info:eu-repo/semantics/bachelorThesis
Quantum exordium for natural language processing: A novel approach to sample on decoders
Fecha
2021Autor
Ochoa Luna, Jose Eduardo
Institución
Resumen
The sampling task of Seq2Seq models in Natural Language Processing (NLP) is
based on heuristics because of the Non-Deterministic Polynomial Time (NP)
nature of this problem. The goal of this research is to develop a quantum sampler for Seq2Seq models, and give evidence that Quantum Annealing (QA) can
guide the search space of these samplers. The contribution of this work is given
by showing an architecture to represent Recurrent Neural Networks (RNN) in
a quantum computer to finally develop a quantum sampler. The individual
architectures (i.e. summation, multiplication, argmax, and activation functions) achieve optimal accuracies in both simulated and quantum environments. While the results of the overall proposal show that it can either outperform or match greedy approaches. As the very first steps of quantum NLP,
these are tested against simple RNN with a synthetic data set of random numbers, and a real quantum computer is utilized. Since ane functions are the
basis of most Artificial Intelligence (AI) models, this method can be applied
to more complex architectures in the future.