Quantum Convolutional Neural Networks for the detection of Gamma-Ray Bursts in the AGILE space mission data
This work addresses the problem of efficient astrophysical event detection for space missions, but it is incremental as it shows quantum methods are not yet superior to classical ones in accuracy.
The paper tackled the detection of Gamma-Ray Bursts using Quantum Convolutional Neural Networks (QCNNs) on data from the AGILE space mission, achieving 95.1% accuracy on sky maps, compared to 98.8% for a classical model with many more parameters.
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.