Efficient classical training of model-free quantum photonic reservoir

• URL: http://arxiv.org/abs/2604.12441v1
• Date: Tue, 14 Apr 2026 08:29:38 GMT
• Title: Efficient classical training of model-free quantum photonic reservoir
• Abstract summary: We introduce a training strategy for photonic quantum extreme learning machines.<n>Both the learning stage and the optimization of the measurement settings are performed entirely with classical light.<n>We experimentally show that the resulting classical-to-quantum transfer enables accurate reconstruction of single-qubit Pauli observables.
• Score: 28.404018926483985
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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