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Lookup NU author(s): Dr Anurag SharmaORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
© 2026, Dr. Yashwant Research Labs Pvt. Ltd. All rights reserved.The presented investigation opens up a novel avenue that fuses quantum-assisted deep learning algorithms for the detection at the earliest possible stage of neurodegenerative diseases. The system that unites the pattern recognition capabilities of deep learning and the quantum computing power is aimed at greatly improving the diagnostic accuracy for the initial stages. This method highlights the exploitation of hybrid quantum-classical architectures to execute the complicated and multidimensional medical dataset that captures subtle interrelationship and disturbances that may not be revealed by the traditional models. The synthesis of the quantum feature encoding with the neural architectures that become more sophisticated provides for quicker convergence and better generalization over the various patient sets of data. Also, the model contains a changeable learning method that gets the current clinical patterns and, consequently, be able to timely and more accurately. The innovative architecture not only improves the prediction results but also facilitates the development of more scalable and energy-efficient solutions in the area of neuroinformatics...
Author(s): David DS, Ponkumar DDN, Sharma A, Krishnan SB, Chakrabarti P
Publication type: Article
Publication status: Published
Journal: International Journal of Drug Delivery Technology
Year: 2026
Volume: 16
Issue: 2s
Pages: 610-615
Print publication date: 31/03/2026
Acceptance date: 02/04/2018
Date deposited: 15/04/2026
ISSN (print): 0975-4415
Publisher: Dr. Yashwant Research Labs Pvt. Ltd
URL: https://ijddt.com/abstract/16/IJDDT,Vol16,Issue2s,Article79.html
DOI: 10.25258/ijddt.16.610-615
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