  Quantum computing has garnered significant attention in recent years from
both academia and industry due to its potential to achieve a "quantum
advantage" over classical computers. The advent of quantum computing introduces
new challenges for security and privacy. This poster explores the performance
and security implications of quantum computing through a case study of machine
learning in a real-world application. We compare the performance of quantum
machine learning (QML) algorithms to their classical counterparts using the
Alzheimer's disease dataset. Our results indicate that QML algorithms show
promising potential while they still have not surpassed classical algorithms in
terms of learning capability and convergence difficulty, and running quantum
algorithms through simulations on classical computers requires significantly
large memory space and CPU time. Our study also indicates that QMLs have
inherited vulnerabilities from classical machine learning algorithms while also
introduce new attack vectors.
