Alzheimer’s disease (AD) is a progressive neurodegenerative disorder characterized by cognitive and behavioral impairments caused by the dysfunction or death of nerve cells, severely affecting daily life activities. Current approaches for early detection rely on biomarkers and neuroimaging techniques. Among these, MRI is widely used for the early diagnosis of AD stages; however, the growing volume of data and aging population is making manual processing and analysis increasingly challenging. Recently, quantum artificial intelligence-based methods have emerged as promising tools for overcoming the limitations of classical approaches and achieving more efficient results, particularly in the early diagnosis and stage classification of AD using MRI data. In this study, we propose a quantum-based parallel model, inspired by classical model parallelism, to classify AD stages with high accuracy. The model was evaluated on two widely used datasets in the literature, OASIS-1 and ADNI. It incorporates two distinct quantum circuits with rotational (U3, RX, RY) and entanglement blocks (CNOT, CY, CCNOT) to exploit quantum advantages. The implementation was carried out on the state-vector simulator default.qubit provided by PennyLane 0.35.1, using 15 epochs and a batch size of eight. The experimental results demonstrate that, for the OASIS-1 dataset, the average training and validation accuracies reached 0.90 and 0.93, with training and validation losses of 1.65 and 1.85, respectively. For the ADNI dataset, the average training/validation accuracies were 0.85/0.81, and the corresponding losses were 1.66/1.86. These findings indicate that the proposed model offers a novel, generalizable, and robust approach for classifying stages of complex diseases such as Alzheimer’s disease.