Efficient classical training of model-free quantum photonic reservoir

TL;DR

This paper presents a classical training method for photonic quantum reservoirs that enables accurate quantum state estimation without relying on prior device models, improving efficiency and adaptability.

Contribution

It introduces a model-free, gradient-based optimization approach for photonic quantum reservoirs using classical light, facilitating accurate quantum state reconstruction.

Findings

Successfully reconstructed single-qubit Pauli observables for unseen states.

Extended the method to estimate bipartite entanglement witnesses.

Demonstrated out-of-distribution generalization across classical and quantum data.

Abstract

Model-independent estimation of the properties of quantum states is a central challenge in quantum technologies, as experimental imperfections, drifts, and imprecise models of the actual quantum dynamics inevitably hinder accurate reconstructions. Here, we introduce a training strategy for photonic quantum extreme learning machines in which both the learning stage and the optimization of the measurement settings are performed entirely with classical light, while inference is carried out on genuinely quantum states. The protocol is based on the identity between the normalized output intensities following the evolution of coherent states through a linear optical reservoir, and the output statistics obtained with separable input quantum states. Building on this correspondence, we implemented a model-free, gradient-based optimization of the reservoir measurement projection directly on experimental data, without relying on a prior model of the device transformation. We experimentally show that the resulting classical-to-quantum transfer enables accurate reconstruction of single-qubit Pauli observables for previously unseen single-photon states, and extends to the estimation of a two-qubit entanglement witness for arbitrary bipartite states. Beyond demonstrating a qualitatively distinct form of out-of-distribution generalization across the classical-to-quantum boundary, our results identify a practical route to fast, adaptive, and resource-efficient training of photonic quantum learning devices.