
pmid: 41385630
pmc: PMC12700208
arXiv: 2502.18361
handle: 2434/1249421 , 10447/695625 , 11573/1764778
pmid: 41385630
pmc: PMC12700208
arXiv: 2502.18361
handle: 2434/1249421 , 10447/695625 , 11573/1764778
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.
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
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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