QUANTUM-INSPIRED INTELLIGENT IMAGING ALGORITHMS FOR MEDICAL AND MULTIMEDIA APPLICATIONS
Tejaswini, Shaik Salar Ahmed, S Bhavya Sri +3 more
Quantum-inspired intelligent imaging has gained significant attention as an emerging computational paradigm for addressing the challenges of high-dimensional, heterogeneous data in medical and multimedia image processing. By mimicking key concepts of quantum mechanics—such as superposition, entanglement, and probabilistic state modeling—quantum-inspired algorithms enhance feature representation, accelerate convergence, and improve optimization efficiency while operating on classical computing platforms. This paper presents a unified quantum-inspired intelligent imaging framework for medical and multimedia applications, integrating quantum-inspired feature encoding with intelligent learning and optimization strategies. The proposed methodology enables effective multimodal image analysis, robust noise resilience, and improved discrimination of complex visual patterns. Experimental evaluations on benchmark medical imaging datasets and multimedia image collections demonstrate superior performance in terms of accuracy, computational efficiency, and generalization when compared to conventional deep learning and evolutionary approaches. The results highlight the potential of quantum-inspired intelligent imaging as a scalable and energy-efficient solution for next-generation healthcare diagnostics and intelligent multimedia systems, paving the way for future integration with hybrid quantum–classical computing architectures.
Iterative International Publishers eBooks•Jun 1, 2026•0 citations