Gravitational wave matched filtering by quantum Monte Carlo integration and quantum amplitude amplification

TL;DR

This paper introduces a quantum algorithm for gravitational wave data analysis that significantly reduces qubit requirements and offers quadratic speedup over classical methods by combining quantum Monte Carlo integration and amplitude amplification.

Contribution

The paper presents a novel quantum algorithm for GW matched filtering that improves qubit efficiency and maintains quadratic speedup over classical approaches.

Findings

Achieves exponential reduction in qubit number compared to previous quantum algorithms.

Maintains quadratic speedup over classical matched filtering.

Uses quantum Monte Carlo integration and amplitude amplification for improved performance.

Abstract

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.