Quantum Machine Learning: Performance and Security Implications in Real-World Applications

TL;DR

This paper examines the performance and security implications of quantum machine learning in real-world applications, comparing quantum and classical algorithms and highlighting their advantages, limitations, and new vulnerabilities.

Contribution

It provides a case study analyzing quantum machine learning's performance and security issues in a practical setting, highlighting current challenges and potential.

Findings

QML shows promising potential but hasn't surpassed classical algorithms.

Quantum algorithms require significant computational resources when simulated classically.

QML inherits classical vulnerabilities and introduces new attack vectors.

Abstract

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.