---
title: "Meaning Representations as Variational — Limbäck-Stokin 2026"
description: "Vision-language models such as CLIP often fail when the right and wrong captions differ only in structure, for example swapped subject and object or…"
authors: ["Tilen Gaetano Limbäck-Stokin", "Tanishka A. Birdavade", "Kin Ian Lo", "Mehrnoosh Sadrzadeh"]
year: 2026
venue: "Proceedings of The Seventh International Workshop on Designing Meaning Representations (DMR 2026) @ LREC 2026"
source: "https://aclanthology.org/2026.dmr-1.10/"
doi: "10.63317/2qyxbre9ncwk"
licence: "CC BY-NC 4.0 (ELRA, LREC proceedings)"
licence_url: "https://creativecommons.org/licenses/by-nc/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/stokin-2026-meaning-representations-as-variational-quantum/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/stokin-2026-meaning-representations-as-variational-quantum/)

[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [2026](https://md.research.elamur.ai/free/en/papers/2026/page/7/) / Meaning Representations as Variational Quantum Circuits

# Meaning Representations as Variational Quantum Circuits

**Authors:** Tilen Gaetano Limbäck-Stokin, Tanishka A. Birdavade, Kin Ian Lo, Mehrnoosh Sadrzadeh · **Year:** 2026 · **Venue:** Proceedings of The Seventh International Workshop on Designing Meaning Representations (DMR 2026) @ LREC 2026 · **Licence:** [CC BY-NC 4.0 (ELRA, LREC proceedings)](https://creativecommons.org/licenses/by-nc/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.dmr-1.10/) · [PDF](https://aclanthology.org/2026.dmr-1.10.pdf) · [DOI 10.63317/2qyxbre9ncwk](https://doi.org/10.63317/2qyxbre9ncwk) · [OpenAlex](https://openalex.org/W7166522313)

**Citations:** 0 citations (OpenAlex, 2026-10-14)

## Summary
Vision-language models such as CLIP often fail when the right and wrong captions differ only in structure, for example swapped subject and object or misattached attributes. They also depend on very large parameter counts. The authors propose CCG-VQC, a meaning representation in which a sentence's syntax determines the layout of a quantum circuit. Sentences are parsed with the Bobcat CCG parser. Atomic types become qubits, each word becomes a trainable parametrised circuit, and grammatical combination is carried out through Bell measurements with post-selection. Each sentence is therefore encoded as a quantum state. Caption–image similarity is scored with quantum fidelity, and training uses a new contrastive objective called QInfoNCE. Images are encoded by a frozen classical vision transformer followed by a simple circuit. On ARO and SVO-Swap, the model outperforms a quantum bag-of-words baseline, CLIP, OpenCLIP and a parameter-matched MicroCLIP. This suggests that explicit grammatical structure can partly replace scale. Captions with deeply nested relational structure remain difficult for the model.

## Key points
- Accuracy of CCG-VQC: 83.16% on SVO-Swap, 71.19% on ARO-Attribution and 57.33% on ARO-Relation.
- The QBoW baseline, which has no structure, stays at chance level (50%) on all datasets.
- MicroCLIP, a small transformer with the same parameter count as CCG-VQC, reaches at most 50.85% on ARO.
- CCG-VQC beats CLIP and OpenCLIP while using about two orders of magnitude fewer parameters.
- Error analysis on ARO-Relation shows that captions with deeply nested prepositional phrases produce very similar text states, which weakens discrimination. Late fusion with a frozen image encoder adds further limits.
