Alzheimer's disease (AD), a progressive neurodegenerative disorder, presents significant challenges in early diagnosis and treatment. Advancements in Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL), when combined with the power of computation of Quantum Computing (QC), offer promising avenues for the prediction and diagnosis of AD. This literature review surveys twenty research studies exploring the combination of QC with AI/ML/DL for AD prediction and compared through algorithms, evaluation metrics, time for training, datasets separately for ML and DL models. Challenges in current implementations and directions for research work are identified in review summary. The novelty of this review lies in its systematic comparison of quantum-assisted AI models across both machine learning and deep learning paradigms for Alzheimer’s detection, highlighting recent (2020–2024) research progress, performance benchmarks, and open challenges.
Alzheimer's disease (AD), a progressive neurodegenerative disorder, presents significant challenges in early diagnosis and treatment. Advancements in Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL), when combined with the power of computation of Quantum Computing (QC), offer promising avenues for the prediction and diagnosis of AD. This literature review surveys twenty research studies exploring the combination of QC with AI/ML/DL for AD prediction and compared through algorithms, evaluation metrics, time for training, datasets separately for ML and DL models. Challenges in current implementations and directions for research work are identified in review summary. The novelty of this review lies in its systematic comparison of quantum-assisted AI models across both machine learning and deep learning paradigms for Alzheimer’s detection, highlighting recent (2020–2024) research progress, performance benchmarks, and open challenges.
Alzheimer's disease (AD) is a debilitating neurodegenerative condition that affects millions globally, with early diagnosis being crucial for effective intervention and treatment. This project explores Quantum Neural Networks (QNN) combined with Magnetic Resonance Imaging (MRI) data to enhance diagnosis accuracy. This hybrid quantum- classical approach leverages advanced preprocessing techniques, uncovering intricate patterns in MRI data. The study highlights the potential of quantum computing in revolutionizing early-stage Alzheimer's detection, achieving superior performance over traditional methods. Keywords— Alzheimer’s disease, Quantum Computing, MRI, Machine Learning, Quantum Neural Networks
‘Quantum Convolutional Neural Networks’ - QCNN is quantum machine learning model that has the potential of improving the accuracy of Alzheimer’s Disease (AD) diagnosis. Alzheimer’s disease - a neurodegenerative disorder - is considered to be the major cause of Dementia. Early diagnosis of AD is critical for improving patient outcomes. However, current diagnostic methods are often less accurate and time-consuming. QCNNs are able to learn complex features from data, which could be used to identify early signs of AD thereby resulting in an early diagnosis of a disease. This proposed technique will investigate the use of QCNN for AD diagnosis. A QCNN will be trained on a large image dataset from healthy controls & AD patients. The QCNN will then be fine-tuned on a smaller image datasets of patients with suspected AD. The performance of the QCNN will be evaluated on a test dataset of images from patients with suspected AD. This work attempts to design and develop a technique for early disease diagnosis. The QCNN could achieve 88% accuracy in classifying the data(2022) Li et al.[2]. They used a QCNN to diagnose AD from brain functional connectivity data. In another study, by Chen et al.[3], a QCNN could achieve 90% accuracy in classifying the images (2022). So taking inspiration from this, we are proposing to fine tune the QCNN in such a way as to further improve its performance and thereby resulting in more accuracy. The results of the proposed work would highlight the potential of QCNN for AD early diagnosis. If the results are promising, QCNN could be a valuable tool for early diagnosis of Alzheimer’s disease and thereby assists in saving millions of lives.
Alzheimer's disease (AD) is the most prevalent neurodegenerative disorder, primarily affecting the elderly population and leading to significant cognitive decline. This decline manifests in various mental faculties such as attention, memory, and higher-order cognitive functions, severely impacting an individual's ability to comprehend information, acquire new knowledge, and communicate effectively. One of the tasks influenced by cognitive impairments is handwriting. By analyzing specific features of handwriting, including pressure, velocity, and spatial organization, researchers can detect subtle changes that may indicate early-stage cognitive impairments, particularly AD. Recent developments in classical artificial intelligence (AI) methods have shown promise in detecting AD through handwriting analysis. However, as the dataset size increases, these AI approaches demand greater computational resources, and diagnoses are often affected by limited classical vector spaces and feature correlations. Recent studies have shown that quantum computing technologies, developed by harnessing the unique properties of quantum particles such as superposition and entanglement, can not only address the aforementioned problems but also accelerate complex data analysis and enable more efficient processing of large datasets. In this study, we propose a variational quantum classifier with fewer circuit elements to facilitate early AD diagnosis based on handwriting data. Our model has demonstrated comparable classification performance to classical methods and underscores the potential of quantum computing models in addressing cognitive problems, paving the way for future research in this domain.
