Machine-learning-assisted state and gate engineering for quantum technologies

In this thesis, we discuss applications of machine learning to quantum information science. The interface between these two fields has been the subject of much research, recently, driven by the many successes of machine learning for diverse pattern recognition tasks. The work reported in this Thesis addresses precisely the potential of such an interdisciplinary line of research and illustrates how machine learning provides a valuable add-on to standard techniques used in the context of quantum technologies. <br/><br/>Specifically, we will first study the problem of devising time-independent dynamics implementing target quantum operations. This involves a challenging optimisation task, which is hard to tackle with standard numerical tools. We demonstrate how the use of supervised learning algorithms can dramatically speed-up this task. <br/><br/>We then consider the tasks of engineering and characterising quantum states in large Hilbert spaces. Motivated by strong experimental reasons, we consider explicitly the embodiment of such multi-dimensional quantum systems provided by the orbital angular momentum and polarisation degrees of freedom of light. We devise a protocol involving quantum walks to implement arbitrary states by only making use of the possibility to couple polarisation and orbital angular momentum through relatively recent technological advances in the field of linear optics. This protocol relies solely on the properties of quantum walk dynamics, and is therefore applicable to different types of architectures. <br/><br/>We then present an experimental demonstration of this engineering strategy, and consider the issue of assessing the quality of the states thus generated. We show how different machine learning algorithms, including supervised and unsupervised learning ones, are able to tackle this problem and provide useful information in realistic experimental conditions.

Name dc:type.qualificationname

Doctor of Philosophy

Level dc:type.qualificationlevel

Doctoral Thesis

Grantor dc:publisher.institution

Queen's University Belfast

Year dc:date.issued

2020

Author dc:creator

• Innocenti, Luca

Advisor dc:contributor.advisor

• Paternostro, Mauro

• Quantum
• quantum information
• machine learning
• quantum walks
• quantum optics

Language dc:language

eng

Identifier

oai:pure.qub.ac.uk/portal:studenttheses/28b47a96-9682-4a28-b61e-c19078a9c1ae

OAI identifier oai:identifier

oai:pure.qub.ac.uk/portal:studenttheses/28b47a96-9682-4a28-b61e-c19078a9c1ae