- Proposed future work includes UD-based circuit topologies, early fusion, pre-training on MSCOCO and ConceptualCaptions, and tests on noisy quantum hardware.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

Fields: [Linguistics & languages](https://md.research.elamur.ai/free/en/shelves/linguistics/)

Topics: [Syntax & grammar formalisms](https://md.research.elamur.ai/free/en/topics/syntax-grammar/) · [Semantic parsing & meaning representation](https://md.research.elamur.ai/free/en/topics/semantic-parsing/)

## Cite

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{limbackstokin2026meaning,
  title = {{Meaning Representations as Variational Quantum Circuits}},
  author = {Limbäck-Stokin, Tilen Gaetano and Birdavade, Tanishka A. and Lo, Kin Ian and Sadrzadeh, Mehrnoosh},
  booktitle = {Proceedings of The Seventh International Workshop on Designing Meaning Representations (DMR 2026) @ LREC 2026},
  year = {2026},
  doi = {10.63317/2qyxbre9ncwk},
  url = {https://doi.org/10.63317/2qyxbre9ncwk}
}
```

- [← previous Meaning Over Morphology: A Multi-Metric Benchmark of LLMs for Bangla Dialect Translation](https://md.research.elamur.ai/free/en/papers/niloy-2026-meaning-over-morphology-multi-metric/)
- [next → Meaning-Making Process and Error Dynamics in ChatGPT-Mediated Translation](https://md.research.elamur.ai/free/en/papers/mihalache-2026-meaning-making-process-error-dynamics/)
- [↑ back to papers of 2026](https://md.research.elamur.ai/free/en/papers/2026/page/7/)

## References

45 references, 6 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Emily M. Bender and Alexander Koller. 2020. Climbing towards NLU: On meaning, form, and understanding in the age of data. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, Online. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Emily+M%2E+Bender+and+Alexander+Koller%2E+2020%2E+Climbing+towards+NLU%3A+On+meaning%2C+form%2C+and+understanding+in+the+age+of+data%2E+In+Proceedings+of+the+58th+Annual+Meeting+of+the+Association+for+Computational) [our card](https://md.research.elamur.ai/free/en/papers/bender-2020-climbing-nlu-meaning-form-understanding/)
2. Patrick Blackburn and Johan Bos. 2005. Representation and Inference for Natural Language: A First Course in Computational Semantics. Center for the Study of Language and Information. [find](https://scholar.google.com/scholar?q=Patrick+Blackburn+and+Johan+Bos%2E+2005%2E+Representation+and+Inference+for+Natural+Language%3A+A+First+Course+in+Computational+Semantics%2E+Center+for+the+Study+of+Language+and+Information%2E)
3. D. A. Boiko, R. MacKnight, B. Kline, et al. 2023. Autonomous chemical research with large language models. Nature, 624:570–578. [find](https://scholar.google.com/scholar?q=D%2E+A%2E+Boiko%2C+R%2E+MacKnight%2C+B%2E+Kline%2C+et+al%2E+2023%2E+Autonomous+chemical+research+with+large+language+models%2E+Nature%2C+624%3A570%E2%80%93578%2E) [our card](https://md.research.elamur.ai/free/en/papers/boiko-2023-autonomous-chemical-research-large-language/)
4. M. Cerezo, Akira Sone, Tyler Volkoff, Lukasz Cincio, and Patrick J. Coles. 2021. Cost function dependent barren plateaus in shallow parametrized quantum circuits. Nature Communications, 12(1). [find](https://scholar.google.com/scholar?q=M%2E+Cerezo%2C+Akira+Sone%2C+Tyler+Volkoff%2C+Lukasz+Cincio%2C+and+Patrick+J%2E+Coles%2E+2021%2E+Cost+function+dependent+barren+plateaus+in+shallow+parametrized+quantum+circuits%2E+Nature+Communications%2C+12%281%29%2E)
