# Quantum AI for Alzheimer's disease early screening

arXiv: https://arxiv.org/abs/2405.00755 · HTML: https://mlsanity.com/paper/2405.00755/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 53 references that are in the map.
- First posted on arXiv 1 May 2024.

## Builds on (most cited first, 10 of 10)

- [Supervised quantum machine learning models are kernel methods](https://mlsanity.com/paper/2101.11020/) (2021)
- [Connecting ansatz expressibility to gradient magnitudes and barren plateaus](https://mlsanity.com/paper/2101.02138/) (2021)
- [Quantum machine learning: a classical perspective](https://mlsanity.com/paper/1707.08561/) (2017)
- [Quantum machine learning beyond kernel methods](https://mlsanity.com/paper/2110.13162/) (2021)
- [The Inductive Bias of Quantum Kernels](https://mlsanity.com/paper/2106.03747/) (2021)
- [Bandwidth Enables Generalization in Quantum Kernel Models](https://mlsanity.com/paper/2206.06686/) (2022)
- [Kernel Methods and their derivatives: Concept and perspectives for the Earth system sciences](https://mlsanity.com/paper/2007.14706/) (2020)
- [Machine learning approach for early detection of autism by combining questionnaire and home video screening](https://mlsanity.com/paper/1703.06076/) (2017)
- [Eigenvalue Decay Implies Polynomial-Time Learnability for Neural Networks](https://mlsanity.com/paper/1708.03708/) (2017)
- [Quantum Machine Learning in the Cognitive Domain: Alzheimer's Disease Study](https://mlsanity.com/paper/2401.06697/) (2023)

## Built on by (most cited first, 2 of 2)

- [Quantum Model Parallelism for MRI-Based Classification of Alzheimer's Disease Stages](https://mlsanity.com/paper/2602.00128/) (2026)
- [Hybrid Classical-Quantum (HCQ) Alzheimer's Classification via Supervised β-VAE and Quantum Kernels](https://mlsanity.com/paper/2606.14194/) (2026)

## Abstract (arXiv)

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.