Hybrid quantum singular spectrum decomposition for time series analysis

Jasper Postema*, Pietro Bonizzi, Gideon Koekoek, Ronald Westra, Servaas Kokkelmans

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

Classical data analysis requires computational efforts that become intractable in the age of Big Data. An essential task in time series analysis is the extraction of physically meaningful information from a noisy time series. One algorithm devised for this very purpose is singular spectrum decomposition (SSD), an adaptive method that allows for the extraction of narrow-banded components from non-stationary and non-linear time series. The main computational bottleneck of this algorithm is the singular value decomposition (SVD). Quantum computing could facilitate a speedup in this domain through superior scaling laws. We propose quantum SSD by assigning the SVD subroutine to a quantum computer. The viability for implementation and performance of this hybrid algorithm on a near term hybrid quantum computer is investigated. In this work, we show that by employing randomized SVD, we can impose a qubit limit on one of the circuits to improve scalibility. Using this, we efficiently perform quantum SSD on simulations of local field potentials recorded in brain tissue, as well as GW150914, the first detected gravitational wave event.
Original languageEnglish
Article number023803
Number of pages14
JournalAVS Quantum Science
Volume5
Issue number2
DOIs
Publication statusPublished - Jun 2023

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