Hybrid quantum singular spectrum decomposition for time series analysis

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