Quantum computing and machine learning for cybersecurity: Technologies, challenges, and future perspectives

Abstract

The cybersecurity is confronted with new challenges due to the increase in cyberattacks in terms of their cleverness, speed, and lack of detection. The traditional security systems tend to be slow in countering the sophisticated attacks. Simultaneously, quantum computing is establishing opportunities and risks in regards to security. The paper reviews the literature on quantum computing and machine learning as approaches to enhancing cybersecurity and also explores the difficulties arising with quantum technologies. The paper is conducted based on the PRISMA framework to conduct the search, filtering, the assessment, and synthesis of recent studies on key scientific databases. According to the results hybrid quantum-classical computing and Quantum Neural Networks can enhance intelligent threat detection, predictive analytics, cyber risk assessment, security automation and autonomous security systems. The analysis also shows some key weaknesses, such as the lack of quantum hardware, quantum error correction issues, high computations, the absence of standard security frameworks, privacy issues, and the paucity of practical deployed research. The paper emphasizes that machine learning and quantum computing used together would make cyber resilience, aiding secure computing, enhancing digital forensic, and speeding up further cybersecurity innovation. It is; the review concludes that future studies in Energy in Quantum Security, Quantum-Resistant Security, Zero Trust Security and intelligent cyber defense systems will be important in creating safe, all-scale, and responsive cybersecurity infrastructures in the digitalized environment of the future.