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:2209.12372

Scalable Quantum Convolutional Neural Networks

Hankyul Baek, Won Joon Yun, Joongheon Kim

Published Sep 26, 2022• 5 citations
arXivHTMLPDF

Abstract

With the beginning of the noisy intermediate-scale quantum (NISQ) era, quantum neural network (QNN) has recently emerged as a solution for the problems that classical neural networks cannot solve. Moreover, QCNN is attracting attention as the next generation of QNN because it can process high-dimensional vector input. However, due to the nature of quantum computing, it is difficult for the classical QCNN to extract a sufficient number of features. Motivated by this, we propose a new version of QCNN, named scalable quantum convolutional neural network (sQCNN). In addition, using the fidelity of QC, we propose an sQCNN training algorithm named reverse fidelity training (RF-Train) that maximizes the performance of sQCNN.

DOI
10.48550/arxiv.2209.12372

Related papers

3D Scalable Quantum Convolutional Neural Networks for Point Cloud Data Processing in Classification Applications

Hankyul Baek, Won Joon Yun, Joongheon Kim

With the beginning of the noisy intermediate-scale quantum (NISQ) era, a quantum neural network (QNN) has recently emerged as a solution for several specific problems that classical neural networks cannot solve. Moreover, a quantum convolutional neural network (QCNN) is the quantum-version of CNN because it can process high-dimensional vector inputs in contrast to QNN. However, due to the nature of quantum computing, it is difficult to scale up the QCNN to extract a sufficient number of features due to barren plateaus. Motivated by this, a novel 3D scalable QCNN (sQCNN-3D) is proposed for point cloud data processing in classification applications. Furthermore, reverse fidelity training (RF-Train) is additionally considered on top of sQCNN-3D for diversifying features with a limited number of qubits using the fidelity of quantum computing. Our data-intensive performance evaluation verifies that the proposed algorithm achieves desired performance.

arXiv:2210.09728•Oct 18, 2022•0 citations

Poster: Scalable Quantum Convolutional Neural Networks for Edge Computing

Jindi Wu, Qun Li

The convolutional neural network (CNN) has become a general approach for image processing in machine learning tasks. Quantum CNN (QCNN) is an emerging method to implement CNN using quantum computing. Quantum computing utilizes the properties of quantum mechanics to perform efficient computing. However, current quantum machines do not support large-scale QCNNs due to a lack of qubits. As a consequence, QCNNs are limited in scale and cannot directly process high-dimensional images. These shortcomings result in suboptimal QCNN performance. Meanwhile, building quantum machines with enough qubits is technically difficult and costly. These obstacles motivate us to design a quantum edge computing (QEC) system capable of achieving the scalability of QCNNs. Quantum machines are organized hierarchically in the QEC system. The quantum machines closer to the users collaboratively load and extract quantum features from the high-dimensional input data. Subsequently, the quantum machine in the next layer collects the extracted features and performs further operations to produce the final results. Each quantum machine in the QEC system is equipped with a local small-scale QCNN to capture the data pattern of its input. The local QCNNs could be combined to form a large-scale QCNN capable of learning and processing high-dimensional data, overcoming hardware limitations and improving performance.

IEEE/ACM Symposium on Edge Computing (SEC)•Dec 1, 2022•8 citations

FV-Train: Quantum Convolutional Neural Network Training with a Finite Number of Qubits by Extracting Diverse Features

Hankyul Baek, Won Joon Yun, Joongheon Kim

Quantum convolutional neural network (QCNN) has just become as an emerging research topic as we experience the noisy intermediate-scale quantum (NISQ) era and beyond. As convolutional filters in QCNN extract intrinsic feature using quantum-based ansatz, it should use only finite number of qubits to prevent barren plateaus, and it introduces the lack of the feature information. In this paper, we propose a novel QCNN training algorithm to optimize feature extraction while using only a finite number of qubits, which is called fidelity-variation training (FV-Training).

arXiv:2209.08727•Sep 19, 2022•1 citation

FV-Train: Quantum Convolutional Neural Network Training with a Finite Number of Qubits by Extracting Diverse Features (Student Abstract)

Hankyul Baek, Won Joon Yun, Joongheon Kim

Quantum convolutional neural network (QCNN) has just become as an emerging research topic as we experience the noisy intermediate-scale quantum (NISQ) era and beyond. As convolutional filters in QCNN extract intrinsic feature using quantum-based ansatz, it should use only finite number of qubits to prevent barren plateaus, and it introduces the lack of the feature information. In this paper, we propose a novel QCNN training algorithm to optimize feature extraction while using only a finite number of qubits, which is called fidelity-variation training (FV-Training).

