aqora

Pattern Recognition with Quantum Neural Networks

Alexandr Alexandrovich Ezhov

Lecture notes in computer scienceJan 1, 200120 citations

Quantum computation via neural networks applied to image processing and pattern recognition

Zhizhai Hu

Jan 1, 20014 citations

Handwritten digital recognition method based on quantum neural networks

Li Peng

The handwritten digital recognition is an important problem of pattern recognition field. An approach to handwritten digital recognition is presented based on multi-level transfer function quantum neural networks (QNN) and multi-layer classifiers. The QNN is trained and tested by the MNIST database. The experimental results indicate the method achieves excellent performance in terms of recognition rates and recognition reliability, and show the superiority and potential of QNN in solving pattern recognition problems.The handwritten digital recognition is an important problem of pattern recognition field. An approach to handwritten digital recognition is presented based on multi-level transfer function quantum neural networks (QNN) and multi-layer classifiers. The QNN is trained and tested by the MNIST database. The experimental results indicate the method achieves excellent performance in terms of recognition rates and recognition reliability, and show the superiority and potential of QNN in solving pattern recognition problems.

Jisuanji gongcheng yu shejiJan 1, 20072 citations

基于量子神经网络的非线性模式识别

Jincheng Zhang

This paper explores the application of quantum neural networks (QNNs) to enhance pattern recognition capabilities, particularly in the analysis of complex datasets. Traditional neural networks often struggle with non-linear data patterns due to limitations in their ability to capture intricate relationships. Quantum neural networks, leveraging quantum mechanical principles, offer a potential paradigm shift in pattern recognition by introducing inherent quantum properties that facilitate novel feature extraction and pattern understanding. We propose a novel QNN architecture designed to automatically learn hidden patterns and relationships within data, aiming to surpass the capabilities of conventional neural networks. This work investigates the potential of quantum-enhanced neural networks for identifying complex, non-linear patterns, demonstrating its efficacy through simulation and preliminary experimentation. The core mechanism involves utilizing quantum entanglement and superposition to represent and process data in a fundamentally different manner, ultimately leading to improved pattern recognition accuracy.This paper explores the application of quantum neural networks (QNNs) to enhance pattern recognition capabilities, particularly in the analysis of complex datasets. Traditional neural networks often struggle with non-linear data patterns due to limitations in their ability to capture intricate relationships. Quantum neural networks, leveraging quantum mechanical principles, offer a potential paradigm shift in pattern recognition by introducing inherent quantum properties that facilitate novel feature extraction and pattern understanding. We propose a novel QNN architecture designed to automatically learn hidden patterns and relationships within data, aiming to surpass the capabilities of conventional neural networks. This work investigates the potential of quantum-enhanced neural networks for identifying complex, non-linear patterns, demonstrating its efficacy through simulation and preliminary experimentation. The core mechanism involves utilizing quantum entanglement and superposition to represent and process data in a fundamentally different manner, ultimately leading to improved pattern recognition accuracy.

Sep 2, 20260 citations

PATTERN CLASSIFICATION BASED ON QUANTUM NEURAL NETWORKS: A CASE STUDY

N.B. Karayiannis, R. Kretzschmar, Hans Richner

WORLD SCIENTIFIC eBooksNov 1, 20015 citations

##基于量子神经网络的模式识别

Jincheng Zhang

This paper investigates the application of quantum neural networks (QNNs) for pattern recognition. Traditional neural networks face limitations in processing complex data due to their reliance on classical computing principles. QNNs leverage quantum mechanics phenomena, specifically superposition and entanglement, to potentially overcome these limitations. This research proposes a framework for constructing a QNN-based pattern recognition system. The core claim is to enhance pattern recognition accuracy and efficiency by utilizing these quantum properties. We detail the conversion process from classical neural networks to QNNs, highlighting the computational advantages offered by quantum computing. This work contributes to the nascent field of quantum machine learning, exploring the potential of QNNs to revolutionize pattern recognition tasks. The theoretical foundation presented here forms a basis for future research and development in this area.This paper investigates the application of quantum neural networks (QNNs) for pattern recognition. Traditional neural networks face limitations in processing complex data due to their reliance on classical computing principles. QNNs leverage quantum mechanics phenomena, specifically superposition and entanglement, to potentially overcome these limitations. This research proposes a framework for constructing a QNN-based pattern recognition system. The core claim is to enhance pattern recognition accuracy and efficiency by utilizing these quantum properties. We detail the conversion process from classical neural networks to QNNs, highlighting the computational advantages offered by quantum computing. This work contributes to the nascent field of quantum machine learning, exploring the potential of QNNs to revolutionize pattern recognition tasks. The theoretical foundation presented here forms a basis for future research and development in this area.

Aug 29, 20260 citations

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