Quantum algorithms for anomaly detection using amplitude estimation

Anomaly detection, as an important branch of machine learning, plays a critical role in fraud health care, intrusion military surveillance, etc. An anomaly detection algorithm based on density estimation (called ADDE algorithm) is one the widely used algorithms. However, computationally expensive when processing big data sets. To solve this problem, paper, we propose efficient quantum amplitude estimation. It shown that our achieves exponential speedup number training points M over its classical counterpart. Besides, idea can be applied to accelerate kernel principal component analysis ADKPCA algorithm), which also has wide range applications. Our shows compared with