Experimental Property Reconstruction in a Photonic Quantum Extreme Learning Machine

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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 characterization.
Funding Information
  • Ministero dell’Università e della Ricerca (2022FEXLYB, 2022FEXLYB, PE0000023-NQSTI)
  • Australian Wildlife Society
  • HORIZON EUROPE Marie Sklodowska-Curie Actions (945231)
  • H2020 Future and Emerging Technologies (766900)
  • HORIZON EUROPE European Innovation Council (101046973)
  • Leverhulme Trust (RPG-2018-266)
  • Engineering and Physical Sciences Research Council (EP/T028424/1)
  • Department for the Economy
  • European Research Council (884676)
  • Royal Society Wolfson fellowship (RSWF/R3/183013)
  • Piano Nazionale di Ripresa e Resilienza

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