کنفرانس بین المللی میکروالکترونیک ایران

صفحه اصلی / ششمین کنفرانس بین المللی میکروالکترونیک ایران

Quantum Machine Learning Acceleration with Quantum Control Techniques

نویسندگان :

Sara Mahmoudi Rashid¹

1- University of Tabriz

کلمات کلیدی :

Quantum computing،Machine learning،Quantum control techniques،Support vector machines (SVMs)،Computational efficiency،Power reduction

چکیده :

The integration of quantum computing with machine learning represents a significant frontier in computational research. This paper introduces an innovative approach that combines quantum control techniques with quantum machine learning (QML) to achieve notable advancements in performance and efficiency. The proposed methodology introduces a new quantum control framework designed to optimize quantum machine learning algorithms. This approach not only enhances computational speed but also improves the accuracy of quantum-enhanced support vector machines (SVMs) for classification tasks. The innovation lies in the application of advanced quantum control techniques, which are demonstrated to offer superior performance compared to existing methods. Additionally, the paper presents novel strategies for reducing power consumption in quantum computing systems. By incorporating dynamic quantum control, the proposed system achieves significant improvements in power efficiency, addressing one of the critical challenges in practical quantum computing applications. Benchmark tests across various datasets validate the effectiveness of the new method, showing advancements in processing efficiency and accuracy. The integration of machine learning creates new opportunities for advancements in research, potentially setting new standards in the field of quantum computing. These innovations underscore the potential for combining quantum control with machine learning to achieve groundbreaking improvements in computational capabilities and system efficiency, offering promising directions for future research and practical implementations.