This paper presents a novel hybrid Quantum Key Distribution ,QKD, protocol that combines entanglement based and non entanglement based approaches to optimize security and the number of generated keys. We introduce a dynamic system that integrates a three particle GHZ state method with the two state B92 protocol, using a quantum superposition state to probabilistically switch between them. The GHZ state component leverages strong three particle entanglement correlations for enhanced security, while the B92 component offers simplicity and potentially higher key generation rates. Implemented and simulated using Qiskit, our approach demonstrates higher number of generated keys compared to standalone protocols while maintaining robust security. We present a comprehensive analysis of the security properties and performance characteristics of the proposed protocol. The results show that this combined method effectively balances the trade offs inherent in QKD systems, offering a flexible framework adaptable to varying channel conditions and security this http URL research contributes to ongoing efforts to make QKD more practical and efficient, potentially advancing the development of large scale, secured quantum networks.

The detection of Alzheimer disease (AD) from clinical MRI data is an active area of research in medical imaging. Recent advances in quantum computing, particularly the integration of parameterized quantum circuits (PQCs) with classical machine learning architectures, offer new opportunities to develop models that may outperform traditional methods. However, quantum machine learning (QML) remains in its early stages and requires further experimental analysis to better understand its behavior and limitations. In this paper, we propose an end to end hybrid classical quantum convolutional neural network (CQ CNN) for AD detection using clinically formatted 3D MRI data. Our approach involves developing a framework to make 3D MRI data usable for machine learning, designing and training a brain tissue segmentation model (Skull Net), and training a diffusion model to generate synthetic images for the minority class. Our converged models exhibit potential quantum advantages, achieving higher accuracy in fewer epochs than classical models. The proposed beta8 3 qubit model achieves an accuracy of 97.50%, surpassing state of the art (SOTA) models while requiring significantly fewer computational resources. In particular, the architecture employs only 13K parameters (0.48 MB), reducing the parameter count by more than 99.99% compared to current SOTA models. Furthermore, the diffusion-generated data used to train our quantum models, in conjunction with real samples, preserve clinical structural standards, representing a notable first in the field of QML. We conclude that CQCNN architecture like models, with further improvements in gradient optimization techniques, could become a viable option and even a potential alternative to classical models for AD detection, especially in data limited and resource constrained clinical settings.