References

Abbaszade, M., Zomorodi, M., Salari, V., & Kurian, P. (2023). Toward Quantum Machine Translation of Syntactically Distinct Languages. https://arxiv.org/abs/2307.16576

Agrawal, S., & Carpuat, M. (2024). Do Text Simplification Systems Preserve Meaning? A Human Evaluation via Reading Comprehension. Transactions of the Association for Computational Linguistics, 12, 432-448.

Alchieri, L., Badalotti, D., Bonardi, P., & Bianco, S. (2021). An introduction to quantum machine learning: from quantum logic to quantum deep learning. Quantum Machine Intelligence, 3(2), 28.

Arya, A., & Ranjan, A. (2024). Beyond Syntax and Semantics: The Quantum Leap in Natural Language Processing. In Federated learning for Internet of Vehicles: IoV Image Processing, Vision and Intelligent Systems (pp. 255-284). Bentham Science Publishers.

Biamonte, J., Wittek, P., Pancotti, N., Rebentrost, P., Wiebe, N., & Lloyd, S. (2017). Quantum machine learning. Nature, 549(7671), 195-202.

Blekos, K., Brand, D., Ceschini, A., Chou, C. H., Li, R. H., Pandya, K., & Summer, A. (2024). A review on quantum approximate optimization algorithm and its variants. Physics Reports, 1068, 1-66.

Coecke, B. (2021). The mathematics of text structure. Joachim Lambek: The Interplay of Mathematics, Logic, and Linguistics, 181-217.

Coecke, B., & Kissinger, A. (2018). Picturing quantum processes: A first course on quantum theory and diagrammatic reasoning. In Diagrammatic Representation and Inference: 10th International Conference, Diagrams 2018, Edinburgh, UK, June 18-22, 2018, Proceedings 10 (pp. 28-31). Springer International Publishing.

Consuegra-Ayala, J. P., Martínez-Murillo, I., Lloret, E., Moreda, P., & Palomar, M. (2024). A multifaceted approach to detect gender biases in Natural Language Generation. Knowledge-Based Systems, 303, 112367.

Fan, Z., Zhang, J., Zhang, P., Lin, Q., Li, Y., & Qian, Y. (2024). Quantum-inspired language models based on unitary transformation. Information Processing & Management, 61(4), 103741.

Galofaro, F., Toffano, Z. and Doan, B.-L. (2018), «A quantum-based semiotic model for textual semantics», Kybernetes, Vol. 47 No. 2, pp. 307-320. https://doi.org/10.1108/K-05-2017-0187

Gan, Y., Poesio, M., & Yu, J. (2024, May). Assessing the Capabilities of Large Language Models in Coreference: An Evaluation. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024) (pp. 1645-1665).

Gauch, H. G. (2003). Scientific method in practice. Cambridge University Press.

Gonzalez-Delgado, Gabriel and Navarro-Colorado, Borja (2024) “The Simplification of the Language of Public Administration: The Case of Ombudsman Institutions” In Proceedings of the Workshop on DeTermIt! Evaluating Text Difficulty in a Multilingual Context  LREC-COLING 2024 pages 125–133, Torino, Italy.

Hegde, P. R., Passarelli, G., Scocco, A., & Lucignano, P. (2022). Genetic optimization of quantum annealing. Physical Review A, 105(1), 012612.

Hur, T., Kim, L., & Park, D. K. (2022). Quantum convolutional neural network for classical data classification. Quantum Machine Intelligence, 4(1), 3.

Huynh, L., Hong, J., Mian, A., Suzuki, H., Wu, Y., & Camtepe, S. (2023). Quantum-inspired machine learning: a survey. arXiv preprint arXiv:2308.11269.

de Aldecoa Quintana, J. M. I. (2014). Niveles de madurez tecnológica: Technology readiness levels: TRLS: Una introducción. Economía industrial, (393), 165-171.

Karamlou, A., Pfaffhauser, M., & Wootton, J. (2022). Quantum natural language generation on near-term devices. Proceedings of the 15th International Conference on Natural Language Generation, pages 267 – 277. July 18-22, 2022. Association for Computational Linguistics.

