标题： 基于量子贝叶斯推理的引力波重正化方法 显示英文标题

标题： Quantum Bayesian Inference with Renormalization for Gravitational Waves

摘要： 引力波干涉仪，特别是下一代引力波干涉仪的进步，有望对引力波天文学和多信使天体物理学产生深远影响。 本文提出了一种混合量子算法，用于从引力波干涉仪探测到的致密双星并合事件中推断参数的量子推理。 该算法执行带重正化和降采样的量子贝叶斯推理（qBIRD）。 我们选择LIGO天文台观测到的黑洞双星（BBH）并合作为测试该算法的第一个案例，但其应用可以扩展到更广泛的实例。 该量子算法能够通过推断模拟的引力波信号生成相关参数（如啁啾质量、质量比、自旋等）的角图，这些信号具有已知注入参数值且无噪声、高斯噪声和真实数据，从而恢复与经典马尔可夫链蒙特卡洛推断相当的精度。 这些模拟使用了2个和4个参数集。 这些结果增强了我们追踪较长持续时间和较低频率的并合信号的能力，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.