Quantum Annealing for Gamma-ray Source Detection and Localization with AGILE Data
Abstract
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. We reformulated the standard analysis as a Quadratic Unconstrained Binary Optimization (QUBO) problem. We tested this method on a set of AGILE observations generated with AGILEpy, solving the QUBO with simulated quantum annealing. The method reproduces the classical flux and position estimates within their statistical uncertainty and identifies the correct grid node among the candidate positions in 97% of the maps. These promising results demonstrate that quantum annealing could be used in this optimization problem, laying the ground for its deployment on actual quantum annealers.
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BibTeX
@misc{rizzo2026quantum,
title = {{Quantum Annealing for Gamma-ray Source Detection and Localization with AGILE Data}},
author = {Alessandro Rizzo and Nicolò Parmiggiani and Andrea Bulgarelli and Gabriele Panebianco and Carlo Burigana and Vincenzo Fabrizio Cardone and Farida Farsian and Massimo Meneghetti and Giuseppe Murante and Giuseppe Sarracino and Roberto Scaramella and Francesco Schillirò and Vincenzo Testa and Tiziana Trombetti and Fabio Roberto Vitello},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.37040},
eprint = {2610.37040},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.37040},
}
RIS
TY - UNPB
TI - Quantum Annealing for Gamma-ray Source Detection and Localization with AGILE Data
AU - Alessandro Rizzo
AU - Nicolò Parmiggiani
AU - Andrea Bulgarelli
AU - Gabriele Panebianco
AU - Carlo Burigana
AU - Vincenzo Fabrizio Cardone
AU - Farida Farsian
AU - Massimo Meneghetti
AU - Giuseppe Murante
AU - Giuseppe Sarracino
AU - Roberto Scaramella
AU - Francesco Schillirò
AU - Vincenzo Testa
AU - Tiziana Trombetti
AU - Fabio Roberto Vitello
PY - 2026
DA - 2026/10/27
PB - arXiv
DO - 10.48550/arxiv.2610.37040
UR - https://arxiv.org/abs/2610.37040
AB - 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. We reformulated the standard analysis as a Quadratic Unconstrained Binary Optimization (QUBO) problem. We tested this method on a set of AGILE observations generated with AGILEpy, solving the QUBO with simulated quantum annealing. The method reproduces the classical flux and position estimates within their statistical uncertainty and identifies the correct grid node among the candidate positions in 97% of the maps. These promising results demonstrate that quantum annealing could be used in this optimization problem, laying the ground for its deployment on actual quantum annealers.
ER -