Laakkonen, T., Meichanetzidis, K., & Coecke, B. (2024). Quantum algorithms for compositional text processing. arXiv preprint arXiv:2408.06061.

Lai, W., Shi, J., & Chang, Y. (2023). Quantum-inspired fully complex-valued neutral network for sentiment analysis. Axioms, 12(3), 308.

Lorenz, R., Pearson, A., Meichanetzidis, K., Kartsaklis, D., Coecke, B.: QNLP in practice: Running compositional models of meaning on a quantum computer (2021). arXiv:2102.12846

Mandal, A. K., & Chakraborty, B. (2024). Quantum computing and quantum-inspired techniques for feature subset selection: a review. Knowledge and Information Systems, 1-43.

Martin, T. J., Abreu Salas, J. I., & Moreda Pozo, P. (2023, June). A review of parallel corpora for automatic text simplification. key challenges moving forward. In International Conference on Applications of Natural Language to Information Systems (pp. 62-78). Cham: Springer Nature &Switzerland.

Martínez P, Ramos A and Moreno L (2024) Exploring Large Language Models to generate Easy to Read content. Front. Comput. Sci. 6:1394705. doi: 10.3389/fcomp.2024.1394705

Minaee, S., Mikolov, T., Nikzad, N., Chenaghlu, M., Socher, R., Amatriain, X., & Gao, J. (2024). Large language models: A survey. arXiv preprint arXiv:2402.06196. https://arxiv.org/abs/2402.06196

Miró Maestre, M., Martínez-Murillo, I., Martin, T.J., Navarro-Colorado, B., Ferrández, A., Suárez Cueto, A., Lloret, E.: Beyond Generative Artificial Intelligence: Roadmap for Natural Language Generation. https://arxiv.org/abs/2407.10554 (2024)

Mojrian, M., & Mirroshandel, S. A. (2021). A novel extractive multi-document text summarization system using quantum-inspired genetic algorithm: MTSQIGA. Expert systems with applications, 171, 114555.

Montanaro, A. (2016). Quantum algorithms: an overview. npj Quantum Information, 2(1), 1-8.

Moreno, L., Palomar, M., Molina, A., & Ferrández, A. (1999). Introducción al procesamiento del lenguaje natural. Servicio de Publicaciones Universidad de Alicante). Universidad de Alicante.

Niroula, P., Shaydulin, R., Yalovetzky, R., Minssen, P., Herman, D., Hu, S., & Pistoia, M. (2022). Constrained quantum optimization for extractive summarization on a trapped-ion quantum computer. Scientific Reports, 12(1), 17171.

Ota, M. (2010). Scrum in research. In Cooperative Design, Visualization, and Engineering: 7th International Conference, CDVE 2010, Calvia, Mallorca, Spain, September 19-22, 2010. Proceedings (pp. 109-116). Springer Berlin Heidelberg.

Panahi, A., Saeedi, S., & Arodz, T. (2019). word2ket: Space-efficient word embeddings inspired by quantum entanglement. arXiv preprint arXiv:1911.04975.

Pasin, A., Ferrari Dacrema, M., Cremonesi, P., & Ferro, N. (2024, September). Overview of QuantumCLEF 2024: The Quantum Computing Challenge for Information Retrieval and Recommender Systems at CLEF. In International Conference of the Cross-Language Evaluation Forum for European Languages (pp. 260-282). Cham: Springer Nature Switzerland.

Paykin, J., Rand, R., & Zdancewic, S. (2017). QWIRE: a core language for quantum circuits. ACM SIGPLAN Notices, 52(1), 846-858.

Peral-García, D., Cruz-Benito, J., & García-Peñalvo, F. J. (2024). Comparing Natural Language Processing and Quantum Natural Processing approaches in text classification tasks. Expert Systems with Applications, 124427.

Peral-García, D., Cruz-Benito, J., & García-Peñalvo, F. J. (2024b). Systematic literature review: Quantum machine learning and its applications. Computer Science Review, 51, 100619.

Piwowarski, B., Amini, M. R., & Lalmas, M. (2012). On using a quantum physics formalism for multidocument summarization. Journal of the American Society for Information Science and Technology, 63(5), 865-888.

