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Quantum Science and Technology

A modified lightweight quantum convolutional neural network for malicious code detection

Qibing Xiong, Yangyang Fei, Qiming Du, Bo Zhao, Shiqin Di, Zheng Shan

Published Sep 27, 2024• 3 citations
Publisher

Abstract

Abstract Quantum neural network fully utilize the respective advantages of quantum computing and classical neural network, providing a new path for the development of artificial intelligence. In this paper, we propose a modified lightweight quantum convolutional neural network (QCNN), which contains a high-scalability and parameterized quantum convolutional layer and a quantum pooling circuit with quantum bit multiplexing, effectively utilizing the computational advantages of quantum systems to accelerate classical machine learning tasks. The experimental results show that the classification accuracy (precision, F1-score) of this QCNN on DataCon2020, Ember and BODMAS have been improved to 96.65% (94.3%, 96.74%), 92.4% (91.01%, 92.53%) and 95.6% (91.99%, 95.78%), indicating that this QCNN has strong robustness as well as good generalization performance for malicious code detection, which is of great significance to cyberspace security.

DOI
10.1088/2058-9565/ad80bd

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Abstract Quantum neural network fully utilize the respective advantages of quantum computing and classical neural network, providing a new path for the development of artificial intelligence. In this paper, we propose a modified lightweight quantum convolutional neural network (QCNN), which contains a high-scalability and parameterized quantum convolutional layer and a quantum pooling circuit with quantum bit multiplexing, effectively utilizing the computational advantages of quantum systems to accelerate classical machine learning tasks. The experimental results show that the classification accuracy (precision, F1-score) of this QCNN on DataCon2020, Ember and BODMAS have been improved to 96.65% (94.3%, 96.74%), 92.4% (91.01%, 92.53%) and 95.6% (91.99%, 95.78%), indicating that this QCNN has strong robustness as well as good generalization performance for malicious code detection, which is of great significance to cyberspace security.
The rapid advancement of quantum computing technology has enabled new approaches to developing sophisticated machine learning models. In this paper, we present a multi-class Quantum Convolutional Neural Network (QCNN) architecture enhanced with strongly entangling circuits. The research targets the improvement of model's representation advantage and efficiency in solving classifications. The proposed method combines amplitude encoding with quantum convolutional and pooling layers or QCNN, augmented by strongly entangling layers to capture the data patterns. Experimental results show that our QCNN achieves higher classification accuracy with fewer parameters compared to traditional quantum models on the MNIST dataset. It highlights the potential of our method in complex multi-class classification.
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Artificial neural networks have achieved great success in many fields ranging from image recognition to video understanding. However, its high requirements for computing and memory resources have limited further development on processing big data with high dimensions. In recent years, advances in quantum computing show that building neural networks on quantum processors is a potential solution to this problem. In this paper, we propose a novel neural network model named Quantum Convolutional Neural Network (QCNN), aiming at utilizing the computing power of quantum systems to accelerate classical machine learning tasks. The designed QCNN is based on implementable quantum circuits and has a similar structure as classical convolutional neural networks. Numerical simulation results on the MNIST dataset demonstrate the effectiveness of our model.
Brain tumor detection is a vital and challenging task in medical diagnosis, requiring highly precise and efficient methods to improve patient treatment outcomes. Traditional approaches rely on classical machine learning and deep learning techniques, which, while effective to a degree, often struggle with capturing the complex and high-dimensional patterns present in medical imaging data. A significant limitation of these conventional methods is their inability to process and analyze intricate features efficiently, which can impact diagnostic accuracy. To address these challenges, this work proposes a novel Quantum Convolutional Neural Network (QCNN) framework that combines quantum computing principles, via quantum circuits, with classical machine learning architectures implemented through tools like PyTorch and PennyLane. This hybrid approach enhances feature extraction and classification capabilities, leading to superior performance. Experimental results demonstrate that the QCNN model achieves an improved accuracy of 90 percent on the test dataset, surpassing traditional methods and showing promise for advancing brain tumor detection technology. This improvement is supported by consistent decreases in training and validation loss during model training, highlighting the robustness and efficacy of the quantum-enhanced model.
With the rapid development of quantum computing, a variety of quantum convolutional neural networks (QCNNs) are proposed. However, only 1 / 2 n 2 features of an n -qubits input are transferred to the next layer in a quantum pooling layer, which results in the accuracy reduction. To solve this problem, a QCNN with a degressive circuit is proposed. In order to enhance the ability of extracting global features, we remove the parameters sharing strategy in the quantum convolutional layer and design a quantum convolutional kernel with global eyesight. In addition, to prevent a sharp feature reduction, a degressive parameterized quantum circuit is adopted to construct the pooling layer. Then the Z -basis measurement is only performed on the first qubit to control the operations on other qubits. Compared with the state-of-the-art QCNN, i.e., hybrid quantum-classical convolutional neural network, the accuracy of our model increased by 0.9%, 1%, and 3%, respectively, in three tasks: quantum state classification, binary code recognition, and quaternary code recognition.
In this work, quantum convolutional neural networks are considered in the task of recognizing handwritten digits. A proprietary quantum scheme for the convolutional layer of a quantum convolutional neural network is proposed. A proprietary quantum scheme for the pooling layer of a quantum convolutional neural network is proposed. The results of learning quantum convolutional neural networks are analyzed. The built models were compared and the best one was selected based on the accuracy, recall, precision and f1-score metrics. A comparative analysis was made with the classic convolutional neural network based on accuracy, recall, precision and f1-score metrics. The object of the study is the task of recognizing numbers. The subject of research is convolutional neural network, quantum convolutional neural network. The result of this work can be applied in the further research of quantum computing in the tasks of artificial intelligence.