量子机器学习：实际应用中的性能与安全影响
Quantum Machine Learning: Performance and Security Implications in Real-World Applications
AI总结：
本文通过阿尔茨海默病数据集案例研究，比较量子机器学习与经典算法的性能与安全影响，发现QML虽有潜力但尚未超越经典算法，且继承旧脆弱性并引入新攻击向量。
摘要：
近年来，量子计算因其可能实现对经典计算机的“量子优势”而受到学术界和工业界的广泛关注。量子计算的出现给安全与隐私带来了新的挑战。本海报通过一个实际应用中的机器学习案例研究，探讨量子计算在性能和安全方面的影响。我们使用阿尔茨海默病数据集，将量子机器学习（QML）算法与其经典对应算法进行性能比较。结果表明，QML算法展现出有前景的潜力，但在学习能力和收敛难度方面仍未超越经典算法，并且通过在经典计算机上模拟运行量子算法需要极大的内存空间和CPU时间。我们的研究还表明，QML继承了经典机器学习算法的脆弱性，同时也引入了新的攻击向量。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
