quant-ph · 2026-09-08 · No. 109

Quantum Physics, 2026-09-08.

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

    AxQM: A Textbook-Scale Benchmark for Formal Proof Synthesis in a Library of Finite-Dimensional Quantum Mechanics

    Weichen Winston Yin, Jacob M. Taylor, Dirk R. Englund, Frank H. L. Koppens

    quant-ph · cs.AI · cs.LO

    Formalizing mathematics in a proof assistant, where a machine checks every definition, statement and proof, has set a new standard of rigor. Large language models are now capable of formalizing autonomously, even at the scale of whole textbooks. We bring this standard of rigor to physics, where theoretical arguments carry idealizations that are rarely stated fully, and any logical gaps could have a cascading effect on interdependent results....

    arxiv.org/abs/2609.05157 · PDF

  2. 02

    Impact of Data Loss in Postprocessing on Training and Inference of Quantum Neural Networks

    Soraya V. Panambalom, Edoardo Altamura, Nick Chancellor, Jonte R. Hance

    quant-ph · cs.ET · cs.LG · physics.comp-ph

    As quantum hardware scales to larger devices, the classical software layers that interface with it must evolve in step. Postprocessing routines developed and tested primarily in simulator settings can encode assumptions that no longer hold on utility-scale devices, leading to data loss that can be difficult to detect from high-level model outputs alone. We present a case study of \texttt{SamplerQNN}, the sampling-based quantum neural network...

    arxiv.org/abs/2609.05060 · PDF

  3. 03

    Qlippy: A Retrieval-Augmented GenAI Assistant for Reproducible Quantum Workflows and Experiment Tracking

    Mahee Gamage, Vlad Stirbu

    quant-ph · cs.AI

    Quantum software development is iterative and error-prone. Noisy hardware and repeated re-execution make experiment tracking, provenance, and reproducibility essential, yet these practices are hard to adopt because of tooling complexity and the specialized knowledge they demand. General-purpose language models can help but tend to hallucinate and lack grounding in domain-specific tooling. We present Qlippy, a retrieval-augmented GenAI...

    arxiv.org/abs/2609.05039 · PDF

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