  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 randomised 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.
