Quantum reservoir computing for photonic entanglement witnessing

Accurately estimating properties of quantum states, such as entanglement, while essential for the development of quantum technologies, remains a challenging task. Standard approaches to property estimation rely on detailed modeling of the measurement apparatus and a priori assumptions on their working principles. Even small deviations can greatly affect reconstruction accuracy and prediction reliability. Here, we demonstrate that quantum reservoir computing embodies a powerful alternative for witnessing quantum entanglement and, more generally, estimating quantum features from experimental data. We leverage the orbital angular momentum of photon pairs as an ancillary degree of freedom to enable informationally complete single-setting measurements of their polarization. Our approach does not require fine-tuning or refined knowledge of the setup, at the same time outperforming conventional approaches. It automatically adapts to noise and imperfections while avoiding overfitting, ensuring robust reconstruction of entanglement witnesses and paving the way to the assessment of quantum features of experimental multiparty states.

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Publication Details

Journal
Science Advances
Published
2025-12-12
DOI
https://doi.org/10.1126/sciadv.ady7987
Citations
2
Primary Topic
Neural Networks and Reservoir Computing
Type
article
Field-Weighted Citation Impact
4.96

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article

Quantum reservoir computing for photonic entanglement witnessing

Nicolò Spagnolo, Taira Giordani, Salvatore Lorenzo, Fabio Sciarrino et al.
2 citations
Science Advances
Neural Networks and Reservoir Computing
4.96
article

Quantum reservoir computing for photonic entanglement witnessing

Nicolò Spagnolo, Taira Giordani, Salvatore Lorenzo, Fabio Sciarrino, Luca Innocenti, Alessandro Ferraro, Alessia Suprano, Mauro Paternostro, Valeria Cimini, G. Massimo Palma, Danilo Zia, Giorgio Minati, Rosario Di Bartolo
article en
2 citations

Abstract

Accurately estimating properties of quantum states, such as entanglement, while essential for the development of quantum technologies, remains a challenging task. Standard approaches to property estimation rely on detailed modeling of the measurement apparatus and a priori assumptions on their working principles. Even small deviations can greatly affect reconstruction accuracy and prediction reliability. Here, we demonstrate that quantum reservoir computing embodies a powerful alternative for witnessing quantum entanglement and, more generally, estimating quantum features from experimental data. We leverage the orbital angular momentum of photon pairs as an ancillary degree of freedom to enable informationally complete single-setting measurements of their polarization. Our approach does not require fine-tuning or refined knowledge of the setup, at the same time outperforming conventional approaches. It automatically adapts to noise and imperfections while avoiding overfitting, ensuring robust reconstruction of entanglement witnesses and paving the way to the assessment of quantum features of experimental multiparty states.

Science AdvancesVol. 11(50)
Queen's University Belfast (GB), University of Milan (IT), Sapienza University of Rome (IT), University of Palermo (IT)
Department for the Economy, European Commission, HORIZON EUROPE Framework Programme
Openalex Percentile: Top 4%
Neural Networks and Reservoir Computing
4.96
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