quant-ph · 2026-08-26 · No. 96

Quantum Physics, 2026-08-26.

3 new papers in quant-ph. Titles, authors, abstracts. Links to arXiv. Want this in your inbox every morning? Subscribe →

01 — The papers

3 entries
  1. 01

    When Similarity Is Interaction-Driven: Quantum Kernels for Regime-Sensitive Learning

    Hanqiu Peng, Jianlong Lu, Ying Chen

    quant-ph · cs.LG

    Similarity in many decision systems is governed not by distance alone but by interactions among variables. In fraud and anomaly detection, small local perturbations can cross interaction-sensitive decision boundaries while leaving ambient distance almost unchanged. Motivated by this setting, we introduce a thin-slab interaction model and an interaction-driven quantum kernel constructed from entangled Pauli-string feature maps. The feature map...

    arxiv.org/abs/2608.24631 · PDF

  2. 02

    Provable Quantum--Classical Separation for Continuous Gibbs Sampling

    Enrico Olivucci, Mariia Sobchuk, Sehmimul Hoque, Jeffrey Hnybida, Kyungho W. Kim, Ala Shayeghi, Pooya Ronagh

    quant-ph · cs.DS · cs.ET · cs.LG

    We prove the first quantum--classical separation for a sampling problem over a continuous domain. For a class of Gibbs states $p\propto e^{-βE}$ on the torus $\mathbb{T}^d$ with smooth ($s$-Gevrey) potential and barrier amplitude $α=e^{βΔ}$, where $Δ= \max E-\min E$, every classical algorithm---querying the value, gradient, or any higher-order derivatives of the log-density---requires $Ω(α)$ queries to sample at constant accuracy in total...

    arxiv.org/abs/2608.24527 · PDF

  3. 03

    A Theory of Finite-Noise Optima and Generalization in Quantum Machine Learning

    Ziyu Zhang, Zikang Jia, Xiaosong Li, Yulong Dong

    quant-ph · cs.LG

    Quantum noise is expected to degrade quantum machine learning by driving circuits away from their noiseless implementations. Yet recent studies show moderate noise can reduce testing error, a behavior unexplained by weak-noise perturbative error accumulation or strong-noise trainability collapse. Here we develop a statistical learning theory connecting microscopic noise processes to macroscopic learning performance. At its heart is a...

    arxiv.org/abs/2608.24229 · PDF

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