Recent developments have led to the possibility of embedding machine learning tools into experi- mental platforms to address key problems, including the characterization of the properties of quantum states. Leveraging on this, we implement a quantum extreme learning machine in a photonic platform to achieve resource-efficient and accurate characterization of the polarization state of a photon. The underlying reservoir dynamics through which such input state evolves is implemented using the coined quantum walk of high-dimensional photonic orbital angular momentum, and performing projective measurements over a fixed basis. We demonstrate how the reconstruction of an unknown polarization state does not need a careful characterization of the measurement apparatus and is robust to experimental imperfections, thus representing a promising route for resource-economic state characterisation

Alessia Suprano, Danilo Zia, Luca Innocenti, Salvatore Lorenzo, Valeria Cimini, Taira Giordani, et al. (2024). Experimental property-reconstruction in a photonic quantum extreme learning machine. PHYSICAL REVIEW LETTERS, 132, 160802 [10.1103/PhysRevLett.132.160802].

Experimental property-reconstruction in a photonic quantum extreme learning machine

Luca Innocenti
Co-primo
;
Salvatore Lorenzo
Co-primo
;
G. M Palma;Mauro Paternostro
Ultimo
2024-04-16

Abstract

Recent developments have led to the possibility of embedding machine learning tools into experi- mental platforms to address key problems, including the characterization of the properties of quantum states. Leveraging on this, we implement a quantum extreme learning machine in a photonic platform to achieve resource-efficient and accurate characterization of the polarization state of a photon. The underlying reservoir dynamics through which such input state evolves is implemented using the coined quantum walk of high-dimensional photonic orbital angular momentum, and performing projective measurements over a fixed basis. We demonstrate how the reconstruction of an unknown polarization state does not need a careful characterization of the measurement apparatus and is robust to experimental imperfections, thus representing a promising route for resource-economic state characterisation
16-apr-2024
Settore FIS/03 - Fisica Della Materia
Alessia Suprano, Danilo Zia, Luca Innocenti, Salvatore Lorenzo, Valeria Cimini, Taira Giordani, et al. (2024). Experimental property-reconstruction in a photonic quantum extreme learning machine. PHYSICAL REVIEW LETTERS, 132, 160802 [10.1103/PhysRevLett.132.160802].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10447/627414
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