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Quantum 2.0 Conference and Exhibition

Photonic quantum extreme learning machine

Alessia Suprano, Danilo Zia, Luca Innocenti, Salvatore Lorenzo, Valeria Cimini, Taira Giordani, Ivan Palmisano, Emanuele Polino, Nicolò Spagnolo, Fabio Sciarrino, G. Massimo Palma, Alessandro Ferraro, Mauro Paternostro

Published Jan 1, 2024• 1 citation
Publisher

Abstract

We experimentally implemented a quantum extreme learning machine to re-construct the polarization state of single photons. Our approach offers a resource-efficient method that does not require a detailed apparatus calibration.
DOI
10.1364/quantum.2024.qw4a.2

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Quantum extreme learning machines (QELMs) are unconventional computing architectures that bear remarkable promise in both classical and quantum machine-learning tasks, such as the estimation of quantum state properties. However, the probabilistic nature of quantum measurements demands extensive repetitions for training to precisely estimate expectation values, imposing stringent trade-offs among experimental resources, acquisition time, and signal-to-noise ratio, particularly for large datasets. Here, we introduce a paradigm shift by training the QELM exclusively with intense classical fields, namely twin beams generated by stimulated emission, while performing inference directly on previously unseen genuine quantum input states to predict their quantum properties. This strategy dramatically reduces acquisition times while substantially enhancing the signal-to-noise ratio. Using frequency-bin encoded biphoton states, implemented here for the first time in a quantum machine learning architecture, we demonstrate entanglement witnessing of two-qubit states with (93 +- 4)% accuracy, multi-dimensional entanglement detection, and learning of the Hamiltonian governing photon-pair generation with a fidelity of (96 +- 4)%. Our results open a new pathway toward faster and more robust training of photonic quantum extreme learning machines for quantum feature extraction.
We propose and numerically simulate a photonic quantum extreme learning machine (QELM) based on non-linear interactions in a time-delay non-linear interferometer with photon-number-resolving detection. We theoretically demonstrate the reservoir's computational capability by binary classification of the non-linear two moons benchmark. Analysis of the learned output weights and alternative quantum readouts shows that compact feature sets based on photon-number correlations preserve classification accuracy while potentially reducing measurement complexity. We further show that the QELM maintains its classification performance under the effect of losses by naturally redistributing the learned weights across a larger set of available features. These results demonstrate the resilience of the time-delay photonic implementation and highlight its potential as an experimentally accessible QELM platform.