Abstract
To achieve faster computational speeds than classical computing, quantum computing is rapidly evolving to become one of the most popular areas of computer engineering. The advent of Noisy Intermediate Scale Quantum (NISQ) devices has made it possible to perform this work on quantum computers in areas like chemistry or machine learning. In the field of machine learning, one of the sub-areas of interest is quantum natural language processing. One of the lines of research already allows not only the encoding of words into qubits, also the association of these words -segmented according to their syntactic categorization-, to quantum combinational circuits or ansatz to allow, for example, the association of this circuit to a neural network.
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Peral-García, D., Cruz-Benito, J., García-Peñalvo, F.J. (2023). Development of Algorithms and Methods for the Simulation and Improvement in the Quantum Natural Language Processing Area. In: García-Peñalvo, F.J., García-Holgado, A. (eds) Proceedings TEEM 2022: Tenth International Conference on Technological Ecosystems for Enhancing Multiculturality. TEEM 2022. Lecture Notes in Educational Technology. Springer, Singapore. https://doi.org/10.1007/978-981-99-0942-1_130
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