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PATTERN CLASSIFICATION BASED ON QUANTUM NEURAL NETWORKS: A CASE STUDY

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

WORLD SCIENTIFIC eBooksNov 1, 20015 citations

FROM CLASSICAL NEURAL NETWORKS TO QUANTUM NEURAL NETWORKS

Brunello Tirozzi

Sep 27, 20120 citations

Recurrent Quantum Neural Network and its Applications

Laxmidhar Behera, Indrani Kar, Avshalom C. Elitzur

Springer eBooksSep 4, 200612 citations

RECURRENT QUANTUM NEURAL NETWORKS: A REVIEW

Gleydson Fernandes de Jesus, Valéria Loureiro da Silva

The analysis of historical data allows the execution of predictive tasks such as weather and stock price forecasting. To achieve these goals, Recurrent Neural Networks are implemented in classical computers and, in recent years, quantum methods have also emerged to perform prediction tasks based on the analysis of historical series, which have been called Quantum Recurrent Neural Network (QRNN). The objective of this work is to identify and review the main QRNNs discussed in the literature. A literature search in google scholar resulted in eight relevant papers that were reviewed. In general, the QRNNs show better training accuracy and stability compared to classical methods. It is not possible to speak of a training time advantage with the noisy and low-scale quantum computers currently available.The analysis of historical data allows the execution of predictive tasks such as weather and stock price forecasting. To achieve these goals, Recurrent Neural Networks are implemented in classical computers and, in recent years, quantum methods have also emerged to perform prediction tasks based on the analysis of historical series, which have been called Quantum Recurrent Neural Network (QRNN). The objective of this work is to identify and review the main QRNNs discussed in the literature. A literature search in google scholar resulted in eight relevant papers that were reviewed. In general, the QRNNs show better training accuracy and stability compared to classical methods. It is not possible to speak of a training time advantage with the noisy and low-scale quantum computers currently available.

Oct 1, 20231 citation

Fundamentals of Recurrent Quantum Neural Networks

Vaibhav Gandhi

Elsevier eBooksJan 1, 20157 citations

HYBRID QUANTUM-CLASSICAL NEURAL NETWORKS: BRIDGING CLASSICAL AND QUANTUM COMPUTING PARADIGMS

Karthikeyan Rajamani

The emerging field of Hybrid Quantum-Classical Neural Networks (HQCNNs) represents a groundbreaking fusion of quantum computing capabilities with traditional neural network architectures.HQCNNs leverage the quantum advantages of superposition states and high-dimensional Hilbert spaces while maintaining the robust pattern recognition capabilities of classical networks.By combining parameterized quantum circuits with classical optimization methods, these hybrid systems demonstrate remarkable potential across multiple domains, from molecular simulations to financial modeling.The emerging field of Hybrid Quantum-Classical Neural Networks (HQCNNs) represents a groundbreaking fusion of quantum computing capabilities with traditional neural network architectures.HQCNNs leverage the quantum advantages of superposition states and high-dimensional Hilbert spaces while maintaining the robust pattern recognition capabilities of classical networks.By combining parameterized quantum circuits with classical optimization methods, these hybrid systems demonstrate remarkable potential across multiple domains, from molecular simulations to financial modeling.

INTERNATIONAL JOURNAL OF ADVANCED RESEARCH IN ENGINEERING & TECHNOLOGYFeb 28, 20251 citation

RECURRENT QUANTUM NEURAL NETWORKS: A PARADIGM FOR SUBJECTIVE COMPUTING MODEL

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