论文标题
通过量子蒙特卡洛整合和量子振幅扩增引力波匹配的过滤
Gravitational wave matched filtering by quantum Monte Carlo integration and quantum amplitude amplification
论文作者
论文摘要
现在，在包括物理和天文学的数据分析在内的各个领域都积极研究了通过量子计算进行重型数值任务的加速。在本文中，我们根据Gao等人的先前工作，提出了一种用于重力波（GW）数据分析中匹配过滤的新量子算法。 Rev. Research 4，023006（2022）[Arxiv：2109.01535]。我们的方法使用量子算法进行蒙特卡洛整合以进行信噪比（SNR）计算，而不是Gao等人使用的快速傅立叶变换。并通过量子振幅扩增搜索具有高SNR的信号模板。通过这种方式，与Gao等人的算法相比，我们实现了量子数的指数减小，相对于模板号，在经典的GW匹配过滤方面保持了二次加速。
The speedup of heavy numerical tasks by quantum computing is now actively investigated in various fields including data analysis in physics and astronomy. In this paper, we propose a new quantum algorithm for matched filtering in gravitational wave (GW) data analysis based on the previous work by Gao et al., Phys. Rev. Research 4, 023006 (2022) [arXiv:2109.01535]. Our approach uses the quantum algorithm for Monte Carlo integration for the signal-to-noise ratio (SNR) calculation instead of the fast Fourier transform used in Gao et al. and searches signal templates with high SNR by quantum amplitude amplification. In this way, we achieve an exponential reduction of the qubit number compared with Gao et al.'s algorithm, keeping a quadratic speedup over classical GW matched filtering with respect to the template number.
