mlsanityData to 14 Oct 2026

QOuLiPo: What a quantum computer sees when it reads a book

arXiv 2605.14188 ↗First posted 13 May 2026quant-ph cs.CL cs.DLcited by 0

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Abstract (arXiv)

What does a book look like to a quantum computer? This paper takes eight classical works of the Renaissance and its late-antique inheritance -- from Augustine to Galileo -- and runs each through a neutral-atom quantum processor. The bridge is graphs: each textual unit becomes an atom, and graph edges are physical blockade constraints for engineered exact unit-disk designs, or a 2D approximation to the semantic graph for natural texts. Three contributions follow. First, we introduce rigidity rho, a metric for how unique a book's structural backbone is -- distinguishing Marguerite de Navarre's Heptameron (rigid, twelve-nouvelle hard core) from Boethius (fully fungible, every chapter substitutable). Second, we invert the pipeline: rather than extracting a graph from existing prose, we pick a target graph the hardware encodes natively, and write a book whose structure matches it. The twenty-nine texts written this way, collected under the name QOuLiPo, extend the OuLiPo tradition to graph-topological constraints and, together with the eight natural texts, form a benchmark distribution against which neutral-atom hardware can be tracked as it scales. Third, we run both natural and engineered texts on Pasqal's FRESNEL processor up to one hundred atoms; engineered texts reach high approximation ratios, the cleanest instances returning the exact backbone. A cloud-accessible quantum machine plus an agentic coding environment now lets a single investigator run this pipeline end-to-end. What is reported is an application layer, not a speedup -- humanistic instances ready to load onto neutral-atom processors as they scale, already complementing classical text analysis. The Digital Humanities community has a stake in building familiarity with this hardware now: the engineered-corpus design choices made today fix the benchmark distribution future hardware will be measured against.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 11 of its 75 references that are in the map. First posted on arXiv 13 May 2026.

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11 of 75 references.

References · 11

  1. 2024M3-Embedding: Multi-Linguality, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillationcited by 1,073
  2. 2024Multilingual E5 Text Embeddings: A Technical Reportcited by 372
  3. 2025MMTEB: Massive Multilingual Text Embedding Benchmarkcited by 125
  4. 2021QNLP in Practice: Running Compositional Models of Meaning on a Quantum Computercited by 67
  5. 2024Arctic-Embed 2.0: Multilingual Retrieval Without Compromisecited by 58
  6. 2020Grammar-aware sentence classification on quantum computerscited by 17
  7. 2026Computational Hermeneutics: Evaluating generative AI as a cultural technologycited by 8
  8. 2014Probing the topological properties of complex networks modeling short written textscited by 5
  9. 2024IICONGRAPH: improved Iconographic and Iconological Statements in Knowledge Graphscited by 4
  10. 2013Identification of Literary Movements Using Complex Networks to Represent Textscited by 3
  11. 2021From Digital Humanities to Quantum Humanities: Potentials and Applicationscited by 1

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