RT Journal Article
T1 Quantum Bayesian Inference with renormalization for gravitational waves
A1 Escrig, Gabriel
A1 Campos, Roberto
A1 Qi, Hong
A1 Martín-Delgado Alcántara, Miguel Ángel
AB 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.
PB IOP Publishing
SN 2041-8205
YR 2025
FD 2025
LK https://hdl.handle.net/20.500.14352/122422
UL https://hdl.handle.net/20.500.14352/122422
LA eng
NO Gabriel Escrig et al 2025 ApJL 979 L36
NO W911NF-14-1-0103;2022–2023 STFC IAA;PHY0757058; PHY-0823459; PHY-1626190; PHY-1700765.
NO Ministerio de Ciencia e Innovación (España)
NO Comunidad de Madrid
NO European Commission
NO Ministerio para la Transformación Digital y de la Función Pública (España)
NO U.S. Army Research Office
NO Department for Science, Innovation and Technology (Reino Unido)
NO National Science Foundation (US)
DS Docta Complutense
RD 28 mar 2026