A HYBRID QUANTUM CLASSICAL FRAMEWORK FOR CROSS DOMAIN TRANSFER LEARNING WITH REAL TIME ADAPTIVE MULTI TASK LEARNING
CHEEMALADINNE VENGAIAH, ROOPADEVI BOSUKONDA, DR. B. RANGA SWAMY, RAGHUNATH MANDIPUDI, D. VIJAYA SRI, DR. ATHMAKURI SATISH KUMAR, DR. M. SRIDHAR, JOHN T MESIA DHAS
The growing need for intelligent systems, which can operate in heterogeneous areas, indicates the limitations of traditional transfer learning methods, especially with regard to processing high-dimensional data and operating in real-time conditions. This paper presents a new cross-domain transfer learning system based on Quantum Neural Networks (QNNs) to be used in multi-task applications with real-time adaptation, to facilitate scalable and efficient knowledge transfer across domains, including, but not limited to, healthcare, IoT, and cybersecurity. The suggested architecture is based on a hybrid quantum-classical architecture, where parameterized quantum circuits are used to represent the features, domain-invariant learning and multi-task optimization strategies are applied, and a reinforcement learning-based module is used to provide continuous real-time adaptation by using streaming data. Experimental findings show that the model has 94.8% accuracy, which is significantly higher than the current classical and transformer-based methods, and that the adaptation latency of the model is 95 ms, and the transfer efficiency is more than 10 times higher, which means that generalization and robustness are higher. The results prove that quantum-enhanced transfer learning is a powerful and scalable solution in next-generation intelligent systems, with significant practical impact in real-time systems, such as smart healthcare, cybersecurity, and IoT systems.
Journal of Theoretical and Applied Information Technology•Aug 15, 2026•0 citations