用于异常检测的量子机器学习算法：综述
Quantum machine learning algorithms for anomaly detection: A review
AI总结：
本综述系统梳理了用于异常检测的量子机器学习算法，按监督、无监督和强化学习分类，并评估硬件资源与计算复杂度。
摘要：
量子计算机的出现证明了开发量子机器学习算法的合理性，这些算法基于将机器学习原理适配到量子比特形式体系中。在此类量子算法中，异常检测是一个跨越网络安全、欺诈检测和粒子物理等多个学科的重要问题。我们总结了量子计算涉及的关键概念，并引入了量子加速的形式化概念。本综述提供了一幅基于量子机器学习的异常检测结构化图谱。我们将现有算法根据不同的学习方法进行分组，即量子监督学习、量子无监督学习和量子强化学习。我们提供了对未来提供足够计算能力所需硬件资源的估计。本综述对属于每个类别的技术提供了系统且紧凑的理解。最后，我们讨论了学习方法在真实应用领域中的计算复杂度。
英文摘要：
The advent of quantum computers has justified the development of quantum machine learning algorithms , based on the adaptation of the principles of machine learning to the formalism of qubits. Among such quantum algorithms, anomaly detection represents an important problem crossing several disciplines from cybersecurity, to fraud detection to particle physics. We summarize the key concepts involved in quantum computing, introducing the formal concept of quantum speed up. The review provides a structured map of anomaly detection based on quantum machine learning. We have grouped existing algorithms according to the different learning methods, namely quantum supervised, quantum unsupervised and quantum reinforcement learning, respectively. We provide an estimate of the hardware resources to provide sufficient computational power in the future. The review provides a systematic and compact understanding of the techniques belonging to each category. We eventually provide a discussion on the computational complexity of the learning methods in real application domains.
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。
