Gravitational-wave matched filtering with variational quantum algorithms

TL;DR

This paper investigates the use of variational quantum algorithms for gravitational wave detection via matched filtering, comparing their performance to Grover search, and finds classical algorithms outperform the quantum approaches tested.

Contribution

It introduces quantum walk-based variational algorithms for gravitational wave matched filtering and evaluates their effectiveness against classical and Grover search methods.

Findings

Variational quantum algorithms underperform compared to Grover search.

Classical numerical simulations show limited advantage of quantum algorithms.

Grover search remains the most efficient method among those tested.

Abstract

In this paper, we explore the application of variational quantum algorithms designed for classical optimization to the problem of matched filtering in the detection of gravitational waves. Matched filtering for detecting gravitational wave signals requires searching through a large number of template waveforms, to find one which is highly correlated with segments of detector data. This computationally intensive task needs to be done quickly for low latency searches in order to aid with follow-up multi-messenger observations. The variational quantum algorithms we study for this task consist of quantum walk-based generalizations of the Quantum Approximate Optimization Algorithm (QAOA). We present results of classical numerical simulations of these quantum algorithms using open science data from LIGO. These results show that the tested variational quantum algorithms are outperformed by an unstructured restricted-depth Grover search algorithm, suggesting that the latter is optimal for this computational task.