quant-ph · 2026-07-28 · No. 67

Quantum Physics, 2026-07-28.

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

01 — The papers

4 entries
  1. 01

    Efficient LLM-Generated Shuttling Compilers for Complex Trapped-Ion Architectures

    Fabian Kreppel, Reza Salkhordeh, Ferdinand Schmidt-Kaler, André Brinkmann

    quant-ph · cs.AI · cs.ET

    Trapped-ion quantum computers rely on shuttling compilers, which cast an input algorithm into a sequence of ion-qubit movements within a given architecture. We present the first study in which a single frontier large language model (LLM), Claude Opus 4.7, generates and iteratively refines the full Python code of shuttling compilers from written specifications. We start with a compiler for (i) a linear segmented trap, extend it to (ii) a trap...

    arxiv.org/abs/2607.24714 · PDF

  2. 02

    Stacking the Deck: Tunable Trainability in Stacked LCUs

    Nikhil Khatri, Stefan Zohren, Gabriel Matos

    quant-ph · cs.LG

    Variational quantum circuits have been central to many proposed near-term applications of quantum computing, but a growing body of evidence suggests that trainability and quantum advantage are fundamentally at odds: ansätze expressive enough to resist efficient classical simulation tend to exhibit barren plateaus, while structures that provably rule out barren plateaus typically render them classically simulable. We propose a stacked linear...

    arxiv.org/abs/2607.24686 · PDF

  3. 03

    Multivariate Time Series Forecasting with Adaptive Non-Local Observables

    Yu-Ting Lee, Huan-Hsin Tseng, Samuel Yen-Chi Chen

    quant-ph · cs.AI · cs.LG

    Multivariate time series forecasting (MTSF) predicts future values of multiple variables from historical data. While quantum neural networks have been increasingly applied to this task, they typically rely on fixed local measurements, which restrict their expressivity. We propose MTSF-ANO, a simple hybrid model for MTSF that integrates variational quantum circuits with adaptive non-local observables (ANO). On the four ETT datasets, MTSF-ANO...

    arxiv.org/abs/2607.24399 · PDF

  4. 04

    Variational Quantum Conditional Boltzmann Machines for Time-Series Forecasting: Architectures, Symmetric Hyperparameter Evaluation, and a Nonlinear Benchmark

    Gerhard Hellstern, Danyal Maheshwari, Martin Zaefferer, Martin Braun, Tanja Döhler

    quant-ph · cs.LG · q-fin.ST

    In this study, we developed and evaluated four conditional energy-based forecasting architectures: a classical Gaussian-Bernoulli CRBM, a hybrid quantum-classical QCRBM, a full-register QQRBM, and a lag-feature QFeatureQRBM with complete derivations of their conditional distributions, Contrastive-Divergence gradients, and hybrid training, bridging the energy-based formulation and the implementation-level quantum computation. Unlike prior...

    arxiv.org/abs/2607.24065 · PDF

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