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

arXiv:2203.15368 · Frontiers in Physics

Multiclass classification using quantum convolutional neural networks with hybrid quantum-classical learning

Denis Bokhan, Alena S. Mastiukova, Aleksey S. Boev, Dmitrii N. Trubnikov, Aleksey K. Fedorov

Published Nov 23, 2022• 81 citations
arXivHTMLPDF

Abstract

Multiclass classification is of great interest for various applications, for example, it is a common task in computer vision, where one needs to categorize an image into three or more classes. Here we propose a quantum machine learning approach based on quantum convolutional neural networks for solving the multiclass classification problem. The corresponding learning procedure is implemented via TensorFlowQuantum as a hybrid quantum-classical (variational) model, where quantum output results are fed to the softmax activation function with the subsequent minimization of the cross entropy loss via optimizing the parameters of the quantum circuit. Our conceptional improvements here include a new model for a quantum perceptron and an optimized structure of the quantum circuit. We use the proposed approach to solve a 4-class classification problem for the case of the MNIST dataset using eight qubits for data encoding and four ancilla qubits; previous results have been obtained for 3-class classification problems. Our results show that the accuracy of our solution is similar to classical convolutional neural networks with comparable numbers of trainable parameters. We expect that our findings will provide a new step toward the use of quantum neural networks for solving relevant problems in the NISQ era and beyond.

DOI
10.3389/fphy.2022.1069985

Related papers

Hybrid Classic-Quantum Neural Networks for Image Classification

Yevhenii Trochun, Sergii Stirenko, Oleksandr Rokovyi +3 more

In the last decade, quantum computing (QC) has shown its great potential in advancing research in many fields. Here we introduce a new quantum-classical neural network, by combining quantum computing and classical computing in a hybrid neural network (HNN) that can be trained to perform image classification. The HNN on the basis of the classic convolutional neural network (CNN) with quantum circuit is considered for image classification problem. The various configurations of HNN were investigated where QC with different number of qubits were used and compared. The HNN configurations were trained, validated, and tested on the more complex CIFAR10 and CIFAR100 datasets in addition to our previous attempts on the simpler MNIST, notMNIST, MNIST Fashion datasets. Performance of HNN was compared for multiclass classification on these datasets for different number of classes (from 2 to 10) using QCs with correspondent number of qubits (from 2 to 4). The metrics measured (accuracy and loss) during these experiments support our assumption about feasibility of HNN application for multiclass classification problems.

IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS)•Sep 22, 2021•21 citations

Quantum Convolutional Neural Network Architecture for Multi-Class Classification

Samarth Kashyap, Shayan Srinivasa Garani

We propose quantum circuit architectures for convolutional neural networks based on generalized 3-qubit and 2-qubit quantum gates for the multiclass classification problem. The quantum architecture is equivalent to a classical convolutional neural network with fully connected layers and densely connected layers. The quantum circuit parameters are optimized by minimizing the cross-entropy loss function. We validate the classification performance over several model configurations on the MNIST, Fashion-MNIST and Kuzushiji-MNIST datasets. Our proposed architecture shows classification accuracies that are comparable to classical CNNs with a similar number of parameters. In addition to this, we find that circuit depth is greatly decreased by a logarithmic factor compared to classical CNNs. We study the performance and complexity tradeoffs over several model configurations within the proposed quantum CNN architecture.

International Joint Conference on Neural Networks (IJCNN)•Jun 18, 2023•7 citations

Impact of Hybrid Neural Network Structure on Performance of Multiclass Classification

Yevhenii Trochun, Evgen Pavlov, Sergii Stirenko +1 more

This article describes hybrid convolutional neural network that uses one quantum circuit for image classification. The different configurations of the hybrid neural network with the quantum circuit are considered. Several different quantum circuits with different number of qubits are compared. These hybrid neural network configurations are evaluated on MNIST and MNIST Fashion datasets, which is radically different from MNIST dataset. Performance of hybrid neural network is compared for multiclass classification on MNIST and MNIST Fashion datasets for 4, 6, 8, 10 classes using quantum circuits with 2, 3, 4 qubits. The results of the experiments indicate the feasibility of using hybrid neural networks for multiclass classification.

