Implementation of Quantum Neural Network in an Integrated Circuit

Abstract

The implementation of artificial intelligence based on Deep learning is currently done with digital neuronetworks. These present a series of drawbacks for the implementation of certain functions such as network training within an integrated circuit. In addition, new applications of A.I. they require an increasing number of layers and neurons, producing a bottleneck given the high consumption of this technology and the limits of component integration. This work is part of the research project "Optimization of Artificial Intelligence Systems based on Deep Learning with Hybrid, Analog and Digital Nanoelectronic Devices.", which analyzes the bases of the design of a quantum neural network in an integrated circuit as one of the possible ways of solving the aforementioned limitations.