  Current gravitational wave (GW) detection pipelines for compact binary
coalescence based on matched-filtering have reported over 90 confident
detections during the first three observing runs of the LIGO-Virgo-KAGRA (LVK)
detector network. Decreasing the latency of detection, in particular for future
detectors anticipated to have high detection rates, remains an ongoing effort.
In this paper, we develop and test a sparse dictionary learning (SDL) algorithm
for the rapid detection of GWs. We evaluate the algorithms biases and estimate
its GW detection rate for an astrophysical population of binary black holes.
The SDL algorithm is assessed using both, simulated data injected into the
proposed A+ detector sensitivity and real data containing confident detections
from the third LVK observing run. We find that our SDL algorithm can
reconstruct a single binary black hole signal in less than 1 s. This suggests
that SDL could be regarded as a promising approach for rapid, efficient GW
detection in future observing runs of ground-based detectors.
