Experimental property-reconstruction in a photonic quantum extreme learning machine

TL;DR

This paper demonstrates a resource-efficient quantum machine learning method using photonic quantum walks to accurately reconstruct unknown photon polarization states without detailed measurement calibration.

Contribution

It introduces a novel experimental quantum extreme learning machine leveraging high-dimensional photonic quantum walks for robust quantum state characterization.

Findings

Achieved accurate polarization state reconstruction without detailed measurement calibration.

Demonstrated robustness of the method against experimental imperfections.

Showed resource efficiency in quantum state characterization.

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.