  Advancements in gravitational-wave interferometers, particularly the next
generation, are poised to profoundly impact gravitational wave astronomy and
multimessenger astrophysics. A hybrid quantum algorithm is proposed to carry
out quantum inference of parameters from compact binary coalescences detected
in gravitational-wave interferometers. It performs quantum Bayesian Inference
with Renormalization and Downsampling (qBIRD). We choose binary black hole
(BBH) mergers from LIGO observatories as the first case to test the algorithm,
but its application can be extended to more general instances. The quantum
algorithm is able to generate corner plots of relevant parameters such as chirp
mass, mass ratio, spins, etc. by inference of simulated gravitational waves
with known injected parameter values with zero noise, Gaussian noise and real
data, thus recovering an accuracy equivalent to that of classical Markov Chain
Monte Carlo inferences. The simulations are performed with sets of 2 and 4
parameters. These results enhance the possibilities to extend our capacity to
track signals from coalescences over longer durations and at lower frequencies
extending the accuracy and promptness of gravitational wave parameter
estimation.
