Theory and interpretability of Quantum Extreme Learning Machines: a Pauli-transfer matrix approach

• URL: http://arxiv.org/abs/2602.18377v1
• Date: Fri, 20 Feb 2026 17:33:27 GMT
• Title: Theory and interpretability of Quantum Extreme Learning Machines: a Pauli-transfer matrix approach
• Abstract summary: We consider n-qubit quantum extreme learning machines (QELMs) with continuous-time reservoir dynamics.<n>We apply the Pauli transfer matrix (PTM) formalism to theoretically analyze the influence of encoding, reservoir dynamics, and measurement operations.<n>We show that a QELM trained on such trajectories learns a surrogate-approximation to the underlying flow map.
• Score: 5.486630950557179
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Quantum reservoir computers (QRCs) have emerged as a promising approach to quantum machine learning, since they utilize the natural dynamics of quantum systems for data processing and are simple to train. Here, we consider n-qubit quantum extreme learning machines (QELMs) with continuous-time reservoir dynamics. QELMs are memoryless QRCs capable of various ML tasks, including image classification and time series forecasting. We apply the Pauli transfer matrix (PTM) formalism to theoretically analyze the influence of encoding, reservoir dynamics, and measurement operations, including temporal multiplexing, on the QELM performance. This formalism makes explicit that the encoding determines the complete set of (nonlinear) features available to the QELM, while the quantum channels linearly transform these features before they are probed by the chosen measurement operators. Optimizing a QELM can therefore be cast as a decoding problem in which one shapes the channel-induced transformations such that task-relevant features become available to the regressor. The PTM formalism allows one to identify the classical representation of a QELM and thereby guide its design towards a given training objective. As a specific application, we focus on learning nonlinear dynamical systems and show that a QELM trained on such trajectories learns a surrogate-approximation to the underlying flow map.

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