Entanglement and Classical Simulability in Quantum Extreme Learning Machines

• URL: http://arxiv.org/abs/2509.06873v2
• Date: Fri, 24 Oct 2025 16:57:50 GMT
• Title: Entanglement and Classical Simulability in Quantum Extreme Learning Machines
• Authors: A. De Lorenzis, M. P. Casado, N. Lo Gullo, T. Lux, F. Plastina, A. Riera,
• Abstract summary: We investigate Quantum Extreme Learning Machines (QELMs), a quantum analogue of classical Extreme Learning Machines.<n>Our architecture combines dimensionality reduction (via PCA or Autoencoders), quantum state encoding, evolution under an XX Hamiltonian, and projective measurement.<n>We show that this performance enhancement correlates with the onset of entanglement, which improves the embedding of classical data.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Quantum Machine Learning (QML) has emerged as a promising framework to exploit quantum mechanics for computational advantage. Here we investigate Quantum Extreme Learning Machines (QELMs), a quantum analogue of classical Extreme Learning Machines in which training is restricted to the output layer. Our architecture combines dimensionality reduction (via PCA or Autoencoders), quantum state encoding, evolution under an XX Hamiltonian, and projective measurement to produce features for a classical single-layer classifier. By analyzing the classification accuracy as a function of evolution time, we identify a sharp transition between low- and high-accuracy regimes, followed by saturation. Remarkably, the saturation value coincides with that obtained using random unitaries that generate maximally complex dynamics, even though the XX model is integrable and local. We show that this performance enhancement correlates with the onset of entanglement, which improves the embedding of classical data in Hilbert space and leads to more separable clusters in measurement probability space. Thus, entanglement contributes positively to the structure of the data embedding, improving learnability without necessarily implying computational advantage. For the image classification tasks studied in this work (namely MNIST, Fashion-MNIST, and CIFAR-10) the required evolution time corresponds to information exchange among nearest neighbors and is independent of the system size. This implies that QELMs rely on limited entanglement and remain classically simulable for a broad class of learning problems. Our results clarify how moderate quantum correlations bridge the gap between quantum dynamics and classical feature learning.

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