标题： 量子贝叶斯推断与引力波的重整化 显示英文标题

标题： Quantum Bayesian Inference with Renormalization for Gravitational Waves

摘要： 引力波干涉仪的进展，特别是下一代干涉仪，即将对引力波天文学和多信使天体物理学产生深远影响。 提出了一种混合量子算法，用于从引力波干涉仪中检测到的致密双星合并中进行参数的量子推断。 它执行带有重整化和下采样的量子贝叶斯推断（qBIRD）。 我们选择来自LIGO观测站的双黑洞（BBH）合并作为第一个案例来测试该算法，但其应用可以扩展到更一般的情况。 该量子算法能够通过推断具有已知注入参数值的模拟引力波（无噪声、高斯噪声和真实数据），生成相关参数（如啁啾质量、质量比、自旋等）的角落图，从而恢复出与经典马尔可夫链蒙特卡洛推断相当的准确性。 模拟是使用2个和4个参数的集合进行的。 这些结果增强了我们跟踪长时间和低频合并信号的能力，从而提高了引力波参数估计的准确性和及时性。 显示英文摘要

摘要： Advancements in gravitational-wave interferometers, particularly the next generation, are poised to profoundly impact gravitational wave astronomy and multimessenger astrophysics. A hybrid quantum algorithm is proposed to carry out quantum inference of parameters from compact binary coalescences detected in gravitational-wave interferometers. It performs quantum Bayesian Inference with Renormalization and Downsampling (qBIRD). We choose binary black hole (BBH) mergers from LIGO observatories as the first case to test the algorithm, but its application can be extended to more general instances. The quantum algorithm is able to generate corner plots of relevant parameters such as chirp mass, mass ratio, spins, etc. by inference of simulated gravitational waves with known injected parameter values with zero noise, Gaussian noise and real data, thus recovering an accuracy equivalent to that of classical Markov Chain Monte Carlo inferences. The simulations are performed with sets of 2 and 4 parameters. These results enhance the possibilities to extend our capacity to track signals from coalescences over longer durations and at lower frequencies extending the accuracy and promptness of gravitational wave parameter estimation.