Quantum Algorithms Database
Quantum Anomaly Detection
Quantum approaches to identifying unusual or outlier data points within a larger dataset, relevant to fraud detection and system monitoring.
Year
2018
Inventor(s)
Multiple contributors, including Liu & Rebentrost
Speedup Type
Polynomial Speedup
Difficulty
★★★★☆
The Problem
Identifying data points that deviate significantly from the normal pattern in a dataset, without needing labeled examples of what 'abnormal' looks like.
How It Works
Uses quantum kernel methods or density estimation techniques to measure how far a data point's quantum-encoded representation differs from the bulk of the dataset.
Real-World Impact
An active research direction for potential applications like fraud detection and network security monitoring, though — like most quantum ML proposals — without demonstrated advantage on real-world datasets yet.