Quantum machine learning is a new research field combining quantum
information science and machine learning. Quantum computing technologies seem
to be particularly well suited to solving problems in the health sector in an
efficient way, because they may deal with large datasets more efficiently than
classical AI.
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 kernel methods for classification task and compare their
performances with the ones obtained via quantum machine learning methods. We
find that quantum and classical algorithms achieve similar performances and in
some cases quantum methods perform even better.
Our results pave the way for future new quantum machine learning applications
in early-screening diagnostics in the healthcare domain.

量子机器学习能够在健康领域中进行高效解决大数据集问题，此研究将其应用于阿尔茨海默病的诊断，并发现量子和经典算法在分类任务中表现相似，甚至有时量子方法表现更好，为将来在早期筛查诊断方面开发新的量子机器学习应用铺平了道路。