5. Stephen Clark. 2021. Something old, something new: Grammar-based ccg parsing with transformer models. [find](https://scholar.google.com/scholar?q=Stephen+Clark%2E+2021%2E+Something+old%2C+something+new%3A+Grammar%2Dbased+ccg+parsing+with+transformer+models%2E)
6. Bob Coecke, Edward Grefenstette, and Mehrnoosh Sadrzadeh. 2013. Lambek vs Lambek: Functorial vector space semantics and string diagrams for Lambek calculus. Annals of Pure and Applied Logic, 164(11):1079–1100. [find](https://scholar.google.com/scholar?q=Bob+Coecke%2C+Edward+Grefenstette%2C+and+Mehrnoosh+Sadrzadeh%2E+2013%2E+Lambek+vs+Lambek%3A+Functorial+vector+space+semantics+and+string+diagrams+for+Lambek+calculus%2E+Annals+of+Pure+and+Applied+Logic%2C+164%2811%29%3A1)
7. Bob Coecke, Mehrnoosh Sadrzadeh, and Stephen Clark. 2010. Mathematical foundations for a compositional distributional model of meaning. [find](https://scholar.google.com/scholar?q=Bob+Coecke%2C+Mehrnoosh+Sadrzadeh%2C+and+Stephen+Clark%2E+2010%2E+Mathematical+foundations+for+a+compositional+distributional+model+of+meaning%2E)
8. Edward Grefenstette and Mehrnoosh Sadrzadeh. 2015. Concrete models and empirical evaluations for the categorical compositional distributional model of meaning. Computational Linguistics, 41(1):71–118. [find](https://scholar.google.com/scholar?q=Edward+Grefenstette+and+Mehrnoosh+Sadrzadeh%2E+2015%2E+Concrete+models+and+empirical+evaluations+for+the+categorical+compositional+distributional+model+of+meaning%2E+Computational+Linguistics%2C+41%281%29%3A71%E2%80%93118%2E)
9. Marwan Ait Haddou and Mohamed Bennai. 2025. Sculpting quantum landscapes: Fubini-study metric conditioning for geometry aware learning in parameterized quantum circuits. [find](https://scholar.google.com/scholar?q=Marwan+Ait+Haddou+and+Mohamed+Bennai%2E+2025%2E+Sculpting+quantum+landscapes%3A+Fubini%2Dstudy+metric+conditioning+for+geometry+aware+learning+in+parameterized+quantum+circuits%2E)
10. Vu Tuan Hai and Le Bin Ho. 2023. Universal compilation for quantum state tomography. Scientific Reports, 13(1):3750. [find](https://scholar.google.com/scholar?q=Vu+Tuan+Hai+and+Le+Bin+Ho%2E+2023%2E+Universal+compilation+for+quantum+state+tomography%2E+Scientific+Reports%2C+13%281%29%3A3750%2E)
11. Lisa Anne Hendricks and Aida Nematzadeh. 2021. Probing image–language transformers for verb understanding. In Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, pages 3635–3644, Online. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Lisa+Anne+Hendricks+and+Aida+Nematzadeh%2E+2021%2E+Probing+image%E2%80%93language+transformers+for+verb+understanding%2E+In+Findings+of+the+Association+for+Computational+Linguistics%3A+ACL%2DIJCNLP+2021%2C+pages+3635%E2%80%93364)
12. T. Hubert, R. Mehta, L. Sartran, et al. 2025. Olympiad-level formal mathematical reasoning with reinforcement learning. Nature, 632:290– 297. [find](https://scholar.google.com/scholar?q=T%2E+Hubert%2C+R%2E+Mehta%2C+L%2E+Sartran%2C+et+al%2E+2025%2E+Olympiad%2Dlevel+formal+mathematical+reasoning+with+reinforcement+learning%2E+Nature%2C+632%3A290%E2%80%93+297%2E)
13. Gabriel Ilharco, Mitchell Wortsman, Ross Wightman, Cade Gordon, Nicholas Carlini, Rohan Taori, Achal Dave, Vaishaal Shankar, Hongseok Namkoong, John Miller, Hannaneh Hajishirzi, Ali Farhadi, and Ludwig Schmidt. 2021. Openclip. If you use this software, please cite it as below. [find](https://scholar.google.com/scholar?q=Gabriel+Ilharco%2C+Mitchell+Wortsman%2C+Ross+Wightman%2C+Cade+Gordon%2C+Nicholas+Carlini%2C+Rohan+Taori%2C+Achal+Dave%2C+Vaishaal+Shankar%2C+Hongseok+Namkoong%2C+John+Miller%2C+Hannaneh+Hajishirzi%2C+Ali+Farhadi%2C+and+Ludwi)
