quant-ph · 2026-09-23 · No. 122

Quantum Physics, 2026-09-23.

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

01 — The papers

5 entries
  1. 01

    Quantum Advantage for Distributed Symmetry Breaking

    Maxime Flin, Longcheng Li, Jukka Suomela

    quant-ph · cs.DC

    We present a distributed quantum algorithm that $3$-colors cycles in $O(1)$ rounds, with high probability. It follows that all locally checkable labeling problems (LCLs) that have round complexity $O(\log^* n)$ in the classical LOCAL model can be solved in $O(1)$ rounds in the quantum-LOCAL model, with high probability; this includes problems such as maximal independent set and maximal matching in bounded-degree graphs. This presents the...

    arxiv.org/abs/2609.26788 · PDF

  2. 02

    When are bosonic Gaussian states classical to learn?

    Senrui Chen, Antonio Anna Mele, Francesco Anna Mele, John Preskill

    quant-ph · cs.IT · cs.LG · math-ph

    A fundamental question in physics is: When does classical behavior emerge from quantum systems? Bosonic Gaussian states provide a natural setting to explore this quantum-classical boundary, as they capture both the classical field behavior and the intrinsic quantum nature of light. Here, we address this problem from a learning-theoretic perspective by asking: When are bosonic Gaussian states classical to learn? That is, under what conditions...

    arxiv.org/abs/2609.26705 · PDF

  3. 03

    Hyperbolic Restricted Boltzmann Machine Neural Quantum State

    H. L. Dao

    quant-ph · cond-mat.dis-nn · cs.LG

    We construct the first type of non-Euclidean non-autoregressive neural quantum state (NQS) in the form of the hyperbolic Restricted Boltzmann Machine (HRBM), which is studied in the variational Monte-Carlo (VMC) setting of the Quantum Sherrington-Kirkpatrick (QSK) model whose ground state exhibits volume-law entanglement. Across a 512-fold increase in the Hilbert space dimension corresponding to a system size increase from $N=14$ to $N=24$,...

    arxiv.org/abs/2609.26032 · PDF

  4. 04

    Bridge of $Ψ$'s: Quantum Circuit Optimization with Schrödinger Bridges

    Lino S. Hofstetter, Lia Yeh, Prakash Murali

    quant-ph · cs.LG

    Quantum circuit optimization replaces a circuit with an equivalent one of fewer gates and lower depth, reducing execution cost and error rate. We ask whether a generative model can learn this transformation directly from examples, rather than selecting from a fixed rewrite library or rigid algebraic routines. We present Bridge of $Ψ$'s (BOPS), a generative model based on Schrödinger bridges, using a custom denoiser architecture, that learns a...

    arxiv.org/abs/2609.25947 · PDF

  5. 05

    When Quantum Meets AI: Quantum Methods for Machine Learning and Machine Learning Methods for Quantum Systems

    Tak Hur

    quant-ph · cs.AI

    This thesis studies the intersection of quantum computing and artificial intelligence in two directions: quantum methods for machine learning and machine learning methods for quantum systems. For quantum machine learning, Neural Quantum Embedding learns data representations that increase the trace distance between embedded class ensembles, lowering an embedding-dependent bound on empirical risk and improving classification on noisy quantum...

    arxiv.org/abs/2609.25641 · PDF

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