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Quantum reservoir computing for photonic entanglement witnessing

Authors: Danilo Zia; Luca Innocenti; Giorgio Minati; Salvatore Lorenzo; Alessia Suprano; Rosario Di Bartolo; Nicolò Spagnolo; +6 Authors

Quantum reservoir computing for photonic entanglement witnessing

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.

Countries
Italy, United Kingdom
Keywords

Quantum Physics, photonic quantum computing, quantum reservoir computing; entanglement witnessing; structured light, Settore PHYS-04/A - Fisica teorica della materia, modelli, metodi matematici e applicazioni, quantum machine learning, FOS: Physical sciences, Physical and Materials Sciences, quantum reservoir computing, Quantum Physics (quant-ph), quantum machine learning; quantum property validation; photonic quantum computing; quantum reservoir computing, quantum property validation

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    popularity
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    influence
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
8
Top 10%
Average
Top 10%
Green
gold
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