Quantum machine learning with cyber security

Quantum computing (QC) has emerged as a revolutionary
technology with the potential to solve complex problems that are
difficult for classical computing. Quantum Machine Learning
(QML) is a particularly promising field that combines quantum
computing with machine learning techniques, aiming to enhance
the performance and efficiency of algorithms. This research
explores the competitive performance of quantum machine
learning with cybersecurity aspects. The integration of Quantum
Machine Learning (QML) with cybersecurity presents a novel
approach to modern security challenges. Unlike traditional
classical machine learning, which often struggles with complex
cryptography and large-scale data issues, QML leverages the
power of quantum computing to enhance cryptographic
protocols, improve anomaly detection, and strengthen threat
mitigation. This research will focus on different key areas such as
quantum-enhanced anomaly detection, quantum-resistant and
enhanced cryptography, cryptographic techniques, adversarial
robustness, and the use of quantum algorithms for threat
detection using hybrid quantum-classical Frameworks and
benchmarking and standardization. By using quantum kernels
and variational quantum circuits, QML shows potential in
defending networks against advanced cyber threats while
evaluating current advancements, limitations, and prospects in
this field.