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Quantum Machine Intelligence

Quantum variational rewinding for gravitational wave detection

Benjamin Rodrigues de Miranda, Domenica Dibenedetto, Niels M. P. Neumann, Ward van der Schoot

Published Feb 8, 2025• 3 citations
Publisher

Abstract

Abstract Gravitational waves carry information about far away phenomena which astronomers can use to form a better picture of the universe. Over the past few years, numerous gravitational waves have been detected using a method called matched filtering; however, this approach has two main drawbacks. First, it requires knowledge of the sought-after waveform beforehand. Second, it is infeasible for gravitational wave searches which are precise or cover a long time span. While attempts have been made to use classical machine learning to overcome these difficulties, the usage of quantum machine learning in this context is relatively unexplored. Quantum variational rewinding is a hybrid quantum-classical machine learning algorithm for time series anomaly detection. This work makes the first steps in investigating its potential for gravitational wave detection. Training and testing the algorithm was done without depending on synthetic data. In a noiseless simulated environment, quantum variational rewinding was able to pick out all confirmed gravitational wave signals from the background noise of a detector with perfect accuracy when filtering was applied before hand and was able to do so to a high degree of success without filtering. Anomaly detection with this algorithm was performed with two signals on quantum hardware as a proof of concept. Using Fisher information, both the capacity and the trainability of quantum variational rewinding are described so that future approaches can be compared in these regards. With new gravitational wave detectors under development, even more data will be generated for analysis in the future, compounding the existing bottlenecks in the search pipeline. As such, approaches which help tackle issues with the current approach to gravitational wave analysis are in demand, and the empirical results from this work assert that quantum computing should not be overlooked in this process.

DOI
10.1007/s42484-025-00244-w

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Quantum computational devices, currently under development, have the potential to accelerate data analysis techniques beyond the ability of any classical algorithm. We propose the application of a quantum algorithm for the detection of unknown signals in noisy data. We apply Grover's algorithm to matched-filtering, a signal processing technique that compares data to a number of candidate signal templates. In comparison to the classical method, this provides a speed-up proportional to the square-root of the number of templates, which would make possible otherwise intractable searches. We demonstrate both a proof-of-principle quantum circuit implementation, and a simulation of the algorithm's application to the detection of the first gravitational wave signal GW150914. We discuss the time complexity and space requirements of our algorithm as well as its implications for the currently computationally-limited searches for continuous gravitational waves.
In this paper, we explore the application of variational quantum algorithms designed for classical optimization to the problem of matched filtering in the detection of gravitational waves. Matched filtering for detecting gravitational wave signals requires searching through a large number of template waveforms, to find one which is highly correlated with segments of detector data. This computationally intensive task needs to be done quickly for low latency searches in order to aid with follow-up multi-messenger observations. The variational quantum algorithms we study for this task consist of quantum walk-based generalizations of the Quantum Approximate Optimization Algorithm (QAOA). We present results of classical numerical simulations of these quantum algorithms using open science data from LIGO. These results show that the tested variational quantum algorithms are outperformed by an unstructured restricted-depth Grover search algorithm, suggesting that the latter is optimal for this computational task.
Quantum computational devices currently under development have the potential to accelerate data analysis techniques beyond the ability of any classical algorithm. We propose the application of a quantum algorithm for the detection of unknown signals in noisy data. We apply Grover's algorithm to matched filtering, a signal processing technique that compares data to a number of candidate signal templates. In comparison to the classical method, this provides a speedup proportional to the square root of the number of templates, which would make possible otherwise intractable searches. We demonstrate both a proof-of-principle quantum circuit implementation and a simulation of the algorithm's application to the detection of the gravitational wave signal GW150914. We discuss the time complexity and space requirements of our algorithm as well as its implications for the currently computationally limited searches for continuous gravitational waves.
Gravitational wave astronomy is rapidly advancing with the development of new observatories, leading to an increasing volume and complexity of data. This trend places growing pressure on classical data analysis methods and motivates the exploration of quantum approaches. In this work, we introduce a quantum matched filtering framework for gravitational-wave detection based on the Long algorithm, marking its first application to the gravitational-wave data analysis. Numerical simulations show that the proposed approach preserves the quadratic speedup of quantum search while exhibiting significantly improved robustness, thereby overcoming key limitations of the Grover-algorithm based methods.
Gravitational wave detection is a monumental challenge, requiring exceptionally sensitive detectors and sophisticated data analysis techniques. This paper investigates a novel approach to enhance signal detection by utilizing quantum entanglement to create subtle correlations between gravitational wave signals and background noise. We propose a new algorithm, the Quantum Entanglement Correlation Detector (QECD), which leverages entanglement to identify and amplify these correlations, offering a potential breakthrough in detecting faint gravitational wave signals. The core claim is that this algorithm can significantly improve precision and detection rates compared to existing methods. We present a theoretical framework and preliminary implementation of the QECD, demonstrating its potential through simulations and initial experimental considerations. The study explores the implications of quantum entanglement for enhancing gravitational wave detection and addresses key challenges related to data processing and algorithm design.
Abstract The field of gravitational wave (GW) detection is progressing rapidly, with several next-generation observatories on the horizon, including LISA. GW data is challenging to analyze due to highly variable signals shaped by source properties and the presence of complex noise. These factors emphasize the need for robust, advanced analysis tools. In this context, we have initiated the development of a low-latency GW detection pipeline based on quantum neural networks (QNNs). Previously, we demonstrated that QNNs can recognize GWs simulated using post-Newtonian approximations in the Newtonian limit. We then extended this work using data from the LISA Consortium, training QNNs to distinguish between noisy GW signals and pure noise. Currently, we are evaluating performance on the Sangria LISA Data Challenge dataset and comparing it against classical methods. Our results show that QNNs can reliably distinguish GW signals embedded in noise, achieving classification accuracies above 98%. Notably, our QNN identified 5 out of 6 mergers in the Sangria blind dataset. The missed event corresponds to the lowest signal-to-noise ratio (SNR) source, indicating that model sensitivity improvements are needed for weak signals. This can potentially be addressed using additional mock training datasets, and by testing different QNN architectures and ansatzes. Compared with a recurrent neural network baseline, the QNN achieves comparable accuracy on higher-SNR events while using orders of magnitude fewer trainable parameters. These results demonstrate the feasibility of QNNs for GW detection and motivate further investigation of quantum-enhanced data analysis techniques for LISA.