Quantum Computing Enhancements in Deep Learning Models for Cybersecurity

The cybersecurity industry has undergone a sea change due to the combination of quantum computing and deep learning models. This study presents a novel approach that makes use of the unique features of quantum computing to improve the efficiency and safety of deep learning models used in cybersecurity. The suggested method improves upon previous methods in terms of both the precision and efficiency with which threats may be identified and categorized by making use of Quantum Neural Networks (QNNs), Quantum Support Vector Machines (QSVMs), and Quantum Key Distribution (QKD). Our findings show that the suggested method is superior to conventional approaches through a thorough examination of detection accuracy and computing efficiency. Combining quantum computing principles with deep learning techniques provides a game-changing answer to the growing complexity of today's cybersecurity threats.