Experimental property-reconstruction in a photonic quantum extreme learning machine

• URL: http://arxiv.org/abs/2308.04543v1
• Date: Tue, 8 Aug 2023 19:22:03 GMT
• Title: Experimental property-reconstruction in a photonic quantum extreme learning machine
• Authors: Alessia Suprano, Danilo Zia, Luca Innocenti, Salvatore Lorenzo, Valeria Cimini, Taira Giordani, Ivan Palmisano, Emanuele Polino, Nicol\`o Spagnolo, Fabio Sciarrino, G. Massimo Palma, Alessandro Ferraro and Mauro Paternostro
• Abstract summary: 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. 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.
• Score: 43.55994393060723
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent developments have led to the possibility of embedding machine learning tools into experimental 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.

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