Quantum Algorithm for Anomaly Detection of Sequences

TL;DR

This paper introduces a quantum algorithm for sequence anomaly detection that significantly speeds up the classical ADPAAD method by leveraging quantum parallelism for processing large-scale data.

Contribution

The paper presents a novel quantum algorithm for ADPAAD that achieves polynomial speedups in processing large sequences compared to classical methods.

Findings

Quantum algorithm achieves polynomial speedup over classical ADPAAD.

Parallel processing of subsequences and their averages in quantum domain.

Potential for handling large-scale sequence anomaly detection efficiently.

Abstract

Anomaly detection of sequences is a hot topic in data mining. Anomaly Detection using Piecewise Aggregate approximation in the Amplitude Domain (called ADPAAD) is one of the widely used methods in anomaly detection of sequences. The core step in the classical algorithm for performing ADPAAD is to construct an approximate representation of the subsequence, where the elements of each subsequence are divided into several subsections according to the amplitude domain and then the average of the subsections is computed. It is computationally expensive when processing large-scale sequences. In this paper, we propose a quantum algorithm for ADPAAD, which can divide the subsequence elements and compute the average in parallel. Our quantum algorithm can achieve polynomial speedups on the number of subsequences and the length of subsequences over its classical counterpart.