrate higher efficiency in processing large data.

Maximum complexity handling capacity of QCNNs is analyzed.

Abstract

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.