Proceedings of the AAAI Conference on Artificial Intelligence•Jun 26, 2023•4 citations

A quantum convolutional neural network on NISQ devices

Shijie Wei, YanHu Chen, Zengrong Zhou +1 more

Abstract Quantum machine learning is one of the most promising applications of quantum computing in the noisy intermediate-scale quantum (NISQ) era. We propose a quantum convolutional neural network(QCNN) inspired by convolutional neural networks (CNN), which greatly reduces the computing complexity compared with its classical counterparts, with O (( log 2 M ) 6 ) basic gates and O ( m 2 + e ) variational parameters, where M is the input data size, m is the filter mask size, and e is the number of parameters in a Hamiltonian. Our model is robust to certain noise for image recognition tasks and the parameters are independent on the input sizes, making it friendly to near-term quantum devices. We demonstrate QCNN with two explicit examples. First, QCNN is applied to image processing, and numerical simulation of three types of spatial filtering, image smoothing, sharpening, and edge detection is performed. Secondly, we demonstrate QCNN in recognizing image, namely, the recognition of handwritten numbers. Compared with previous work, this machine learning model can provide implementable quantum circuits that accurately corresponds to a specific classical convolutional kernel. It provides an efficient avenue to transform CNN to QCNN directly and opens up the prospect of exploiting quantum power to process information in the era of big data.

arXiv:2104.06918•AAPPS bulletin•Jan 9, 2022•192 citations

Quantum-Eyes: Scalable Quantum Convolutional Neural Networks for Low-Overhead Object Detection [Research Frontier]

Joongheon Kim, Emily Jimin Roh, Chaemoon Im +1 more

Quantum neural networks (QNNs) are gaining attention as a promising foundation for next-generation machine learning. However, the practical deployment of QNNs, particularly in vision tasks, remains limited due to hardware constraints such as qubit scarcity. To address this challenge, this paper proposes a quantum convolutional neural network (QCNN)-based object detection framework designed for the noisy intermediate scale quantum (NISQ) era. The proposed method introduces four key components, patch processing, channel uploading, hybrid channel construction, and heterogeneous knowledge distillation, to enable efficient multi-channel object detection with a limited number of qubits. Furthermore, the knowledge distillation strategy facilitates the transfer of knowledge from a classical region proposal network (RPN) to its quantum counterpart, thereby enhancing detection accuracy despite the limitations of early-stage quantum machine learning. The evaluation results demonstrate the feasibility and efficiency of the proposed quantum object detection model, which can be effectively applied to complex vision tasks within existing quantum hardware limitations.

