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.
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.
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.
In this work, we explore the possibility of using quantum computers provided for usage in cloud by big companies (such as IBM, IonQ, IQM Quantum Computers, etc.) to run our quantum neural network (QNN) developed for data analysis in the context of LISA Space Mission, developed with the Qiskit library in Python. Our previous work demonstrated that our QNN learns patterns in gravitational wave (GW) data much faster than a classical neural network, making it suitable for fast GW signal detection in future LISA data streams. Analyzing the fees from hardware providers like IBM Quantum, Amazon Braket and Microsoft Azure, we found that the fees for running the first segment of our QNN sum up to $2000, $60000, and $1000000 respectively. Using free plans, we succeed to run the 3-qubit feature map of the QNN for one random data sample on {\fontfamily{qcr} \selectfont ibm\_kyoto} and {\fontfamily{qcr}\selectfont IQM Quantum Computers\_Garnet} quantum computers, obtaining a fidelity of 99\%; we could also run the first prediction segment of our QNN on {\fontfamily{qcr} \selectfont ibm\_kyoto}, implemented for 4 qubits, and obtained a prediction accuracy of 20\%. We queried providers such as IBM Quantum, Amazon Braket, Pasqal, and Munich Quantum Valley to obtain access to their plans, but, with the exception of Amazon Braket, our applications remain unanswered to this day. Other major setbacks in using the quantum computers we had access to included Qiskit library version issues (as in the cases of IBM Quantum and IQM Quantum Computers) and the frequent unavailability of the devices, as was the case with the Microsoft Azure provider. All the results presented in this paper were accumulated in 2024.
This study evaluates the use of Quantum Convolutional Neural Networks (QCNNs) for identifying signals resembling Gamma-Ray Bursts (GRBs) within simulated astrophysical datasets in the form of light curves. The task addressed here focuses on distinguishing GRB-like signals from background noise in simulated Cherenkov Telescope Array Observatory (CTAO) data, the next-generation astrophysical observatory for very high-energy gamma-ray science. QCNNs, a quantum counterpart of classical Convolutional Neural Networks (CNNs), leverage quantum principles to process and analyze high-dimensional data efficiently. We implemented a hybrid quantum-classical machine learning technique using the Qiskit framework, with the QCNNs trained on a quantum simulator. Several QCNN architectures were tested, employing different encoding methods such as Data Reuploading and Amplitude encoding. Key findings include that QCNNs achieved accuracy comparable to classical CNNs, often surpassing 90%, while using fewer parameters, potentially leading to more efficient models in terms of computational resources. A benchmark study further examined how hyperparameters like the number of qubits and encoding methods affected performance, with more qubits and advanced encoding methods generally enhancing accuracy but increasing complexity. QCNNs showed robust performance on time-series datasets, successfully detecting GRB signals with high precision. The research is a pioneering effort in applying QCNNs to astrophysics, offering insights into their potential and limitations. This work sets the stage for future investigations to fully realize the advantages of QCNNs in astrophysical data analysis.
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.
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.
The emergence of Beyond 5G (B5G) and 6G networks introduces advanced capabilities in speed, latency, and connectivity, while also amplifying the risk of sophisticated security threats such as False Base Stations (FBS). These rogue entities imitate legitimate base stations to intercept data, perform man-in-the-middle attacks, or disrupt services. Researchers have explored conventional machine learning methods for FBS detection, but these methods often struggle to address the high-dimensional and dynamic nature of next-generation networks. This paper proposes a Quanvolutional Neural Network (QNN) framework as a quantum-enhanced solution for FBS detection. QNN utilizes quantum phenomena such as superposition and entanglement to perform complex feature transformations, enabling more expressive data representations than classical models. We evaluate the proposed QNN against a traditional Convolutional Neural Network (CNN) using a classification task designed to simulate the detection of FBS. Results demonstrate that although the QNN exhibits slower convergence during early training, it ultimately outperforms the CNN across all key evaluation metrics. The QNN achieves higher accuracy (90.65%), improved recall, lower false positive rate, and a superior F1 score, while also attaining a lower final training loss. These findings suggest that QNNs have strong potential for real-time, localized threat detection in AInative 6G architectures. Future work will explore deployment on Noisy Intermediate-Scale Quantum (NISQ) hardware and evaluate model performance under realistic noise conditions. This study contributes to ongoing research on integrating quantum machine learning into secure and intelligent wireless communication systems