IEEE EUROCON - International Conference on Computer as a Tool (EUROCON)•Jul 6, 2021•8 citations

Multi-Class Quantum Convolutional Neural Networks

Marco Mordacci, Davide Ferrari, Michele Amoretti

Classification is particularly relevant to Information Retrieval, as it is used in various subtasks of the search pipeline. In this work, we propose a quantum convolutional neural network (QCNN) for multi-class classification of classical data. The model is implemented using PennyLane. The optimization process is conducted by minimizing the cross-entropy loss through parameterized quantum circuit optimization. The QCNN is tested on the MNIST dataset with 4, 6, 8 and 10 classes. The results show that with 4 classes, the performance is slightly lower compared to the classical CNN, while with a higher number of classes, the QCNN outperforms the classical neural network.

arXiv:2404.12741•Jun 3, 2024•9 citations

Quantum machine learning for image classification

Arsenii Senokosov, Alexandr Sedykh, Asel Sagingalieva +2 more

Abstract Image classification, a pivotal task in multiple industries, faces computational challenges due to the burgeoning volume of visual data. This research addresses these challenges by introducing two quantum machine learning models that leverage the principles of quantum mechanics for effective computations. Our first model, a hybrid quantum neural network with parallel quantum circuits, enables the execution of computations even in the noisy intermediate-scale quantum era, where circuits with a large number of qubits are currently infeasible. This model demonstrated a record-breaking classification accuracy of 99.21% on the full MNIST dataset, surpassing the performance of known quantum–classical models, while having eight times fewer parameters than its classical counterpart. Also, the results of testing this hybrid model on a Medical MNIST (classification accuracy over 99%), and on CIFAR-10 (classification accuracy over 82%), can serve as evidence of the generalizability of the model and highlights the efficiency of quantum layers in distinguishing common features of input data. Our second model introduces a hybrid quantum neural network with a Quanvolutional layer, reducing image resolution via a convolution process. The model matches the performance of its classical counterpart, having four times fewer trainable parameters, and outperforms a classical model with equal weight parameters. These models represent advancements in quantum machine learning research and illuminate the path towards more accurate image classification systems.

arXiv:2304.09224•Machine Learning Science and Technology•Feb 20, 2024•181 citations

QcNet: A Hybrid Quantum-Classical Neural Network for Multi-Class Image Classification

Sakthi Prabhu Gunasekar, Chatterjee Subarna

Quantum Computing is a very promising paradigm given the ability that Quantum brings in terms of recognizing patterns which their classical counterparts very likely fail at.The intersection of Quantum Computing and Machine Learning gave rise to Quantum Machine Learning, which brings the power of Quantum mechanics to accelerate the progress of Machine Learning. However, Quantum Machine Learning still has its own set of challenges, given the state of Quantum Hardware currently. Here we propose QcNet: A Hybrid Quantum-Classical Neural Network which combines a tensor network with a parameterized quantum circuit. Here we modify the Quantum Neural Network by placing the parametrized quantum circuit convolutional layer at the top followed by some classical CNN layers to make it more feasible for the near term NISQ era and to make it scale to 43 classes. The Quantum Convolutional Layer extracts the higher level features and passes it to the classical layers for further processing. Our model is an end-to-end solution to train a quantum-style model over a multi-class supervised learning problem. This model pushes the limits beyond binary or few class classification and successfully performs well over multiclass classification tasks over 43 classes of the GTSRB dataset. Also it is highly flexible and can be adopted well in the NISQ era, considering the amount of quantum resources available.

