Quantum AI for Alzheimer?s disease early screening

Year: 2025

Authors: Cappiello G., Caruso F.

Autors Affiliation: Univ Studi Firenze, DiMaI U Dini, Viale Morgagni 67-A, I-50134 Florence, Italy; Florence Univ, Dept Phys & Astron, Via Sansone 1, I-50019 Sesto Fiorentino, Italy; Florence Univ, LENS European Lab Nonlinear Spect, Via Nello Carrara 1, I-50019 Sesto Fiorentino, Italy; Ist Nazl Ott Consiglio Nazl Ric CNR INO, I-50019 Sesto Fiorentino, Italy.

Abstract: 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.

Journal/Review: NEUROCOMPUTING

Volume: 647      Pages from: 130565-1  to: 130565-11

More Information: This work was supported by the European Commission´s Horizon Europe Framework Programme under the Research and Innovation Action GA n. 101070546\u2013MUQUABIS, by the European Union´s Horizon 2020 research and innovation programme under FET-OPEN GA n. 828946\u2013PATHOS, by the European Defence Agency under the project Q-LAMPS Contract No B PRJ- RT-989, and by the MUR Progetti di Ricerca di Rilevante Interesse Nazionale (PRIN) Bando 2022 – project n. 20227HSE83 \u2013 ThAI-MIA funded by the European Union – Next Generation EU . G. C. is a member of INFM (INdAM).
KeyWords: Quantum machine learning; Alzheimer´s disease; Supervised learning; Data classification; Parametrized quantum circuit
DOI: 10.1016/j.neucom.2025.130565