Quantum convolutional neural network for image classification

Chen, Guo-Ming; Chen, Qiang; Long, Shun; Zhu, Weiheng; Yuan, Zeduo; Wu, Yilin

doi:10.1007/s10044-022-01113-z

Cited by 47 publications

(20 citation statements)

References 21 publications

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“…The way to implement pooling layers is to measure a qubit subset of the qubits and then use the measurement to control the following operations. Pooling layers are an important component of quantum convolutional networks [ 66 ]. Measurement Layers.…”

Section: Quantum Neural Network Technologies and Methodologiesmentioning

confidence: 99%

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Programming Quantum Neural Networks on NISQ Systems: An Overview of Technologies and Methodologies

Markidis

2023

Entropy

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Noisy Intermediate-Scale Quantum (NISQ) systems and associated programming interfaces make it possible to explore and investigate the design and development of quantum computing techniques for Machine Learning (ML) applications. Among the most recent quantum ML approaches, Quantum Neural Networks (QNN) emerged as an important tool for data analysis. With the QNN advent, higher-level programming interfaces for QNN have been developed. In this paper, we survey the current state-of-the-art high-level programming approaches for QNN development. We discuss target architectures, critical QNN algorithmic components, such as the hybrid workflow of Quantum Annealers and Parametrized Quantum Circuits, QNN architectures, optimizers, gradient calculations, and applications. Finally, we overview the existing programming QNN frameworks, their software architecture, and associated quantum simulators.

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Section: Quantum Neural Network Technologies and Methodologiesmentioning

confidence: 99%

“…The QNN framework has found applications in image classification [ 87 ], cyber-security [ 88 ], medical [ 89 ], and high-energy physics problems [ 90 , 91 ]. has been used for image classification [ 66 ], remote sensing [ 92 ], and medical applications [ 93 ].…”

Section: Quantum Neural Network Technologies and Methodologiesmentioning

confidence: 99%

Programming Quantum Neural Networks on NISQ Systems: An Overview of Technologies and Methodologies

Markidis

2023

Entropy

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“…Quantum convolutional neural network (QCNN) [27] can recognize specifc features of quantum states. It is signifcant to study the combination of local features and the global QCNN circuit structure and that of the bidirectional contributions.…”

Section: Quantum Convolutional Neural Networkmentioning

confidence: 99%

Robustness and Explainability of Image Classification Based on QCNN

Chen

Long

Yuan

et al. 2023

Quantum Engineering

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In this paper, we propose a multiscale entanglement renormalization ansatz (MERA) feature extraction method based on a novel quantum convolutional neural network (QCNN) for binary scanning tunneling microscopy (STM) image classification. We design QCNN quantum circuits for state preparation, quantum convolution, and quantum pooling in the TensorFlow quantum framework and compare the performance of QCNN classifier and two hybrid quantum-classical QCNN models. Adversarial attacks are considered as a type of interpretable method to evaluate the robustness of QCNN models. The similarity between the pixels of image bitplane slicing and Ising phase transition opens up new ways for exploring classification performance enhancement by QCNN classifiers. Classification performance of different bitplanes of QCNN also shows that they can robustly resist adversarial attacks such as FGSM, CW, JSMA, and DEEPFOOL.

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“…Due to the weak spectral absorption intensity of organic matter in water, low signal-to-noise ratio, and vulnerability to external conditions, the detection accuracy of existing quantitative analysis models based on NIR is restricted. Therefore, researchers have proposed many valuable algorithms, such as using the continuous projection algorithm (SPA) to filter the characteristic wavelength of the full spectrum of water samples [2] , establishing a least squares support vector machine model [3] , using near-infrared transmission spectrum and moving The windowed partial least squares (MWPLS) method is used to select the band for near-infrared spectral analysis of wastewater chemical oxygen demand [4] . Some scholars combine the improved deep convolutional generative adversarial neural network to handle the solution of rice variety classification [5] , but it was not used for testing on sewage samples.…”

Section: Introductionmentioning

confidence: 99%

Research and design of near infrared spectroscopy wastewater COD analysis system based on 1D-CNN

Li,

Lin,

Hou

2024

International Conference on Frontiers of Applied Optics and Computer Engineering (AOCE 2024)

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Chemical Oxygen Demand (COD) serves as a pivotal parameter for assessing water quality in wastewater. Traditional chemical detection methods are time-consuming and prone to secondary pollution of the environment. Near-infrared spectroscopy technology as an alternative to traditional chemical detection methods is a viable approach. However, the experimental data shows data overfitting and high interference within the sample dataset parameters，which has poor applicability in traditional quantitative and qualitative analysis methods. In this paper, a one-dimensional Convolutional Neural Network (1D-CNN) method is used to establish a relationship model between near-infrared features of water samples and the COD, which has the advantages of moderate depth, small parameter amount, and fast network training speed. It is extremely suitable for rapid detection of samples which can effectively solve the above problems. Experimental results show that the proposed model is better than random forest classification, SGD Classifier, and support vector Machine regression(SVM).

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Quantum convolutional neural network for image classification

Cited by 47 publications

References 21 publications

Programming Quantum Neural Networks on NISQ Systems: An Overview of Technologies and Methodologies

Programming Quantum Neural Networks on NISQ Systems: An Overview of Technologies and Methodologies

Robustness and Explainability of Image Classification Based on QCNN

Research and design of near infrared spectroscopy wastewater COD analysis system based on 1D-CNN

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