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.

中文翻译：

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用于在 LISA 太空任务背景下快速探测引力波的量子计算工具

引力波 （GW） 探测领域正在迅速发展，包括 LISA 在内的几个下一代天文台即将建成。由于信号源特性影响的信号变化很大，并且存在复杂的噪声，因此 GW 数据的分析具有挑战性。这些因素强调了对强大、高级分析工具的需求。在此背景下，我们启动了基于量子神经网络（QNNs）的低延迟 GW 检测管道的开发。之前，我们证明了 QNN 可以识别在牛顿极限中使用后牛顿近似模拟的 GW。然后，我们使用来自 LISA 联盟的数据扩展了这项工作，训练 QNN 区分噪声 GW 信号和纯噪声。目前，我们正在评估桑格利亚汽酒 LISA 数据挑战数据集的性能，并将其与经典方法进行比较。我们的结果表明，QNN 可以可靠地区分噪声中嵌入的 GW 信号，分类准确率超过 98%。值得注意的是，我们的 QNN 在桑格利亚盲数据集中确定了 6 个合并中的 5 个。漏报事件对应于最低的信噪比（SNR）源，表明需要提高弱信号的模型灵敏度。这可以通过使用额外的模拟训练数据集以及测试不同的 QNN 架构和答案来解决。与循环神经网络基线相比，QNN 在较高信噪比事件上实现了相当的准确性，同时使用的可训练参数减少了数量级。这些结果证明了 QNNs 在 GW 检测中的可行性，并推动了 LISA 量子增强数据分析技术的进一步研究。