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Matched filtering is a signal processing technique used to maximize the signal-to-noise ratio by correlating signal templates with detector data. It is central for detecting the extraordinarily wea...
Matched filtering is a signal processing technique used to maximize the signal-to-noise ratio by correlating signal templates with detector data. It is central for detecting the extraordinarily weak signals in the field of gravitational wave astronomy. In cases where the signal is too weak and the template bank is too extensive, the matched filtering process can become computationally unfeasible. In this talk I will analyse the potential of Grover's algorithm to speed up match filtering for gravitational wave detection, where even a polynomial speed-up could make previously intractable searches possible. I will present an example of simulated quantum matched filtering applied to the detection of the first gravitational wave signal to demonstrate the algorithm's potential application in data analysis.