Enhancing Quantum Computing's Machine Learning Performance
Vol. 2 , Issue 2 (2024) · pp. 58-66
Abstract
The exponential growth of data across diverse domains such as healthcare, finance, agriculture, remote sensing, engineering, and public administration has created an urgent demand for advanced computational paradigms capable of extracting meaningful insights from massive and complex datasets. Traditional machine learning and deep learning techniques have demonstrated remarkable success in handling such data; however, their performance is increasingly constrained by computational complexity, energy consumption, scalability limitations, and long training times. These challenges are particularly evident in data-intensive models such as deep neural networks, convolutional neural networks, and large-scale optimization systems. As datasets continue to grow in size and dimensionality, conventional computing architectures struggle to maintain efficiency, motivating the exploration of alternative computing paradigms. In this context, quantum computing has emerged as a promising technology with the potential to significantly enhance machine learning performance.