International Conference on Inventive Research in Computing Applications (ICIRCA)•Jun 25, 2025•1 citation
Multiclass classification is of great interest for various applications, for example, it is a common task in computer vision, where one needs to categorize an image into three or more classes. Here we propose a quantum machine learning approach based on quantum convolutional neural networks for solving the multiclass classification problem. The corresponding learning procedure is implemented via TensorFlowQuantum as a hybrid quantum-classical (variational) model, where quantum output results are fed to the softmax activation function with the subsequent minimization of the cross entropy loss via optimizing the parameters of the quantum circuit. Our conceptional improvements here include a new model for a quantum perceptron and an optimized structure of the quantum circuit. We use the proposed approach to solve a 4-class classification problem for the case of the MNIST dataset using eight qubits for data encoding and four ancilla qubits; previous results have been obtained for 3-class classification problems. Our results show that the accuracy of our solution is similar to classical convolutional neural networks with comparable numbers of trainable parameters. We expect that our findings will provide a new step toward the use of quantum neural networks for solving relevant problems in the NISQ era and beyond.
In the last decade, quantum computing (QC) has shown its great potential in advancing research in many fields. Here we introduce a new quantum-classical neural network, by combining quantum computing and classical computing in a hybrid neural network (HNN) that can be trained to perform image classification. The HNN on the basis of the classic convolutional neural network (CNN) with quantum circuit is considered for image classification problem. The various configurations of HNN were investigated where QC with different number of qubits were used and compared. The HNN configurations were trained, validated, and tested on the more complex CIFAR10 and CIFAR100 datasets in addition to our previous attempts on the simpler MNIST, notMNIST, MNIST Fashion datasets. Performance of HNN was compared for multiclass classification on these datasets for different number of classes (from 2 to 10) using QCs with correspondent number of qubits (from 2 to 4). The metrics measured (accuracy and loss) during these experiments support our assumption about feasibility of HNN application for multiclass classification problems.
We propose quantum circuit architectures for convolutional neural networks based on generalized 3-qubit and 2-qubit quantum gates for the multiclass classification problem. The quantum architecture is equivalent to a classical convolutional neural network with fully connected layers and densely connected layers. The quantum circuit parameters are optimized by minimizing the cross-entropy loss function. We validate the classification performance over several model configurations on the MNIST, Fashion-MNIST and Kuzushiji-MNIST datasets. Our proposed architecture shows classification accuracies that are comparable to classical CNNs with a similar number of parameters. In addition to this, we find that circuit depth is greatly decreased by a logarithmic factor compared to classical CNNs. We study the performance and complexity tradeoffs over several model configurations within the proposed quantum CNN architecture.
This article describes hybrid convolutional neural network that uses one quantum circuit for image classification. The different configurations of the hybrid neural network with the quantum circuit are considered. Several different quantum circuits with different number of qubits are compared. These hybrid neural network configurations are evaluated on MNIST and MNIST Fashion datasets, which is radically different from MNIST dataset. Performance of hybrid neural network is compared for multiclass classification on MNIST and MNIST Fashion datasets for 4, 6, 8, 10 classes using quantum circuits with 2, 3, 4 qubits. The results of the experiments indicate the feasibility of using hybrid neural networks for multiclass classification.
Classification is particularly relevant to Information Retrieval, as it is used in various subtasks of the search pipeline. In this work, we propose a quantum convolutional neural network (QCNN) for multi-class classification of classical data. The model is implemented using PennyLane. The optimization process is conducted by minimizing the cross-entropy loss through parameterized quantum circuit optimization. The QCNN is tested on the MNIST dataset with 4, 6, 8 and 10 classes. The results show that with 4 classes, the performance is slightly lower compared to the classical CNN, while with a higher number of classes, the QCNN outperforms the classical neural network.
Abstract Image classification, a pivotal task in multiple industries, faces computational challenges due to the burgeoning volume of visual data. This research addresses these challenges by introducing two quantum machine learning models that leverage the principles of quantum mechanics for effective computations. Our first model, a hybrid quantum neural network with parallel quantum circuits, enables the execution of computations even in the noisy intermediate-scale quantum era, where circuits with a large number of qubits are currently infeasible. This model demonstrated a record-breaking classification accuracy of 99.21% on the full MNIST dataset, surpassing the performance of known quantum–classical models, while having eight times fewer parameters than its classical counterpart. Also, the results of testing this hybrid model on a Medical MNIST (classification accuracy over 99%), and on CIFAR-10 (classification accuracy over 82%), can serve as evidence of the generalizability of the model and highlights the efficiency of quantum layers in distinguishing common features of input data. Our second model introduces a hybrid quantum neural network with a Quanvolutional layer, reducing image resolution via a convolution process. The model matches the performance of its classical counterpart, having four times fewer trainable parameters, and outperforms a classical model with equal weight parameters. These models represent advancements in quantum machine learning research and illuminate the path towards more accurate image classification systems.
Quantum Computing is a very promising paradigm given the ability that Quantum brings in terms of recognizing patterns which their classical counterparts very likely fail at.The intersection of Quantum Computing and Machine Learning gave rise to Quantum Machine Learning, which brings the power of Quantum mechanics to accelerate the progress of Machine Learning. However, Quantum Machine Learning still has its own set of challenges, given the state of Quantum Hardware currently. Here we propose QcNet: A Hybrid Quantum-Classical Neural Network which combines a tensor network with a parameterized quantum circuit. Here we modify the Quantum Neural Network by placing the parametrized quantum circuit convolutional layer at the top followed by some classical CNN layers to make it more feasible for the near term NISQ era and to make it scale to 43 classes. The Quantum Convolutional Layer extracts the higher level features and passes it to the classical layers for further processing. Our model is an end-to-end solution to train a quantum-style model over a multi-class supervised learning problem. This model pushes the limits beyond binary or few class classification and successfully performs well over multiclass classification tasks over 43 classes of the GTSRB dataset. Also it is highly flexible and can be adopted well in the NISQ era, considering the amount of quantum resources available.