Quantum Technologies. 2024. Quantum Technologies Report. Yole Intelligence (Yole Group), May 2024. https://goo.su/M1ukY2

Ruan, Y., Yuan, Z., Xue, X., & Liu, Z. (2023). Quantum approximate optimization for combinatorial problems with constraints. Information Sciences, 619, 98-125.

Saggion, H. (2024). Artificial intelligence and natural language processing for easy-to-read texts. In Revista de Llengua i Dret. DOI: 10.58992/rld.i82.2024.4362

Pandey S., Basisth, N.J., Sachan, T., Kumari, N., Pakray, P. (2023). Quantum machine learning for natural language processing application. Physica A: Statistical Mechanics and its Applications, Volume 627,129123. https://doi.org/10.1016/j.physa.2023.129123.

Simões, R. D. M., Huber, P., Meier, N., Smailov, N., Füchslin, R. M., & Stockinger, K. (2023). Experimental evaluation of quantum machine learning algorithms. IEEE Access, 11, 6197-6208.

Sodorni, Alessandro; Nie, Jian-Yun; Bengio, Yoshua. Modeling term dependencies with quantum language models for IR. En Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval. 2013. p. 653-662.

Srivastava, A., Bhardwaj, S., & Saraswat, S. (2017, May). SCRUM model for agile methodology. In 2017 International Conference on Computing, Communication and Automation (ICCCA) (pp. 864-869). IEEE.

Tian, J., Sun, X., Du, Y., Zhao, S., Liu, Q., Zhang, K., … & Tao, D. (2023). Recent advances for quantum neural networks in generative learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(10), 12321-12340.

Ulker, M., & Ozer, A. B. (2024). Abstractive Summarization Model for Summarizing Scientific Article. IEEE Access.

Varmantchaonala, C. M., Fendji, J. L. E., Schöning, J., & Atemkeng, M. (2024). Quantum Natural Language Processing: A Comprehensive Survey. IEEE Access.

Wichert, A. (2020). Principles of quantum artificial intelligence: quantum problem solving and machine learning.

Widdows, D., Aboumrad, W., Kim, D., Ray, S., & Mei, J. (2024). Quantum natural language processing. KI-Künstliche Intelligenz, 1-18.

Widdows, D., Alexander, A., Zhu, D., Zimmerman, C., & Majumder, A. (2024). Near-term advances in quantum natural language processing. Annals of Mathematics and Artificial Intelligence, 1-24.

Willis, J. M. (2024). QIXAI: A Quantum-Inspired Framework for Enhancing Classical and Quantum Model Transparency and Understanding. arXiv preprint arXiv:2410.16537.

Wu, S., Li, J., Zhang, P., & Zhang, Y. (2021). Natural language processing meets quantum physics: A survey and categorization. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (pp. 3172-3182).

Yu, W., Yin, L., Zhang, C., Chen, Y., & Liu, A. X. (2024). Application of Quantum Recurrent Neural Network in Low Resource Language Text Classification. IEEE Transactions on Quantum Engineering.

Zaech, J. N., Liniger, A., Danelljan, M., Dai, D., & Van Gool, L. (2022). Adiabatic quantum computing for multi object tracking. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 8811-8822).

Zamuda, A., & Lloret, E. (2020). Optimizing data-driven models for summarization as parallel tasks. Journal of Computational Science, 42, 101101.

Zamuda, A., Dugonitk, J. and Lloret, E. (2024). Comparing Evolved Extractive Text Summary Scores of Bidirectional Encoder Representations from Transformers. 11th International Conference on Electrical, Electronics and Computer Engineering (IcETRAN), Nis, 3-6 June 2024

Zhang, T., Ladhak, F., Durmus, E., Liang, P., McKeown, K., & Hashimoto, T. B. (2024). Benchmarking large language models for news summarization. Transactions of the Association for Computational Linguistics, 12, 39-57.

Zeng, W., & Coecke, B. (2016). Quantum algorithms for compositional natural language processing. arXiv preprint arXiv:1608.01406.