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Abstract

Parkinson’s disease (PD) is a progressive neurological disorder that primarily affects individuals over the age of 55. It is characterized by a range of motor and non-motor symptoms that can significantly impact various aspects of daily life. Despite notable advancements in medical science, there is currently no permanent cure or definitive treatment for PD. This therapeutic gap underscores the critical importance of early diagnosis, which remains a major focus of ongoing research. Due to the disease’s gradual progression, PD symptoms may take years to fully develop, making early detection essential for improving patient outcomes and quality of life. Moreover, the clinical presentation of PD overlaps with several other neurological conditions, highlighting the need for accurate and reliable diagnostic methods. This study proposes a novel framework for analyzing auditory data to enhance the precision of Parkinson’s disease diagnosis. By integrating quantum computing with neural network architectures, the proposed approach aims to improve diagnostic accuracy and support early detection and intervention. The quantum component facilitates more precise and efficient computations, while the neural model enhances information flow and feature extraction. Specifically, a Quantum Convolutional Neural Network (QCNN) is developed and evaluated using auditory data for PD diagnosis. The QCNN demonstrated exceptional performance on Dataset C, which includes 1,000 samples, achieving an accuracy of 99%, a precision of 99.5%, a recall of 99.5%, and an F1-score of 100%. Compared to classical convolutional neural networks, the proposed QCNN offers several advantages. Quantum algorithms are capable of performing specific computations significantly faster than their classical counterparts, leading to reduced processing time. Furthermore, they allow for a more compact representation of features, which lowers model complexity while enhancing overall performance and diagnostic accuracy.

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