Quantum algorithm for the implementation of a perceptron

Quantum information and quantum computers have enabled us to write algorithms, - quantum algorithms - that in some cases display a speedup with respect to their classical counterpart. As machine learning is becoming of upmost importance not only for our every-day-life and for scientific research, it is natural to inquire if we could use methods from quantum information to speed up machine learning’s processes. In this Thesis, we review a model of a quantum perceptron, a key component for modern neural networks, and simulate its time evolution to characterize behavior.