Quantum AI for psychiatric diagnosis: enhancing dementia classification with quantum machine learning

Topic: Risk, prevention and care in population studies

Early detection of dementia is a key requirement for effective patient management. Therefore, classification of dementia is pertinent and requires a highly accurate methodology. Deep learning (DL) models process immense amounts of input data, whereas quantum machine learning (QML) models use qubits and quantum operations to enhance computational speed and data storage through algorithms. QML is a research domain that investigates the interactions between quantum computing concepts and machine learning. A quantum computer reduces training time and uses qubits that play a vital role in learning complex imaging patterns, unlike convolutional kernels. The proposed study focused on imaging data and QML because they are more efficient and accurate than ML/DL for practical applications. Therefore, a hybrid quantum-classical convolutional neural network (QCNN) is proposed that integrates both quantum and classical learning paradigms. In the proposed framework, MRI images are pre-processed through resizing and normalization, followed by the extraction of a region of interest (ROI) from the center of each image. Within the ROI, a 2×2 patch is passed to a quantum circuit, where pixel values are encoded as qubits using rotation gates (RY). A parameterized quantum circuit (PQC) with entangling layers computes expectation values to generate a quantum feature map, which is then utilized as input to the classical CNN. To further improve generalization, a knowledge distillation (KD) framework is employed, where a teacher model (a deeper CNN with high representational capacity) guides a student model (the QCNN), transferring soft-label information via a temperature-scaled softmax. This setup enables the student model to learn more discriminative features while maintaining efficiency. Comprehensive experiments are conducted on benchmark ADNI-1, ADNI-2, and OASIS-2 MRI datasets, and results are reported both with and without KD. Without KD, the QCNN achieves strong performance with accuracies of 0.9523 (ADNI-1), 0.9611 (ADNI-2), and 0.9412 (OASIS-2). With KD, the student model demonstrates enhanced sensitivity to challenging classes, achieving an accuracy of up to 0.9978, surpassing state-of-the-art approaches. Combining quantum feature extraction with teacher-student knowledge transfer yields a scalable and highly accurate framework for dementia classification in clinical practice.

Key findings

• Comprehensive experiments are conducted on benchmark ADNI-1, ADNI-2, and OASIS-2 MRI datasets, and results are reported both with and without KD.

Why it matters

This paper proposes a hybrid quantum-classical neural network for classifying dementia from MRI images and reports experiments on benchmark datasets. It is a technical classification method and does not show any treatment, cure, or prevention of Alzheimer's disease.

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Study details

• Evidence strengthLow

Read the original

• DOI: 10.3389/fpsyt.2025.1648060
• PubMed: 41383998
• Original source →
• Free full text: Frontiers in Psychiatry →

Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).

@article{amin2025-41383998,
  author  = {Amin, Javeria and Ali, Muhammad Umair and Islam, Muhammad Zubair and Lee, Seungwon},
  title   = {{Quantum AI for psychiatric diagnosis: enhancing dementia classification with quantum machine learning}},
  journal = {Frontiers in Psychiatry},
  year    = {2025},
  doi     = {10.3389/fpsyt.2025.1648060},
  pmid    = {41383998},
  url     = {https://doi.org/10.3389/fpsyt.2025.1648060}
}

Studies this one cites

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• Using Machine Learning to Predict Dementia from Neuropsychiatric Symptom and Neuroimaging Data Journal of Alzheimer's disease : JAD · 2020-01-01 · usefulness score 56/100 · Digital health tools
• ALZENET: Deep learning-based early prediction of Alzheimer's disease through magnetic resonance imaging analysis Telematics and Informatics Reports · 2025-02-14 · usefulness score 48/100 · Brain scans and imaging
• Machine-learning techniques for building a diagnostic model for very mild dementia NeuroImage · 2010-04-09 · usefulness score 44/100 · Digital health tools

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• The prevalence of frontotemporal dementia Neurology · 2002-06-01 · usefulness score 60/100 · Getting a diagnosis
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