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lambeq: An Efficient High-Level Python Library for Quantum NLP

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arxiv 2110.04236 v1 pith:BSVSHDZD submitted 2021-10-08 cs.CL cs.AIquant-ph

Dimitri Kartsaklis , Ian Fan , Richie Yeung , Anna Pearson , Robin Lorenz , Alexis Toumi , Giovanni de Felice , Konstantinos Meichanetzidis
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Stephen Clark Bob Coecke
This is my paper · ORCID
classification cs.CLcs.AIquant-ph
keywords quantumlambeqdiagramshigh-levelimplementinglibrarymodulesnumber
verification ladder T0 review T1 audit T2 compute T3 formal
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We present lambeq, the first high-level Python library for Quantum Natural Language Processing (QNLP). The open-source toolkit offers a detailed hierarchy of modules and classes implementing all stages of a pipeline for converting sentences to string diagrams, tensor networks, and quantum circuits ready to be used on a quantum computer. lambeq supports syntactic parsing, rewriting and simplification of string diagrams, ansatz creation and manipulation, as well as a number of compositional models for preparing quantum-friendly representations of sentences, employing various degrees of syntax sensitivity. We present the generic architecture and describe the most important modules in detail, demonstrating the usage with illustrative examples. Further, we test the toolkit in practice by using it to perform a number of experiments on simple NLP tasks, implementing both classical and quantum pipelines.

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Cited by 5 Pith papers

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  1. Quantum Compositional NLP for Arabic: Grammar, Morphology, and Word Sense in Circuit Topology

    cs.CL 2026-05 conditional novelty 6.0 of 10

    On matched-pair Arabic word-order classification, quantum circuits with grammar-derived topology and one entangling layer score 64.9%, versus exactly 50% with no entanglement, isolating the causal contribution of enta...

  2. Extending the Frontiers of QNLP Beyond English: Grammar-Sensitive Pipeline for Hindi Sentiment Classification Using Compositional Quantum Models

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    A grammar-aware quantum NLP pipeline with a custom negation rule classifies Hindi sentiment at 55–73% accuracy on a manually annotated 250-sentence dataset in simulation.

  3. Compositional Concept Generalization with Variational Quantum Circuits

    cs.AI 2025-09 conditional novelty 5.0 of 10

    Variational quantum circuits with DisCoCat sentence structure outperform classical DisCoCat on a toy left/right image captioning benchmark, with MHE encodings working best and CLIP results near chance.

  4. A Rose by Any Other Name Would Smell as Sweet: Categorical Homotopy Theory for Large Language Models

    cs.CL 2025-08 reject novelty 5.0 of 10

    The paper argues that LLM next-token distributions form Markov categories whose paraphrase equivalences can be studied by homotopy theory, but its main theorem is unsupported.

  5. Towards a Comparative Framework for Compositional AI Models

    cs.CL 2025-06 conditional novelty 5.0 of 10

    A categorical framework for compositional generalisation is applied to DisCoCirc models, showing quantum circuits outperform neural networks on systematicity while neural models overfit more.

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