Development of Quantum Algorithms for Neural Network Optimization in Big Data Analysis

Abstrak

This study explores the development and implementation of quantum annealing algorithms for optimizing neural networks in big data analysis. The background highlights the computational challenges faced by traditional optimization techniques in handling large-scale datasets and the potential of quantum computing to overcome these challenges. The research objective is to demonstrate the effectiveness of quantum annealing algorithms in improving training time, convergence rates, and predictive accuracy of neural networks. Methodologically, the study employs a quantitative approach, utilizing simulation experiments and empirical data analysis to evaluate the performance of quantum-enhanced optimization techniques. The results indicate significant reductions in training time, accelerated convergence rates, and improved predictive accuracy, showcasing the potential of quantum computing to enhance machine learning models for big data analytics. These findings have implications for various industries reliant on data-driven decision-making, paving the way for transformative developments in computational intelligence and quantum-enhanced machine learning.