Photometric Redshift Estimation with Hybrid Quantum Neural Network
Abstract
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. We see that HQNN consistently recovers the reference photometric redshift with a high statistical fidelity. Furthermore, both the R2 and the MSE found by the HQNN are consistently better than those found by the classical counterpart (albeit consistent in the error range), proving the worth of further investigating these novel technologies for this particular astrophysical application.
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BibTeX
@misc{sarracino2026photometric,
title = {{Photometric Redshift Estimation with Hybrid Quantum Neural Network}},
author = {Giuseppe Sarracino and Vincenzo Fabrizio Cardone and Irene Graziotti and Crescenzo Tortora and Roberto Scaramella and Andrea Bulgarelli and Carlo Burigana and Farida Farsian and Massimo Meneghetti and Giuseppe Murante and Niccolò Parmiggiani and Alessandro Rizzo and Francesco Schillirò and Vincenzo Testa and Tiziana Trombetti and Fabio Roberto Vitello},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.35196},
eprint = {2610.35196},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.35196},
}
RIS
TY - UNPB
TI - Photometric Redshift Estimation with Hybrid Quantum Neural Network
AU - Giuseppe Sarracino
AU - Vincenzo Fabrizio Cardone
AU - Irene Graziotti
AU - Crescenzo Tortora
AU - Roberto Scaramella
AU - Andrea Bulgarelli
AU - Carlo Burigana
AU - Farida Farsian
AU - Massimo Meneghetti
AU - Giuseppe Murante
AU - Niccolò Parmiggiani
AU - Alessandro Rizzo
AU - Francesco Schillirò
AU - Vincenzo Testa
AU - Tiziana Trombetti
AU - Fabio Roberto Vitello
PY - 2026
DA - 2026/10/26
PB - arXiv
DO - 10.48550/arxiv.2610.35196
UR - https://arxiv.org/abs/2610.35196
AB - 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. We see that HQNN consistently recovers the reference photometric redshift with a high statistical fidelity. Furthermore, both the R^2 and the MSE found by the HQNN are consistently better than those found by the classical counterpart (albeit consistent in the error range), proving the worth of further investigating these novel technologies for this particular astrophysical application.
ER -