Logo image
Back
InQMAD: Incremental Quantum Measurement Anomaly Detection Dataset
Dataset   Open access

InQMAD: Incremental Quantum Measurement Anomaly Detection Dataset

Joseph Gallego, Oscar Alberto Bustos-Briñez and Fabio Gonzalez
11 Feb 2026
DOI: https://doi.org/10.5281/zenodo.18613985
url
https://doi.org/10.5281/zenodo.18613985View
Open

Abstract

Anomaly Detection Streaming Unsupervised Machine Learning Machine Learning
This dataset contains all the necessary files for testing the InQMAD: Incremental Quantum Measurement Anomaly Detection in github https://github.com/Joaggi/INQMAD-Incremental-Anomaly-Detection-using-Quantum-Measurements   Abstract Paper:  Streaming anomaly detection refers to the problem of detecting anomalous data samples in streams of data. This problem poses challenges that classical and deep anomaly detection methods are not designed to cope with, such as conceptual drift and continuous learning. State-of-the-art flow anomaly detection methods rely on fixed memory using hash functions or nearest neighbors that may not be able to constrain high frequency values or remove seamless outliers and cannot be trained in an end-to-end deep learning architecture. We present a new incremental anomaly detection method that performs continuous density estimation based on random Fourier features and the mechanism of quantum measurements and density matrices. The method continuously updates the estimation of the density of normal samples, giving more importance to new samples and decreasing the importance of older samples. It can process potentially endless data and its update complexity is constant O(1). A systematic evaluation against 12 state-of-the-art streaming anomaly detection algorithms and using 12 streaming datasets is presented.

Metrics

3 Record Views

Details

Logo image