A Detailed Exploration of Elevating Cybersecurity through Quantum Computing: Innovative Deep Learning Strategies and Optimization Methods

Abstract

The rapid advancement of digital technologies has significantly increased the complexity and scope of cybersecurity challenges. With the rise in sophisticated cyberattacks, traditional cryptographic techniques frequently fall short of the risks that are changing, which creates a need for more sophisticated solutions. The capacity of quantum computing to solve complicated problems tenfold quicker than traditional systems, presents a promising approach for enhancing cybersecurity. When integrated with deep learning, better threat detection, vulnerability assessment, and data encryption are all possible with quantum computing. 50 research papers that address the convergence of deep learning, quantum computing, and cybersecurity optimization strategies and were published between 2023 and 2024 are critically examined in this review. The problem statement focuses on how quantum-enhanced deep learning models can help overcome the shortcomings of traditional approaches in dealing with new cyber threats. The review highlights the key methodologies, optimization strategies, and outcomes presented in recent studies, offering insights into their practical applications and potential impact on future cybersecurity frameworks. Additionally, it discusses the challenges associated with implementing quantum computing in real-world scenarios, such as scalability, resource requirements, and integration with existing security infrastructures, providing a comprehensive perspective on the changing terrain of cybersecurity solutions.