Quantum Bayesian Inference with Renormalization for Gravitational Waves
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...
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| Language: | English |
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IOP Publishing
2025-01-01
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| Series: | The Astrophysical Journal Letters |
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| Online Access: | https://doi.org/10.3847/2041-8213/ada6ae |
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| author | Gabriel Escrig Roberto Campos Hong Qi M. A. Martin-Delgado |
| author_facet | Gabriel Escrig Roberto Campos Hong Qi M. A. Martin-Delgado |
| author_sort | Gabriel Escrig |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-3df87ceb1af5417f8d8e64189b89ae09 |
| institution | DOAJ |
| issn | 2041-8205 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | The Astrophysical Journal Letters |
| spelling | doaj-art-3df87ceb1af5417f8d8e64189b89ae092025-08-20T03:05:09ZengIOP PublishingThe Astrophysical Journal Letters2041-82052025-01-019792L3610.3847/2041-8213/ada6aeQuantum Bayesian Inference with Renormalization for Gravitational WavesGabriel Escrig0https://orcid.org/0000-0003-2881-085XRoberto Campos1https://orcid.org/0000-0002-2527-4177Hong Qi2https://orcid.org/0000-0001-6339-1537M. A. Martin-Delgado3https://orcid.org/0000-0003-2746-5062Departamento de Física Teórica, Universidad Complutense de Madrid , 28040 Madrid, Spain ; gescrig@ucm.es, robecamp@ucm.es, mardel@ucm.esDepartamento de Física Teórica, Universidad Complutense de Madrid , 28040 Madrid, Spain ; gescrig@ucm.es, robecamp@ucm.es, mardel@ucm.esSchool of Mathematical Sciences, Queen Mary University of London , London E1 4NS, UK ; hong.qi@ligo.orgDepartamento de Física Teórica, Universidad Complutense de Madrid , 28040 Madrid, Spain ; gescrig@ucm.es, robecamp@ucm.es, mardel@ucm.es; CCS-Center for Computational Simulation, Universidad Politécnica de Madrid , SpainAdvancements 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.https://doi.org/10.3847/2041-8213/ada6aeGravitational wavesAlgorithmsMarkov chain Monte CarloGravitational wave sources |
| spellingShingle | Gabriel Escrig Roberto Campos Hong Qi M. A. Martin-Delgado Quantum Bayesian Inference with Renormalization for Gravitational Waves The Astrophysical Journal Letters Gravitational waves Algorithms Markov chain Monte Carlo Gravitational wave sources |
| title | Quantum Bayesian Inference with Renormalization for Gravitational Waves |
| title_full | Quantum Bayesian Inference with Renormalization for Gravitational Waves |
| title_fullStr | Quantum Bayesian Inference with Renormalization for Gravitational Waves |
| title_full_unstemmed | Quantum Bayesian Inference with Renormalization for Gravitational Waves |
| title_short | Quantum Bayesian Inference with Renormalization for Gravitational Waves |
| title_sort | quantum bayesian inference with renormalization for gravitational waves |
| topic | Gravitational waves Algorithms Markov chain Monte Carlo Gravitational wave sources |
| url | https://doi.org/10.3847/2041-8213/ada6ae |
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