  Citation: Shahwar, T.; Zafar, J.; Almogren, A.; Zafar, H.; Rehman, A.U.; Shafiq, M.; Hamam, H. Automated Detection of Alzheimer’s via Hybrid Classical Quantum Neural Networks. Electronics 2022, 11, 721. https://doi.org/10.3390/ electronics11050721 Academic Editor: Amir Mosavi Received: 28 January 2022 Accepted: 24 February 2022 Published: 26 February 2022 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affil- iations. Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). electronics Article Automated Detection of Alzheimer’s via Hybrid Classical Quantum Neural Networks Tayyaba Shahwar 1 , Junaid Zafar 1, *, Ahmad Almogren 2 , Haroon Zafar 3,4,5 , Ateeq Ur Rehman 1 , Muhammad Shafiq 6, * and Habib Hamam 7,8,9 1 Department of Electrical Engineering, Government College University, Lahore 54000, Pakistan; tayyabaniazi28@gmail.com (T.S.); ateeq.rehman@gcu.edu.pk (A.U.R.) 2 Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh 11633, Saudi Arabia; ahalmogren@ksu.edu.sa 3 Cardiovascular Research & Innovation Centre Ireland, School of Medicine, National University of Ireland Galway, H91 TX33 Galway, Ireland; haroon.zafar@nuigalway.ie 4 Lambe Institute for Translational Research, National University of Ireland Galway, H91 TX33 Galway, Ireland 5 College of Engineering and Informatics, National University of Ireland Galway, H91 TX33 Galway, Ireland 6 Department of Information and Communication Engineering, Yeungnam University, Gyeongsan 38541, Korea 7 Faculty of Engineering, Uni de Moncton, Moncton, NB E1A3E9, Canada; habib.hamam@umoncton.ca 8 International Institute of Technology and Management, Libreville BP1989, Gabon 9 School of Electrical Engineering, Department of Electrical and Electronic Engineering Science, University of Johannesburg, Johannesburg 2006, South Africa * Correspondence: chairperson.engineering@gcu.edu.pk (J.Z.); shafiq@ynu.ac.kr (M.S.) Abstract: Deep Neural Networks have offered numerous innovative solutions to brain-related diseases including Alzheimer’s. However, there are still a few standpoints in terms of diagnosis and planning that can be transformed via quantum Machine Learning (QML). In this study, we present a hybrid classical–quantum machine learning model for the detection of Alzheimer’s using 6400 labeled MRI scans with two classes. Hybrid classical–quantum transfer learning is used, which makes it possible to optimally pre-process complex and high-dimensional data. Classical neural networks extract high-dimensional features and embed informative feature vectors into a quantum processor. We use resnet34 to extract features from the image and feed a 512-feature vector to our quantum variational circuit (QVC) to generate a four-feature vector for precise decision boundaries. Adam optimizer is used to exploit the adaptive learning rate corresponding to each parameter based on first- and second-order gradients. Furthermore, to validate the model, different quantum simulators (PennyLane, qiskit.aer and qiskit.basicaer) are used for the detection of the demented and non-demented images. The learning rate is set to 10 −4 for and optimized quantum depth of six layers, resulting in a training accuracy of 99.1% and a classification accuracy of 97.2% for 20 epochs. The hybrid classical–quantum network significantly outperformed the classical network, as the classification accuracy achieved by the classical transfer learning model was 92%. Thus, a hybrid transfer-learning model is used for binary detection, in which a quantum circuit improves the performance of a pre-trained ResNet34 architecture. Therefore, this work offers a method for selecting an optimal approach for detecting Alzheimer’s disease. The proposed model not only allows for the automated detection of Alzheimer’s but would also speed up the process significantly in clinical settings. Keywords: machine learning; deep neural network; quantum computing; quantum machine learning; quantum neural network; Alzheimer’s disease 1. Introduction According to the World Health Organization, Alzheimer’s disease will be a serious health burden in coming times, as approximately 24 million people are affected worldwide, and this number is anticipated to double every 20 years [1]. Against this backdrop, Deep Electronics 2022, 11, 721. https://doi.org/10.3390/electronics11050721 https://www.mdpi.com/journal/electronics