arXiv Machine Learning By Ait Haddou Marwan

The Signal Horizon: Local Blindness and the Contraction of Pauli-Weight Spectra in Noisy Quantum Encodings

Read the original on arXiv Machine Learning →

arXiv:2602. 14735v2 Announce Type: replace-cross Abstract: The performance of quantum classifiers is typically analyzed through global state distinguishability or the trainability of variational models.

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arXiv Machine Learning
Jun 17

Resource-Efficient Variational Quantum Classifier

arXiv:2511. 09204v3 Announce Type: replace-cross Abstract: We introduce the unambiguous quantum classifier based on Hamming distance measurements combined with classical post-processing.

By Petr Pt\'a\v{c}ek, Paulina Lewandowska, Ryszard Kukulski
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
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