Quantum Convolutional Neural Networks for the detection of Gamma-Ray Bursts in the AGILE space mission data

TL;DR

This paper explores the application of quantum convolutional neural networks to detect Gamma-Ray Bursts in space mission data, demonstrating promising accuracy with potential advantages over classical methods.

Contribution

It introduces quantum convolutional neural networks for astrophysical data analysis and compares their performance to classical neural networks using simulated quantum frameworks.

Findings

QCNN achieved 95.1% accuracy on sky maps.

Classical CNN achieved 98.8% accuracy with more parameters.

Quantum models show potential for efficient astrophysical data classification.

Abstract

Quantum computing represents a cutting-edge frontier in artificial intelligence. It makes use of hybrid quantum-classical computation which tries to leverage quantum mechanic principles that allow us to use a different approach to deep learning classification problems. The work presented here falls within the context of the AGILE space mission, launched in 2007 by the Italian Space Agency. We implement different Quantum Convolutional Neural Networks (QCNN) that analyze data acquired by the instruments onboard AGILE to detect Gamma-Ray Bursts from sky maps or light curves. We use several frameworks such as TensorFlow-Quantum, Qiskit and PennyLane to simulate a quantum computer. We achieved an accuracy of 95.1% on sky maps with QCNNs, while the classical counterpart achieved 98.8% on the same data, using however hundreds of thousands more parameters.