IEEE Computational Intelligence Magazine•Jul 14, 2025•2 citations
With the beginning of the noisy intermediate-scale quantum (NISQ) era, quantum neural network (QNN) has recently emerged as a solution for the problems that classical neural networks cannot solve. Moreover, QCNN is attracting attention as the next generation of QNN because it can process high-dimensional vector input. However, due to the nature of quantum computing, it is difficult for the classical QCNN to extract a sufficient number of features. Motivated by this, we propose a new version of QCNN, named scalable quantum convolutional neural network (sQCNN). In addition, using the fidelity of QC, we propose an sQCNN training algorithm named reverse fidelity training (RF-Train) that maximizes the performance of sQCNN.
With the beginning of the noisy intermediate-scale quantum (NISQ) era, a quantum neural network (QNN) has recently emerged as a solution for several specific problems that classical neural networks cannot solve. Moreover, a quantum convolutional neural network (QCNN) is the quantum-version of CNN because it can process high-dimensional vector inputs in contrast to QNN. However, due to the nature of quantum computing, it is difficult to scale up the QCNN to extract a sufficient number of features due to barren plateaus. Motivated by this, a novel 3D scalable QCNN (sQCNN-3D) is proposed for point cloud data processing in classification applications. Furthermore, reverse fidelity training (RF-Train) is additionally considered on top of sQCNN-3D for diversifying features with a limited number of qubits using the fidelity of quantum computing. Our data-intensive performance evaluation verifies that the proposed algorithm achieves desired performance.
The convolutional neural network (CNN) has become a general approach for image processing in machine learning tasks. Quantum CNN (QCNN) is an emerging method to implement CNN using quantum computing. Quantum computing utilizes the properties of quantum mechanics to perform efficient computing. However, current quantum machines do not support large-scale QCNNs due to a lack of qubits. As a consequence, QCNNs are limited in scale and cannot directly process high-dimensional images. These shortcomings result in suboptimal QCNN performance. Meanwhile, building quantum machines with enough qubits is technically difficult and costly. These obstacles motivate us to design a quantum edge computing (QEC) system capable of achieving the scalability of QCNNs. Quantum machines are organized hierarchically in the QEC system. The quantum machines closer to the users collaboratively load and extract quantum features from the high-dimensional input data. Subsequently, the quantum machine in the next layer collects the extracted features and performs further operations to produce the final results. Each quantum machine in the QEC system is equipped with a local small-scale QCNN to capture the data pattern of its input. The local QCNNs could be combined to form a large-scale QCNN capable of learning and processing high-dimensional data, overcoming hardware limitations and improving performance.
Quantum convolutional neural network (QCNN) has just become as an emerging research topic as we experience the noisy intermediate-scale quantum (NISQ) era and beyond. As convolutional filters in QCNN extract intrinsic feature using quantum-based ansatz, it should use only finite number of qubits to prevent barren plateaus, and it introduces the lack of the feature information. In this paper, we propose a novel QCNN training algorithm to optimize feature extraction while using only a finite number of qubits, which is called fidelity-variation training (FV-Training).
Quantum convolutional neural network (QCNN) has just become as an emerging research topic as we experience the noisy intermediate-scale quantum (NISQ) era and beyond. As convolutional filters in QCNN extract intrinsic feature using quantum-based ansatz, it should use only finite number of qubits to prevent barren plateaus, and it introduces the lack of the feature information. In this paper, we propose a novel QCNN training algorithm to optimize feature extraction while using only a finite number of qubits, which is called fidelity-variation training (FV-Training).
Abstract Quantum machine learning is one of the most promising applications of quantum computing in the noisy intermediate-scale quantum (NISQ) era. We propose a quantum convolutional neural network(QCNN) inspired by convolutional neural networks (CNN), which greatly reduces the computing complexity compared with its classical counterparts, with O (( log 2 M ) 6 ) basic gates and O ( m 2 + e ) variational parameters, where M is the input data size, m is the filter mask size, and e is the number of parameters in a Hamiltonian. Our model is robust to certain noise for image recognition tasks and the parameters are independent on the input sizes, making it friendly to near-term quantum devices. We demonstrate QCNN with two explicit examples. First, QCNN is applied to image processing, and numerical simulation of three types of spatial filtering, image smoothing, sharpening, and edge detection is performed. Secondly, we demonstrate QCNN in recognizing image, namely, the recognition of handwritten numbers. Compared with previous work, this machine learning model can provide implementable quantum circuits that accurately corresponds to a specific classical convolutional kernel. It provides an efficient avenue to transform CNN to QCNN directly and opens up the prospect of exploiting quantum power to process information in the era of big data.
Quantum neural networks (QNNs) are gaining attention as a promising foundation for next-generation machine learning. However, the practical deployment of QNNs, particularly in vision tasks, remains limited due to hardware constraints such as qubit scarcity. To address this challenge, this paper proposes a quantum convolutional neural network (QCNN)-based object detection framework designed for the noisy intermediate scale quantum (NISQ) era. The proposed method introduces four key components, patch processing, channel uploading, hybrid channel construction, and heterogeneous knowledge distillation, to enable efficient multi-channel object detection with a limited number of qubits. Furthermore, the knowledge distillation strategy facilitates the transfer of knowledge from a classical region proposal network (RPN) to its quantum counterpart, thereby enhancing detection accuracy despite the limitations of early-stage quantum machine learning. The evaluation results demonstrate the feasibility and efficiency of the proposed quantum object detection model, which can be effectively applied to complex vision tasks within existing quantum hardware limitations.