Benchmarking Quantum Neural Networks Developed for MBHB Coalescence in the Context of LISA Space Mission

Author(s)

Isfan, Maria-Catalina, Caramete, Laurentiu-Ioan, Caramete, Ana

Abstract

Machine learning (ML) techniques are increasingly explored to analyse gravitational wave (GW) data expected from the Laser Interferometer Space Antenna (LISA). At the same time, advances in quantum computing have stimulated interest in quantum machine learning as a potential alternative to conventional approaches. We investigate the applicability of quantum, hybrid quantum-classical, and classical ML models to two tasks relevant for LISA observations of massive black hole binaries: GW signal detection and merger prediction. Using the Sangria dataset from the LISA Data Challenge, we benchmark several quantum architectures, including variational quantum classifiers (VQCs), post VQC hybrid models, quantum kernel methods, data reuploading classifiers, and quantum long short-term memory (QLSTM) networks, against classical support vector machines, multilayer perceptrons, recurrent neural networks, and LSTM networks. The models are trained on labelled data derived from the Sangria training dataset and evaluated on the corresponding blind dataset. For signal detection, several quantum models match or outperform classical baselines. In particular, the best performing hybrid post VQC architecture correctly identifies all signal samples in the blind dataset, while the standard VQC and data reuploading classifier achieve competitive detection rates. For merger detection and coalescence time estimation, however, the standard VQC emerges as the most reliable model, successfully localising all detected merger events with the lowest localisation errors. We further analyse the trade-offs between predictive performance, model complexity, and quantum circuit resources. Our results indicate that, although no universal quantum advantage is observed, selected quantum models can provide practical benefits for specific GW analysis tasks and represent promising candidates for future LISA data analysis pipelines.

Figures

Upper panel: the training dataset consisting of 15 MBHB GW consecutive signals. Middle panel: the blind dataset, with undisclosed information. Lower panel: noise in the training dataset.
Caption Upper panel: the training dataset consisting of 15 MBHB GW consecutive signals. Middle panel: the blind dataset, with undisclosed information. Lower panel: noise in the training dataset.
Caption
Caption Caption
Validation learning curves for the quantum and classical models. (a) Validation accuracy learning curves for the quantum and classical models as a function of the training epoch. The shaded regions represent standard deviation across repeated runs. (b) Validation accuracy of the quantum kernel and classical SVMs as a function of the training dataset size. The shaded regions represent the standard deviation across repeated runs.
Caption Validation learning curves for the quantum and classical models. (a) Validation accuracy learning curves for the quantum and classical models as a function of the training epoch. The shaded regions represent standard deviation across repeated runs. (b) Validation accuracy of the quantum kernel and classical SVMs as a function of the training dataset size. The shaded regions represent the standard deviation across repeated runs.
Validation learning curves for the quantum and classical models. (a) Validation accuracy learning curves for the quantum and classical models as a function of the training epoch. The shaded regions represent standard deviation across repeated runs. (b) Validation accuracy of the quantum kernel and classical SVMs as a function of the training dataset size. The shaded regions represent the standard deviation across repeated runs.
Caption Validation learning curves for the quantum and classical models. (a) Validation accuracy learning curves for the quantum and classical models as a function of the training epoch. The shaded regions represent standard deviation across repeated runs. (b) Validation accuracy of the quantum kernel and classical SVMs as a function of the training dataset size. The shaded regions represent the standard deviation across repeated runs.
Confusion matrices for quantum and classical models.
Caption Confusion matrices for quantum and classical models.
Confusion matrices for quantum and classical models.
Caption Confusion matrices for quantum and classical models.
Confusion matrices for quantum and classical models.
Caption Confusion matrices for quantum and classical models.
Confusion matrices for quantum and classical models.
Caption Confusion matrices for quantum and classical models.
Confusion matrices for quantum and classical models.
Caption Confusion matrices for quantum and classical models.
Confusion matrices for quantum and classical models.
Caption Confusion matrices for quantum and classical models.
Confusion matrices for quantum and classical models.
Caption Confusion matrices for quantum and classical models.
Confusion matrices for quantum and classical models.
Caption Confusion matrices for quantum and classical models.
Confusion matrices for quantum and classical models.
Caption Confusion matrices for quantum and classical models.
Confusion matrices for quantum and classical models.
Caption Confusion matrices for quantum and classical models.
Confusion matrices for quantum and classical models.
Caption Confusion matrices for quantum and classical models.
Decision regions in the PCA space for selected models.
Caption Decision regions in the PCA space for selected models.
Decision regions in the PCA space for selected models.
Caption Decision regions in the PCA space for selected models.
Decision regions in the PCA space for selected models.
Caption Decision regions in the PCA space for selected models.
Decision regions for selected models in the standard deviation (Feature 2) VS maximum (feature 5) space.
Caption Decision regions for selected models in the standard deviation (Feature 2) VS maximum (feature 5) space.
Decision regions for selected models in the standard deviation (Feature 2) VS maximum (feature 5) space.
Caption Decision regions for selected models in the standard deviation (Feature 2) VS maximum (feature 5) space.
Decision regions for selected models in the standard deviation (Feature 2) VS maximum (feature 5) space.
Caption Decision regions for selected models in the standard deviation (Feature 2) VS maximum (feature 5) space.
Decision regions for selected models in the standard deviation (Feature 2) VS maximum (feature 5) space.
Caption Decision regions for selected models in the standard deviation (Feature 2) VS maximum (feature 5) space.
Decision regions for selected models in the standard deviation (Feature 2) VS maximum (feature 5) space.
Caption Decision regions for selected models in the standard deviation (Feature 2) VS maximum (feature 5) space.
Decision regions for selected models in the standard deviation (Feature 2) VS maximum (feature 5) space.
Caption Decision regions for selected models in the standard deviation (Feature 2) VS maximum (feature 5) space.
Decision regions for selected models in the standard deviation (Feature 2) VS maximum (feature 5) space.
Caption Decision regions for selected models in the standard deviation (Feature 2) VS maximum (feature 5) space.
Decision regions for selected models in the standard deviation (Feature 2) VS maximum (feature 5) space.
Caption Decision regions for selected models in the standard deviation (Feature 2) VS maximum (feature 5) space.
Probability time series produced by (a) the quantum models and (b) the classical models for the analysis of the Sangria blind dataset. Each point corresponds to the probability assigned to a moving data window. Probability thresholds are used to identify candidate merger regions.
Caption Probability time series produced by (a) the quantum models and (b) the classical models for the analysis of the Sangria blind dataset. Each point corresponds to the probability assigned to a moving data window. Probability thresholds are used to identify candidate merger regions.
Probability time series produced by (a) the quantum models and (b) the classical models for the analysis of the Sangria blind dataset. Each point corresponds to the probability assigned to a moving data window. Probability thresholds are used to identify candidate merger regions.
Caption Probability time series produced by (a) the quantum models and (b) the classical models for the analysis of the Sangria blind dataset. Each point corresponds to the probability assigned to a moving data window. Probability thresholds are used to identify candidate merger regions.
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