Comparing Quantum Machine Learning Approaches in Astrophysical Signal Detection

TL;DR

This paper explores quantum machine learning techniques for astrophysical signal detection, emphasizing the importance of data encoding, and demonstrates their effectiveness in detecting Gamma-Ray Bursts.

Contribution

It introduces a four-step QML workflow and investigates data encoding methods, showing their impact on model performance in astrophysics.

Findings

QML can effectively detect astrophysical signals

Data encoding significantly influences QML performance

Quantum approaches offer promising alternatives to classical ML in astrophysics

Abstract

Machine Learning (ML) serves as a general-purpose, highly adaptable, and versatile framework for investigating complex systems across domains. However, the resulting computational resource demands, in terms of the number of parameters and the volume of data required to train ML models, can be high, often prohibitive. This is the case in astrophysics, where multimedia space data streams usually have to be analyzed. In this context, quantum computing emerges as a compelling and promising alternative, offering the potential to address these challenges in a feasible way. Specifically, a four-step quantum machine learning (QML) workflow is proposed encompassing data encoding, quantum circuit design, model training and evaluation. Then, focusing on the data encoding step, different techniques and models are investigated within a case study centered on the Gamma-Ray Bursts (GRB) signal detection in the astrophysics domain. The results thus obtained demonstrate the effectiveness of QML in astrophysics, highlighting the critical role of data encoding, which significantly affects the QML model performance.