14. Raphi Kang, Yue Song, Georgia Gkioxari, and Pietro Perona. 2025. Is clip ideal? no. can we fix it? yes! In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pages 22436–22446. [find](https://scholar.google.com/scholar?q=Raphi+Kang%2C+Yue+Song%2C+Georgia+Gkioxari%2C+and+Pietro+Perona%2E+2025%2E+Is+clip+ideal%3F+no%2E+can+we+fix+it%3F+yes%21+In+Proceedings+of+the+IEEE%2FCVF+International+Conference+on+Computer+Vision+%28ICCV%29%2C+pages+22436%E2%80%932)
15. Dimitri Kartsaklis, Ian Fan, Richie Yeung, Anna Pearson, Robin Lorenz, Alexis Toumi, Giovanni de Felice, Konstantinos Meichanetzidis, Stephen Clark, and Bob Coecke. 2021. lambeq: An Efficient High-Level Python Library for Quantum NLP. arXiv preprint arXiv:2110.04236. — [arXiv:2110.04236](https://arxiv.org/abs/2110.04236) [find](https://scholar.google.com/scholar?q=Dimitri+Kartsaklis%2C+Ian+Fan%2C+Richie+Yeung%2C+Anna+Pearson%2C+Robin+Lorenz%2C+Alexis+Toumi%2C+Giovanni+de+Felice%2C+Konstantinos+Meichanetzidis%2C+Stephen+Clark%2C+and+Bob+Coecke%2E+2021%2E+lambeq%3A+An+Efficient+High%2DLev)
16. Diederik P. Kingma and Jimmy Ba. 2015. Adam: A method for stochastic optimization. In International Conference on Learning Representations (ICLR). [find](https://scholar.google.com/scholar?q=Diederik+P%2E+Kingma+and+Jimmy+Ba%2E+2015%2E+Adam%3A+A+method+for+stochastic+optimization%2E+In+International+Conference+on+Learning+Representations+%28ICLR%29%2E)
17. Darina Koishigarina, Arnas Uselis, and Seong Joon Oh. 2025. Clip behaves like a bag-of-words model cross-modally but not uni-modally. [find](https://scholar.google.com/scholar?q=Darina+Koishigarina%2C+Arnas+Uselis%2C+and+Seong+Joon+Oh%2E+2025%2E+Clip+behaves+like+a+bag%2Dof%2Dwords+model+cross%2Dmodally+but+not+uni%2Dmodally%2E)
18. Brenden M. Lake and Marco Baroni. 2018. Generalization without systematicity: On the compositional skills of sequence-to-sequence recurrent networks. [find](https://scholar.google.com/scholar?q=Brenden+M%2E+Lake+and+Marco+Baroni%2E+2018%2E+Generalization+without+systematicity%3A+On+the+compositional+skills+of+sequence%2Dto%2Dsequence+recurrent+networks%2E)
19. J. Lambek. 1999. Type grammar revisited. In Logical Aspects of Computational Linguistics, pages 1–27, Berlin, Heidelberg. Springer Berlin Heidelberg. [find](https://scholar.google.com/scholar?q=J%2E+Lambek%2E+1999%2E+Type+grammar+revisited%2E+In+Logical+Aspects+of+Computational+Linguistics%2C+pages+1%E2%80%9327%2C+Berlin%2C+Heidelberg%2E+Springer+Berlin+Heidelberg%2E)
20. Martha Lewis, Nihal Nayak, Peilin Yu, Jack Merullo, Qinan Yu, Stephen Bach, and Ellie Pavlick. 2024. Does CLIP bind concepts? probing compositionality in large image models. In Findings of the Association for Computational Linguistics: EACL 2024, pages 1487–1500. [find](https://scholar.google.com/scholar?q=Martha+Lewis%2C+Nihal+Nayak%2C+Peilin+Yu%2C+Jack+Merullo%2C+Qinan+Yu%2C+Stephen+Bach%2C+and+Ellie+Pavlick%2E+2024%2E+Does+CLIP+bind+concepts%3F+probing+compositionality+in+large+image+models%2E+In+Findings+of+the+Associa)
