Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

Lecture notes in networks and systems

Optimization Techniques of Quantum Neural Network for Image Classification

Prateek Karkera, R Srivaramangai

Published Jan 1, 2024• 0 citations
Publisher
DOI
10.1007/978-3-031-64836-6_41

Related papers

Quantum Neural Network for Robust Image Classification: Applications to Medical and Benchmark Datasets

H. Ahmed, Baraa Tantawi, Malak Magdy +2 more

Springer eBooks•Jan 1, 2026•0 citations

Superposition-enhanced quantum neural network for multi-class image classification

Qi Bai, Xianliang Hu

Chinese Journal of Physics•Mar 18, 2024•18 citations

Application of Quantum Multi-classification Network in Image Processing

Jingru Qiu, Shuang Cong

Lecture notes in computer science•Aug 19, 2025•0 citations

Three quantum neural network architectures for multiclass image classification

Guoxin Qu, Xianglong Yu, Zhimin Wang +1 more

Quantum neural networks (QNNs) integrate the advantages of quantum information with the formidable capabilities of classical neural networks, exhibiting notable potential in image classification tasks. However, the performance of current QNNs in processing multi-class image classification tasks still leaves room for improvement. In this paper, we propose three QNN architectures to enhance the accuracy of multi-class image classification by utilizing techniques of overparameterization, dense pooling, and local feature extraction. These networks are capable of mapping conventional data to the amplitudes of quantum states via amplitude encoding, eschewing the need for traditional dimensionality reduction techniques. Following processing with parameterized quantum circuits (PQCs), we conduct quantum measurements to extract feature information. We validate the proposed architectures using the MNIST dataset for eight-class image classification problems. The experimental results unequivocally demonstrate that the three techniques we employed significantly enhance the accuracy of QNNs in multi-class image classification tasks.

International Conference on Algorithms, Network, and Communication Technology (ICANCT)•Mar 3, 2025•2 citations

Image Classification Using Quantum Machine Learning

Amrit Raj, Jayakumar Vaithiyashankar

Lecture notes in networks and systems•Oct 11, 2022•1 citation

Improving Image Classification with Quantum Neural Network

A S Reejisha, Anuraj Mohan

Image classification is a fundamental task in computer vision that involves assigning labels to images based on their content. In recent years, deep learning techniques based on classical neural networks (CNN) have achieved modern performance on many image classification tasks. However, there is growing interest in using quantum neural networks (QNNs) for image classification, as quantum computing has the potential to improve the accuracy and speed of computation. We demonstrate the potential of our QNN model and QCNN model by applying it to the fashion MNIST dataset. The results indicate that the QNN model exhibits faster training speed and higher testing set accuracy compared to a QCNN with a similar architecture.

International Conference on Computer, Electronics & Electrical Engineering & their Applications (IC2E3)•Jun 8, 2023•5 citations
Quantum neural networks (QNNs) integrate the advantages of quantum information with the formidable capabilities of classical neural networks, exhibiting notable potential in image classification tasks. However, the performance of current QNNs in processing multi-class image classification tasks still leaves room for improvement. In this paper, we propose three QNN architectures to enhance the accuracy of multi-class image classification by utilizing techniques of overparameterization, dense pooling, and local feature extraction. These networks are capable of mapping conventional data to the amplitudes of quantum states via amplitude encoding, eschewing the need for traditional dimensionality reduction techniques. Following processing with parameterized quantum circuits (PQCs), we conduct quantum measurements to extract feature information. We validate the proposed architectures using the MNIST dataset for eight-class image classification problems. The experimental results unequivocally demonstrate that the three techniques we employed significantly enhance the accuracy of QNNs in multi-class image classification tasks.
Image classification is a fundamental task in computer vision that involves assigning labels to images based on their content. In recent years, deep learning techniques based on classical neural networks (CNN) have achieved modern performance on many image classification tasks. However, there is growing interest in using quantum neural networks (QNNs) for image classification, as quantum computing has the potential to improve the accuracy and speed of computation. We demonstrate the potential of our QNN model and QCNN model by applying it to the fashion MNIST dataset. The results indicate that the QNN model exhibits faster training speed and higher testing set accuracy compared to a QCNN with a similar architecture.