QUANTUM CONVOLUTIONAL NEURAL NETWORKS: IMPLEMENTATION SPECIFICS IN TECHNICAL, NATURAL, AND SOCIO-ECONOMIC SYSTEMS

Abstract

The paper analyses and investigates the usage of quantum convolutional neural networks in technical, natural, and socio-economic systems. Quantum convolutional neural networks are a novel approach to information processing that is based on the principles of quantum mechanics and artificial intelligence. In technical systems, the potential of using quantum convolutional neural networks for solving complex tasks such as image processing, machine learning, and prediction has been explored. The results have shown that quantum convolutional neural networks can provide more accurate and faster computations compared to classical neural networks.

In natural systems, research has been conducted on the use of quantum convolutional neural networks for modeling and predicting complex natural processes. Their effectiveness in understanding genetic data, studying complex molecular structures, and analyzing ecological systems has been investigated. It has been found that quantum convolutional neural networks can deliver more precise and rapid results compared to conventional data processing methods. In socio-economic systems, the possibilities of employing quantum convolutional neural networks for social network analysis, financial market forecasting, and resource management have been studied. The application of quantum convolutional neural networks has the potential to enhance prediction accuracy and facilitate more effective decision-making in socio-economic systems. The research findings confirm that quantum convolutional neural networks have the potential to be utilized in various domains, including technical, natural, and socio-economic systems. They can achieve higher accuracy, processing speed, and predictive capabilities compared to traditional methods.