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The detection and localization of gamma-ray sources via maximum-likelihood analysis is a complex parameter optimization problem. We address this challenge using quantum annealing, a computational approach that solves optimization tasks by encoding them into the ground state of an Ising Hamiltonian. Specifically, we apply this method to observations from AGILE's Gamma-Ray Imaging Detector (GRID) in the 30 MeV-50 GeV band.
A Hybrid Quantum Neural Network (HQNN) is applied to recover the photometric redshift of a galaxy dataset built using data from two astronomical surveys, the Kilo-Degree Survey (KIDS) and the VISTA Kilo-Degree Infrared Galaxy (VIKING), which provide data with coverage from the optical to the near-infrared electromagnetic spectrum. The results are then compared with those obtained by a classical neural network.
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