21. Haobo Li, Zhaowei Wang, Jiachen Wang, Yueya Wang, Alexis Kai Hon Lau, and Huamin Qu. 2025a. Cllmate: A multimodal benchmark for weather and climate events forecasting. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing. [find](https://scholar.google.com/scholar?q=Haobo+Li%2C+Zhaowei+Wang%2C+Jiachen+Wang%2C+Yueya+Wang%2C+Alexis+Kai+Hon+Lau%2C+and+Huamin+Qu%2E+2025a%2E+Cllmate%3A+A+multimodal+benchmark+for+weather+and+climate+events+forecasting%2E+In+Proceedings+of+the+2025+Confe)
22. Tianle Li, Jihai Zhang, Yongming Rao, and Yu Cheng. 2025b. Unveiling the compositional ability gap in vision-language reasoning model. [find](https://scholar.google.com/scholar?q=Tianle+Li%2C+Jihai+Zhang%2C+Yongming+Rao%2C+and+Yu+Cheng%2E+2025b%2E+Unveiling+the+compositional+ability+gap+in+vision%2Dlanguage+reasoning+model%2E)
23. Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C. Lawrence Zitnick. 2014. Microsoft COCO: Common objects in context. In European Conference on Computer Vision (ECCV), pages 740–755. Springer. [find](https://scholar.google.com/scholar?q=Tsung%2DYi+Lin%2C+Michael+Maire%2C+Serge+Belongie%2C+James+Hays%2C+Pietro+Perona%2C+Deva+Ramanan%2C+Piotr+Doll%C3%A1r%2C+and+C%2E+Lawrence+Zitnick%2E+2014%2E+Microsoft+COCO%3A+Common+objects+in+context%2E+In+European+Conference+on+) [our card](https://md.research.elamur.ai/free/en/papers/lin-2014-microsoft-coco-common-objects-context/)
24. Kin Ian Lo, Hala Hawashin, Mina Abbaszadeh, Tilen Gaetano Limbäck-Stokin, Hadi Wazni, and Mehrnoosh Sadrzadeh. 2025. DisCoCLIP: A distributional compositional tensor network encoder for vision-language understanding. In Proceedings of the 14th Joint Conference on Lexical and Computational Semantics… [find](https://scholar.google.com/scholar?q=Kin+Ian+Lo%2C+Hala+Hawashin%2C+Mina+Abbaszadeh%2C+Tilen+Gaetano+Limb%C3%A4ck%2DStokin%2C+Hadi+Wazni%2C+and+Mehrnoosh+Sadrzadeh%2E+2025%2E+DisCoCLIP%3A+A+distributional+compositional+tensor+network+encoder+for+vision%2Dlanguag)
25. Robin Lorenz, Anna Pearson, Konstantinos Meichanetzidis, Dimitri Kartsaklis, and Bob Coecke. 2021. Qnlp in practice: Running compositional models of meaning on a quantum computer. [find](https://scholar.google.com/scholar?q=Robin+Lorenz%2C+Anna+Pearson%2C+Konstantinos+Meichanetzidis%2C+Dimitri+Kartsaklis%2C+and+Bob+Coecke%2E+2021%2E+Qnlp+in+practice%3A+Running+compositional+models+of+meaning+on+a+quantum+computer%2E)
26. Jean Maillard, Stephen Clark, and Edward Grefenstette. 2014. A type-driven tensor-based semantics for CCG. In Proceedings of the EACL 2014 Workshop on Type Theory and Natural Language Semantics (TTNLS), pages 46–54. [find](https://scholar.google.com/scholar?q=Jean+Maillard%2C+Stephen+Clark%2C+and+Edward+Grefenstette%2E+2014%2E+A+type%2Ddriven+tensor%2Dbased+semantics+for+CCG%2E+In+Proceedings+of+the+EACL+2014+Workshop+on+Type+Theory+and+Natural+Language+Semantics+%28TTNLS)
27. Glyn Morrill. 2010. Categorial Grammar: Logical Syntax, Semantics, and Processing. Oxford University Press. [find](https://scholar.google.com/scholar?q=Glyn+Morrill%2E+2010%2E+Categorial+Grammar%3A+Logical+Syntax%2C+Semantics%2C+and+Processing%2E+Oxford+University+Press%2E)
28. Ruikang Ni, Da Xiao, Qingye Meng, Xiangyu Li, Shihui Zheng, and Hongliang Liang. 2024. Benchmarking and understanding compositional relational reasoning of llms. [find](https://scholar.google.com/scholar?q=Ruikang+Ni%2C+Da+Xiao%2C+Qingye+Meng%2C+Xiangyu+Li%2C+Shihui+Zheng%2C+and+Hongliang+Liang%2E+2024%2E+Benchmarking+and+understanding+compositional+relational+reasoning+of+llms%2E)
