  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.
