Significant progress has been made recently in Natural Language Processing (NLP) due to the development of Large Language Models (LLMs) and
their capacity to understand and produce text together with their capability to successfully address different applications. Despite the fact that the
number, size and capabilities of LLMs are continuously growing, they still present a number of limitations and challenges, not only related to their
computational cost and training data needs, but also to their performance in solving some linguistic phenomena and tasks that can highly affect the trust
and reliability of AI systems. Due to these limitations and in parallel to this great LLM development, other emerging research areas are starting to gain
traction. One of these emerging areas is related to applying quantum mechanics principles to the field of AI, and more specifically Quantum NLP (QNLP).
The motivation for merging quantum computing and NLP lies in quantum systems ability to handle complex, high-dimensional data more efficiently. On
the one hand, quantum computers could provide exponential speedups in tasks like semantic analysis, summarization, text classification and word
embeddings generation, which are computationally expensive on classical systems. On the other hand, quantum algorithms could facilitate more
advanced language models and techniques to grasp subtle nuances in textual data, as well as enable more efficient processing of long texts, improving
the ability to condense information quickly. However, this is still largely theoretical, still in its nascent stages, as it has yet to be better proven with
practical implementations.

Our project, QUantum Mechanics for LAnguage Understanding anD gEneration (QUMLAUDE), seeks to investigate how quantum information
representation and algorithms can address complex NLP tasks such as text simplification and summarization. By leveraging quantum-inspired
algorithms, we aim to model nuanced linguistic phenomena with special attention to semantic representationsuch as contextual embeddings, semantic
composition, and metaphorswhere quantum advancements promise to deliver more accurate and precise results compared to current methods.
To successfully meet the projects objective, the research work has been organized into six modules, with a duration of 3 years: Quantum representation
of the text (M1); Quantum and Quantum-inspired Algorithms for NLP (M2); Integrating quantum theory in NLP tasks and applications (M3); Use Case:
Retrieval Augmented Generation system for the innovation domain (M4); Dissemination (M5) and project management (M6).
QUMLADUE project brings together a multidisciplinary team of experts from linguistics, computational sciences, and experimental sciences, positioning
the project at the forefront of innovation in quantum computing. Our research will not only deepen our understanding of quantum-enhanced NLP but also
foster new standards in quantum computing applications.