29. Michael A. Nielsen and Isaac L. Chuang. 2000. Quantum Computation and Quantum Information. Cambridge University Press. [find](https://scholar.google.com/scholar?q=Michael+A%2E+Nielsen+and+Isaac+L%2E+Chuang%2E+2000%2E+Quantum+Computation+and+Quantum+Information%2E+Cambridge+University+Press%2E)
30. H. Pan, N. Mudur, W. Taranto, et al. 2025. Quantum many-body physics calculations with large language models. Communications Physics, 8:123– 130. [find](https://scholar.google.com/scholar?q=H%2E+Pan%2C+N%2E+Mudur%2C+W%2E+Taranto%2C+et+al%2E+2025%2E+Quantum+many%2Dbody+physics+calculations+with+large+language+models%2E+Communications+Physics%2C+8%3A123%E2%80%93+130%2E)
31. Ofir Press, Muru Zhang, Sewon Min, Ludwig Schmidt, Noah A. Smith, and Mike Lewis. 2023. Measuring and narrowing the compositionality gap in language models. [find](https://scholar.google.com/scholar?q=Ofir+Press%2C+Muru+Zhang%2C+Sewon+Min%2C+Ludwig+Schmidt%2C+Noah+A%2E+Smith%2C+and+Mike+Lewis%2E+2023%2E+Measuring+and+narrowing+the+compositionality+gap+in+language+models%2E)
32. Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, and Ilya Sutskever. 2021. Learning transferable visual models from natural language supervision. [find](https://scholar.google.com/scholar?q=Alec+Radford%2C+Jong+Wook+Kim%2C+Chris+Hallacy%2C+Aditya+Ramesh%2C+Gabriel+Goh%2C+Sandhini+Agarwal%2C+Girish+Sastry%2C+Amanda+Askell%2C+Pamela+Mishkin%2C+Jack+Clark%2C+Gretchen+Krueger%2C+and+Ilya+Sutskever%2E+2021%2E+Learning) [our card](https://md.research.elamur.ai/free/en/papers/radford-2021-learning-transferable-visual-models-natural/)
33. Piyush Sharma, Nan Ding, Sebastian Goodman, and Radu Soricut. 2018. Conceptual captions: A cleaned, hypernymed, image alt-text dataset for automatic image captioning. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (ACL), pages 2556– 2565. [find](https://scholar.google.com/scholar?q=Piyush+Sharma%2C+Nan+Ding%2C+Sebastian+Goodman%2C+and+Radu+Soricut%2E+2018%2E+Conceptual+captions%3A+A+cleaned%2C+hypernymed%2C+image+alt%2Dtext+dataset+for+automatic+image+captioning%2E+In+Proceedings+of+the+56th+Annual) [our card](https://md.research.elamur.ai/free/en/papers/sharma-2018-conceptual-captions-cleaned-hypernymed-image/)
34. Sukin Sim, Peter D. Johnson, and Alán Aspuru-Guzik. 2019. Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies, 2(12). [find](https://scholar.google.com/scholar?q=Sukin+Sim%2C+Peter+D%2E+Johnson%2C+and+Al%C3%A1n+Aspuru%2DGuzik%2E+2019%2E+Expressibility+and+entangling+capability+of+parameterized+quantum+circuits+for+hybrid+quantum%2Dclassical+algorithms%2E+Advanced+Quantum+Technolog)
35. K. Singhal, T. Tu, J. Gottweis, R. Sayres, E. Wulczyn, et al. 2025. Toward expert-level medical question answering with large language models. Nature Medicine, 31:943–950. [find](https://scholar.google.com/scholar?q=K%2E+Singhal%2C+T%2E+Tu%2C+J%2E+Gottweis%2C+R%2E+Sayres%2C+E%2E+Wulczyn%2C+et+al%2E+2025%2E+Toward+expert%2Dlevel+medical+question+answering+with+large+language+models%2E+Nature+Medicine%2C+31%3A943%E2%80%93950%2E)
36. Mark Steedman. 2000. The Syntactic Process. MIT Press. [find](https://scholar.google.com/scholar?q=Mark+Steedman%2E+2000%2E+The+Syntactic+Process%2E+MIT+Press%2E)
37. Mark Steedman and Jason Baldridge. 2011. Combinatory Categorial Grammar , chapter 5. John Wiley & Sons, Ltd. [find](https://scholar.google.com/scholar?q=Mark+Steedman+and+Jason+Baldridge%2E+2011%2E+Combinatory+Categorial+Grammar+%2C+chapter+5%2E+John+Wiley+%26+Sons%2C+Ltd%2E)
