Title of the oral contribution: Quantum Bayesian Inference with Renormalization for Gravitational Waves

Advancements in gravitational-wave interferometers, particularly the next generation, are poised to enable the detections of orders of magnitude more gravitational waves from compact binary coalescences. While these surges in detections will profoundly advance gravitational wave astronomy and multimessenger astrophysics, they also pose significant computational challenges in parameter estimation.

We introduce a hybrid quantum algorithm qBIRD, which performs quantum Bayesian Inference with Renormalization and Downsampling to infer gravitational wave parameters. Testing the algorithm with both simulated and observed gravitational waves from binary black hole mergers using quantum simulators, we show that its accuracy is comparable to that of classical Markov Chain Monte Carlo methods. Our current inference runs focus on a subset of parameters, such as chirp mass and mass ratio, due to constraints from classical hardware limitations in simulating quantum algorithms. However, qBIRD is completely scalable, and its full potential will be unlocked when these constraints are eliminated through a small-scale quantum computer with sufficient logical qubits.