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

Quantum Machine Intelligence

Exploring the features of quanvolutional neural networks for improved image classification

Vu Tuan Hai, Lawrence H. Le, The Bao Pham

Published May 13, 2024• 6 citations
Publisher
DOI
10.1007/s42484-024-00166-z

Related papers

Hybrid Neural Networks with Multi-channel Quanvolutional Input for Medical Image Classification

Yuri G. Gordienko, Arsenii Khmelnytskyi, Vlad Taran +1 more

Algorithms for intelligent systems•Jan 1, 2025•2 citations

Quanvolutional neural networks: powering image recognition with quantum circuits

Maxwell Henderson, Samriddhi Shakya, Shashindra Man Pradhan +1 more

arXiv:1904.04767•Quantum Machine Intelligence•Feb 27, 2020•514 citations

WITHDRAWN: Exploring the features of quanvolutional neural networks for improved image classification

Hai Vu Tuan, Lawrence H. Le, Bao Pham The

Abstract In several machine learning applications, image classification is a crucial problem. Numerous classification techniques based on quantum machine learning have been presented recently. In this article, we investigate the features of the quanvolutional neural network - a hybrid quantum-classical image classification technique inspired by the convolutional neural network which has the potential to outperform current image processing techniques. We improve the training strategy and evaluate the classification tasks on traditional datasets by different topologies, sizes, and depth of filters. Finally, we propose an efficient configuration for the quanvolutional neural network, making it a promising solution for image classification problems.

•Apr 26, 2023•0 citations

ICCAD Special Session Paper: Quantum-Classical Hybrid Machine Learning for Image Classification

Mahabubul Alam, Satwik Kundu, Rasit Onur Topaloglu +1 more

Image classification is a major application domain for conventional deep learning (DL). Quantum machine learning (QML) has the potential to revolutionize image classification. In any typical DL-based image classification, we use convolutional neural network (CNN) to extract features from the image and multi-layer perceptron network (MLP) to create the actual decision boundaries. On one hand, QML models can be useful in both of these tasks. Convolution with parameterized quantum circuits (Quanvolution) can extract rich features from the images. On the other hand, quantum neural network (QNN) models can create complex decision boundaries. Therefore, Quanvolution and QNN can be used to create an end-to-end QML model for image classification. Alternatively, we can extract image features separately using classical dimension reduction techniques such as, Principal Components Analysis (PCA) or Convolutional Autoencoder (CAE) and use the extracted features to train a QNN. We review two proposals on quantum-classical hybrid ML models for image classification namely, Quanvolutional Neural Network and dimension reduction using a classical algorithm followed by QNN. Particularly, we make a case for trainable filters in Quanvolution and CAE-based feature extraction for image datasets (instead of dimension reduction using linear transformations such as, PCA). We discuss various design choices, potential opportunities, and drawbacks of these models. We also release a Python-based framework to create and explore these hybrid models with a variety of design choices.

•Sep 7, 2021•4 citations

DEVELOPING AND EVALUATING A HYBRID QUANTUM CONVOLUTIONAL NEURAL NETWORK FOR ENHANCED IMAGE CLASSIFICATION

Fabiha Shahzad1, Dr Amnah Firdous*2, Shabbar Khan Saddozai3, Manahil Khan4, Muniba Saleem5

•May 26, 2026•0 citations

Quantum-Classical Hybrid Machine Learning for Image Classification (ICCAD Special Session Paper)

Mahabubul Alam, Satwik Kundu, Rasit Onur Topaloglu +1 more

Image classification is a major application domain for conventional deep learning (DL). Quantum machine learning (QML) has the potential to revolutionize image classification. In any typical DL-based image classification, we use convolutional neural network (CNN) to extract features from the image and multi-layer perceptron network (MLP) to create the actual decision boundaries. QML models can be useful in both of these tasks. On one hand, convolution with parameterized quantum circuits (Quanvolution) can extract rich features from the images. On the other hand, quantum neural network (QNN) models can create complex decision boundaries. Therefore, Quanvolution and QNN can be used to create an end-to-end QML model for image classification. Alternatively, we can extract image features separately using classical dimension reduction techniques such as, Principal Components Analysis (PCA) or Convolutional Autoen-coder (CAE) and use the extracted features to train a QNN. We review two proposals on quantum-classical hybrid ML models for image classification namely, Quanvolutional Neural Network and dimension reduction using a classical algorithm followed by QNN. Particularly, we make a case for trainable filters in Quanvolution and CAE-based feature extraction for image datasets (instead of dimension reduction using linear transformations such as, PCA). We discuss various design choices, potential opportunities, and drawbacks of these models. We also release a Python-based framework to create and explore these hybrid models with a variety of design choices.

arXiv:2109.02862•International Conference on Computer Aided Design•Nov 1, 2021•51 citations
Abstract In several machine learning applications, image classification is a crucial problem. Numerous classification techniques based on quantum machine learning have been presented recently. In this article, we investigate the features of the quanvolutional neural network - a hybrid quantum-classical image classification technique inspired by the convolutional neural network which has the potential to outperform current image processing techniques. We improve the training strategy and evaluate the classification tasks on traditional datasets by different topologies, sizes, and depth of filters. Finally, we propose an efficient configuration for the quanvolutional neural network, making it a promising solution for image classification problems.
Image classification is a major application domain for conventional deep learning (DL). Quantum machine learning (QML) has the potential to revolutionize image classification. In any typical DL-based image classification, we use convolutional neural network (CNN) to extract features from the image and multi-layer perceptron network (MLP) to create the actual decision boundaries. On one hand, QML models can be useful in both of these tasks. Convolution with parameterized quantum circuits (Quanvolution) can extract rich features from the images. On the other hand, quantum neural network (QNN) models can create complex decision boundaries. Therefore, Quanvolution and QNN can be used to create an end-to-end QML model for image classification. Alternatively, we can extract image features separately using classical dimension reduction techniques such as, Principal Components Analysis (PCA) or Convolutional Autoencoder (CAE) and use the extracted features to train a QNN. We review two proposals on quantum-classical hybrid ML models for image classification namely, Quanvolutional Neural Network and dimension reduction using a classical algorithm followed by QNN. Particularly, we make a case for trainable filters in Quanvolution and CAE-based feature extraction for image datasets (instead of dimension reduction using linear transformations such as, PCA). We discuss various design choices, potential opportunities, and drawbacks of these models. We also release a Python-based framework to create and explore these hybrid models with a variety of design choices.
Image classification is a major application domain for conventional deep learning (DL). Quantum machine learning (QML) has the potential to revolutionize image classification. In any typical DL-based image classification, we use convolutional neural network (CNN) to extract features from the image and multi-layer perceptron network (MLP) to create the actual decision boundaries. QML models can be useful in both of these tasks. On one hand, convolution with parameterized quantum circuits (Quanvolution) can extract rich features from the images. On the other hand, quantum neural network (QNN) models can create complex decision boundaries. Therefore, Quanvolution and QNN can be used to create an end-to-end QML model for image classification. Alternatively, we can extract image features separately using classical dimension reduction techniques such as, Principal Components Analysis (PCA) or Convolutional Autoen-coder (CAE) and use the extracted features to train a QNN. We review two proposals on quantum-classical hybrid ML models for image classification namely, Quanvolutional Neural Network and dimension reduction using a classical algorithm followed by QNN. Particularly, we make a case for trainable filters in Quanvolution and CAE-based feature extraction for image datasets (instead of dimension reduction using linear transformations such as, PCA). We discuss various design choices, potential opportunities, and drawbacks of these models. We also release a Python-based framework to create and explore these hybrid models with a variety of design choices.