38. James Stokes, Josh Izaac, Nathan Killoran, and Giuseppe Carleo. 2020. Quantum natural gradient. Quantum, 4:269. [find](https://scholar.google.com/scholar?q=James+Stokes%2C+Josh+Izaac%2C+Nathan+Killoran%2C+and+Giuseppe+Carleo%2E+2020%2E+Quantum+natural+gradient%2E+Quantum%2C+4%3A269%2E)
39. Aaron van den Oord, Yazhe Li, and Oriol Vinyals. 2019. Representation learning with contrastive predictive coding. [find](https://scholar.google.com/scholar?q=Aaron+van+den+Oord%2C+Yazhe+Li%2C+and+Oriol+Vinyals%2E+2019%2E+Representation+learning+with+contrastive+predictive+coding%2E) [our card](https://md.research.elamur.ai/free/en/papers/oord-2018-representation-learning-contrastive-predictive-coding/)
40. Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. 2023. Attention is all you need. [find](https://scholar.google.com/scholar?q=Ashish+Vaswani%2C+Noam+Shazeer%2C+Niki+Parmar%2C+Jakob+Uszkoreit%2C+Llion+Jones%2C+Aidan+N%2E+Gomez%2C+Lukasz+Kaiser%2C+and+Illia+Polosukhin%2E+2023%2E+Attention+is+all+you+need%2E)
41. Hadi Wazni, Kin Ian Lo, Lachlan McPheat, and Mehrnoosh Sadrzadeh. 2024. Large scale structure-aware pronoun resolution using quantum natural language processing. Quantum Machine Intelligence, 6(2):60. [find](https://scholar.google.com/scholar?q=Hadi+Wazni%2C+Kin+Ian+Lo%2C+Lachlan+McPheat%2C+and+Mehrnoosh+Sadrzadeh%2E+2024%2E+Large+scale+structure%2Daware+pronoun+resolution+using+quantum+natural+language+processing%2E+Quantum+Machine+Intelligence%2C+6%282%29%3A60%2E)
42. Hadi Wazni and Mehrnoosh Sadrzadeh. 2023. Towards transparency in coreference resolution: A quantum-inspired approach. In Proceedings of The Sixth Workshop on Computational Models of Reference, Anaphora and Coreference (CRAC 2023), pages 15–27, Singapore. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Hadi+Wazni+and+Mehrnoosh+Sadrzadeh%2E+2023%2E+Towards+transparency+in+coreference+resolution%3A+A+quantum%2Dinspired+approach%2E+In+Proceedings+of+The+Sixth+Workshop+on+Computational+Models+of+Reference%2C+Anapho)
43. Gijs Wijnholds, Mehrnoosh Sadrzadeh, and Stephen Clark. 2020. Representation learning for type-driven composition. In Proceedings of the 24th Conference on Computational Natural Language Learning, pages 313–324. [find](https://scholar.google.com/scholar?q=Gijs+Wijnholds%2C+Mehrnoosh+Sadrzadeh%2C+and+Stephen+Clark%2E+2020%2E+Representation+learning+for+type%2Ddriven+composition%2E+In+Proceedings+of+the+24th+Conference+on+Computational+Natural+Language+Learning%2C+pag)
44. Richie Yeung and Dimitri Kartsaklis. 2021. A CCG-based version of the DisCoCat framework. In Proceedings of the 2021 Workshop on Semantic Spaces at the Intersection of NLP, Physics, and Cognitive Science (SemSpace), pages 20– 31, Groningen, The Netherlands. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Richie+Yeung+and+Dimitri+Kartsaklis%2E+2021%2E+A+CCG%2Dbased+version+of+the+DisCoCat+framework%2E+In+Proceedings+of+the+2021+Workshop+on+Semantic+Spaces+at+the+Intersection+of+NLP%2C+Physics%2C+and+Cognitive+Scie)
45. Mert Yuksekgonul, Federico Bianchi, Pratyusha Kalluri, Dan Jurafsky, and James Zou. 2023. When and why Vision-Language Models behave like Bags-of-Words, and what to do about it? In International Conference on Learning Representations (ICLR). [find](https://scholar.google.com/scholar?q=Mert+Yuksekgonul%2C+Federico+Bianchi%2C+Pratyusha+Kalluri%2C+Dan+Jurafsky%2C+and+James+Zou%2E+2023%2E+When+and+why+Vision%2DLanguage+Models+behave+like+Bags%2Dof%2DWords%2C+and+what+to+do+about+it%3F+In+International+Confe)