Quantum Bayesian Inference with renormalization for gravitational waves

Abstract

Advancements in gravitational-wave (GW) interferometers, particularly the next generation, are poised to enable the detections of orders of magnitude more GWs from compact binary coalescences. While the surge in detections will profoundly advance GW astronomy and multimessenger astrophysics, it also poses significant computational challenges in parameter estimation. In this work, we introduce a hybrid quantum algorithm qBIRD, which performs quantum Bayesian inference with renormalization and downsampling to infer GW parameters. We validate the algorithm using both simulated and observed GWs from binary black hole mergers on quantum simulators, demonstrating that its accuracy is comparable to classical Markov Chain Monte Carlo methods. Currently, our analyses focus on a subset of parameters, including chirp mass and mass ratio, due to the limitations from classical hardware in simulating quantum algorithms. However, qBIRD can accommodate a broader parameter space when the constraints are eliminated with a small-scale quantum computer of sufficient logical qubits.