Quantum-Enhanced Convolutional Neural Networks for Image Classification

Tariq Qayyum, Asadullah Tariq, Muhammad Waqad Haseeb, Abderrehmane Lakas, Mohamed Adel Serhani, Zouheir Trabelsi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In recent years, there has been a growing interest in leveraging the unique properties of quantum computing to develop novel machine learning algorithms and architectures. This research paper presents an investigation of quantum convolutional neural networks (QCNNs), which leverage the unique properties of quantum computing to potentially improve the accuracy and efficiency of image classification tasks. Specifically, the paper explores three different QCNN architectures, including a pure quantum-based QCNN, a hybrid QCNN with a single quantum convolution layer, and a hybrid convolutional architecture with multiple quantum filters. We tested the models on MNIST dataset and the results of the study demonstrate that hybrid architectures that combine quantum and classical processing are more effective than pure quantum-based architectures in image classification tasks. In particular, the third model, the Hybrid Convolution with Multiple Quantum Filters, achieved the highest test set accuracy of 92.7%. The use of multiple quantum filters in conjunction with a classical neural network resulted in enhanced accuracy and efficiency in image classification tasks, highlighting the potential of hybrid architectures for future applications in machine learning tasks.

Original languageEnglish
Title of host publicationProceedings - 2023 IEEE Future Networks World Forum
Subtitle of host publicationFuture Networks: Imagining the Network of the Future, FNWF 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350324587
DOIs
Publication statusPublished - 2023
Event6th IEEE Future Networks World Forum, FNWF 2023 - Baltimore, United States
Duration: Nov 13 2023Nov 15 2023

Publication series

NameProceedings - 2023 IEEE Future Networks World Forum: Future Networks: Imagining the Network of the Future, FNWF 2023

Conference

Conference6th IEEE Future Networks World Forum, FNWF 2023
Country/TerritoryUnited States
CityBaltimore
Period11/13/2311/15/23

Keywords

  • CIRQ
  • QCNN
  • Quantum Computing
  • Quantum Convolutional Network

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Hardware and Architecture
  • Safety, Risk, Reliability and Quality

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