Design and implementation of a quantum multilayer neural network framewori

Abstract

Artificial Neurons are biologically inspired algorithms that form the building blocks for Artificial Neural Networks (ANNs) and Multilayer Neural Networks (MNN), which have been recently studied and implemented to solve important ptoblems. Advances in Learning theory and the availability of powerful computational systems has resulted in the creation of many real-world applications. Practically every industry has already adopted Multilayer Learning powered technologies in some part of their processes, as state of the art MNNpowered algorithms can outperform other algorithms and even human accuracy for a wide number of tasks. However, their performance relies heavily on the budget of data available as well as its format, as the most popular applications require a copious amount of training examples. Another limitation to build large scale MNN applications is the vast computational resources needed to build these systems. MNN based algorithms usage is widespread and also getting more complex, this phenomenon creates an ever-growing demand for computational power, which may no longer be satisfied at some point in the new future, thanks to the deceleration in state of the art monolithic processors’ performance. Quantum information theory, is a field that has had success in the last couple of decades, thanks to the creation of algorithms that are in theory able to outperform classical computers. The ability of quantum computers of working with inherently different physical systems than the ones used by classical computers, opens an exciting opportunity for scientists and companies to explore the performance of quantum computers for machine learning tasks, being multilayer learning a focus point, thanks to its importance in classical computing. Although a considerable amount of resources have been allocated to the development of MNN powered algorithms in quantum computers, there are still challenges left to overcome before Quantum Multilayer Neural Networks come to be a technology that can compete with state of the art MNN powered algorithms. This research explores the properties of multilayer neural network algorithms running on quantum computers. The first contribution of the research work reported in this document is the analysis and implementation of a perceptron algorithm running on a quantum computer. The second contribution is the proposal, implementation and analysis of two different information encoding methods for quantum computers. The final, and most important contribution of this work, is the development of a framework that allows training multilayer neural networks for Supervised Learning.