Quantum machine learning is a new research field combining quantum information science and machine learning. Quantum computing technologies appear to be particularly well-suited for addressing problems in the health sector efficiently. They have the potential to handle large datasets more effectively than classical models and offer greater transparency and interpretability for clinicians. Alzheimer’s disease is a neurodegenerative brain disorder that mostly affects elderly people, causing important cognitive impairments. It is the most common cause of dementia and it has an effect on memory, thought, learning abilities and movement control. This type of disease has no cure, consequently an early diagnosis is fundamental for reducing its impact. The analysis of handwriting can be effective for diagnosing, as many researches have conjectured. The DARWIN (Diagnosis AlzheimeR WIth haNdwriting) dataset contains handwriting samples from people affected by Alzheimer’s disease and a group of healthy people. Here we apply quantum AI to this use-case. In particular, we use this dataset to test classical methods for classification and compare their performances with the ones obtained via quantum machine learning methods. We find that quantum methods generally perform better than classical methods. Our results pave the way for future new quantum machine learning applications in early-screening diagnostics in the healthcare domain.
Alzheimer’s Disease (AD) first strikes learning regions of the brain and is one of the most prevalent forms of dementia. Early diagnosis is crucial for optimum management of treatment, as the disorder is both irreversible and progressive. The current research outlines an end-to-end approach toward early diagnosis of AD by utilizing the hippocampus and transfer learning. The results of a conventional machine learning model—Convolutional Neural Network (CNN)—are compared to those of a Quantum Convolutional Neural Network (QCNN), an adaptation of the conventional CNN through the use of quantum computing methods like quantum kernel estimation. QCNN achieves greater efficiency in processing high-dimensional data than traditional CNNs. The main symptoms of Alzheimer’s are memory loss and cognitive impairment, caused by the degeneration and death of those neurons related to memory. Mild Cognitive Impairment (MCI) lies between cognition within the range of normality and Alzheimer’s. The early diagnosis of MCI has the potential to decelerate or even prevent the development of Alzheimer’s. In this paper, we have established that the QCNN model reached precision at 0.88 and recall at 0.96, compared to the classic CNN, where precision at 0.80, and recall at 0.84 have been achieved. The results highlight the prospective ability of Quantum Machine Learning (QML) to diagnose Alzheimer’s Disease at an early stage.
Abstract This review aimed to explore the integration of Quantum Computing (QC) with Artificial Intelligence (AI) subsets such as Machine Learning (ML) and Deep Learning (DL), addressing the computational demands posed by the exponential growth of visual data. It identifies key challenges such as interdisciplinary complexity, lack of standard benchmarks, scalability, integration barriers, and the theoretical-practical gap in quantum applications. The review systematically examines existing literature on the application of quantum algorithms in areas including image processing, Natural Language Processing (NLP), Transfer Learning (TL), Federated Learning (FL), networking, cybersecurity and the finance sector. It highlights the usage of quantum principles like superposition and entanglement to accelerate computations, optimize models, and enhance data security in ML/DL frameworks. Findings indicate that integrating QC with ML/DL offers faster convergence, improved optimization, secure decentralized learning, and efficient handling of large-scale and complex data. Specific improvements are observed in TL and FL approaches, NLP accuracy, cryptographic robustness, and performance in medical diagnostics and autonomous systems. QC holds transformative potential in enhancing ML/DL capabilities across domains. Despite existing challenges such as error mitigation and integration complexity, its combination with classical learning methods opens new frontiers for research in AI-driven sectors. Future studies should focus on bridging theoretical and application-level gaps while creating standardized evaluation frameworks.