<p>In the face of increasingly sophisticated cyber threats in an interconnected world, the need for scalable, intelligent, real-time cybersecurity solutions is intensifying. While traditional deep learning, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), is effective for detecting anomalies in network traffic, their current capabilities do not address challenges with high-dimensionality networks, computational performance, or adaptation to new attacks. Whereas traditional methods suffered constraints, quantum computing offers a complementary advantage in both parallel computing and superior optimization. This study provides a hybrid deep learning and quantum computing framework that employs (i) convolutional neural networks (CNNs) for effectively extracting spatial features, (ii) recurrent neural networks (RNNs) for leveraging temporal patterns, and (iii) Variational Quantum Circuits (VQCs) for quantum-enhanced classification. The framework is built using the CICIDS-2017 dataset to extract features in an extensive preprocessing phase but is a rather laborious process of normalization, feature selection, feature time structuring, and a feature balancing procedure using techniques such as on-over sampling and under-sampling. The hybrid model was evaluated across multiple metrics; for accuracy, recall, F1 score, and ROC-AUC area showed high accuracy (93%) and low false-positive rates across different categories of cyberattacks within an experimental and virtual cyber environment. The suggested framework proved to be generalizable and scalable, suggesting its efficiency in cybersecurity applications that necessitate flexibility and intelligence. This contribution demonstrates a foundational step towards real-time, high performance hybrid quantum - deep learning models for cyber threat detection and prevention.</p>