← Back to all news
arXiv Machine Learning By Markus Gross, Hans-Martin Rieser

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

Read the original on arXiv Machine Learning →

The paper develops a theoretical framework for quantum extreme learning machines (QELMs) using the Pauli transfer matrix (PTM) formalism. It shows how encoding, reservoir dynamics, and measurement observables jointly determine the nonlinear information processing capacity of a QELM, and provides interpretable diagnostics that can guide model design. The authors apply their analysis to forecasting nonlinear dynamical systems, demonstrating that a trained QELM’s readout closely reproduces the underlying flow map at leading orders.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

Related stories

arXiv Machine Learning
Aug 21

Quantum Gaussian processes for prediction of channel observations

arXiv:2608. 19306v1 Announce Type: cross Abstract: Given a set of input states, we consider the task of predicting the expectation value of a Pauli observable at the output of an unknown quantum evolution, using only a limited number of measurements.

By Jonas J\"ager, Yaroslav Khmelnitskiy, Paolo Braccia, Artur Miroszewski, Diego Garc\'ia-Mart\'in, M. Cerezo, Piotr Czarnik
safety
More like this →
arXiv Machine Learning
Sep 25

Task-Resolved Fisher Spectroscopy for Quantum Reservoir Computing

The paper introduces task‑resolved Fisher spectroscopy for quantum reservoir computing, defining orthonormal score coordinates from prediction targets that reweight labeled histories to produce an affine family of reservoir states and measurement outcomes. It establishes a Fisher‑information hierarchy linking state quantum Fisher information, measurement record Fisher information, and moment matrices up to many‑body order, providing a quadratic form that equals the stationary capacity of the optimal linear readout. The method requires only stationary labeled records and measured outcomes, enabling predictions of held‑out capacities, necessary feature order, and measurement‑budget dependence, and demonstrates how optimizing local measurement axes can recover hidden task information in a five‑spin open reservoir.

By Yang Peng
benchmarks
More like this →
arXiv Machine Learning
Sep 17

Fourier Analysis of Parametrized Interactive Quantum Classifiers

The paper derives a closed‑form expression for the reduced quantum channel of a single‑qubit Interactive Quantum Classifier (IQC), revealing that Hamiltonian parameters directly control the constant, sine, and cosine components of the classifier output. This Fourier interpretation motivates a generalized family of Hamiltonian encodings, including matrix‑parameterized environmental Hamiltonians that produce non‑separable Fourier structures. Numerical experiments on synthetic and real‑world datasets demonstrate that these generalized encodings improve classification performance on several nonlinear benchmarks, with a simpler four‑parameter extension achieving comparable results with fewer trainable parameters.

By F\'abio Novaes, Fernando M. de Paula Neto, Jo\~ao V. M. Cardoso
benchmarks
More like this →