In the rapidly evolving landscape of network security, Intrusion Detection Systems (IDS) play an indispensable role in distinguishing normal network traffic from anomalies. While traditional machine learning models have achieved notable success, the introduction of quantum-assisted techniques opens new avenues for improving accuracy and reliability. This review delves into the application of quantum supervised learning for Intrusion Detection, emphasizing recent breakthroughs and comparing the advantages of quantum methods to classical counterparts. As digital networks become increasingly interconnected, the demand for robust Intrusion Detection Systems has grown exponentially. This paper investigates quantum-enhanced cybersecurity solutions, particularly the incorporation of quantum supervised learning, to improve the detection and classification of network intrusions. The review discusses the theoretical principles of quantum supervised learning, its unique attributes, and the advancement of quantum-enabled IDS. This paper provides a detailed overview of how quantum computing is revolutionizing machine learning for cybersecurity, with an emphasis on the enhanced capabilities of IDS through quantum-assisted methodologies.