quant-ph · 2026-07-23 · No. 62

Quantum Physics, 2026-07-23.

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

01 — The papers

6 entries
  1. 01

    Statevector-Referenced Geometry Survival of a Four-Qubit ZZ Quantum Kernel on IBM Quantum Hardware: A Fixed-Subset Diagnostic Across Three Execution Configurations

    Rostyslav Sipakov

    quant-ph · cs.LG

    Quantum-kernel methods encode a dataset's geometry in a Gram matrix, so learning claims on hardware kernels assume the intended geometry survives execution. We measure that survival for one frozen four-qubit ZZ feature-map kernel on $N=24$ real indoor air-quality windows, reconstructed on ibm_fez (1024 shots per circuit) under baseline, dynamical decoupling alone, and gate twirling alone, each a single non-interleaved job. Every configuration...

    arxiv.org/abs/2607.20377 · PDF

  2. 02

    DQAOA-GPT: AI-Accelerated Distributed Quantum Optimization for Combinatorial Problems

    Seongmin Kim, Abhinav Rijal, Yuri Alexeev, Nora Bauer, Martin Roetteler, Mina Yoon, George Siopsis, In-Saeng Suh

    quant-ph · cs.AI · cs.DC · math.OC

    While combinatorial optimization problems are central to many scientific and engineering applications, their solution remains challenging due to exponentially large search spaces. Variational quantum algorithms offer a promising route for tackling such problems, yet their practical performance is limited by repeated quantum circuit evaluations and classical parameter updates. In this work, we introduce DQAOA-GPT, a hybrid framework that...

    arxiv.org/abs/2607.20225 · PDF

  3. 03

    PN-QNN: Harnessing Physical Noise as a Native Regularizer in Photonic Hybrid Quantum Neural Networks

    Farah Elnakhal, Alberto Marchisio, Nouhaila Innan, Gabriel Falcao, Muhammad Shafique

    quant-ph · cs.LG

    Physical noise in near-term quantum hardware is usually treated as a nuisance to suppress. We ask whether it can instead act as a hardware-native regularizer for photonic hybrid quantum-classical neural networks (PHQCNNs), analogous to noise-injection regularization in classical deep learning. Using Quandela's Perceval simulator and the MerLin framework, we build PHQCNNs for Iris, Digits, and MNIST and inject Perceval's seven-parameter...

    arxiv.org/abs/2607.20045 · PDF

  4. 04

    A Multiclass Quantum Aligned Centroid Kernel

    Kilian Tscharke, Pascal Debus

    quant-ph · cs.LG

    Kernel methods are powerful tools in machine learning but commonly used full-Gram kernels face three key limitations: (1) quadratic scaling with training set size; (2) the use of fixed, non-trainable kernels; and (3) the absence of an intrinsic formulation for multiclass classification. We present McQuack, a trainable quantum kernel method for multiclass problems that achieves linear scaling in the number of training samples. This is...

    arxiv.org/abs/2607.19782 · PDF

  5. 05

    Machine-learned syndrome post-selection for reliable quantum error correction

    Tobias Haug, Askery Canabarro, Leandro Aolita

    quant-ph · cs.LG

    Quantum error correction can be enhanced by post-selecting out runs that are likely to produce a logical failure, but the most accurate measures for that require costly decoder-level information. We introduce a practical, decoder-agnostic post-selection method that learns directly from syndrome data. The method trains a supervised classifier to distinguish between syndromes from low- and high-noise regimes, and then uses the classifier's...

    arxiv.org/abs/2607.19563 · PDF

  6. 06

    Hybrid LLM-Guided Search for Quantum Reservoir Architecture Design

    Krishna Bhatia, Gautami Sanjay Naik

    quant-ph · cs.AI

    Quantum reservoir computing (QRC) uses fixed quantum dynamics as a high-dimensional temporal feature map and trains only a lightweight classical readout. QRC is attractive for near-term quantum machine learning, but its performance depends strongly on architecture choices such as input encoding, reservoir depth, entanglement topology, measurement features, state-reset policy, feature construction, and readout regularization. We introduce...

    arxiv.org/abs/2607.19506 · PDF

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