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Title: Operating practical quantum devices in the pre-threshold regime
Author: Gentile, Antonio Andreas
ISNI:       0000 0004 9347 1388
Awarding Body: University of Bristol
Current Institution: University of Bristol
Date of Award: 2020
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The last 10 years have seen an accelerating increase in the interest towards quantum technologies. Such interest has multifaceted motivations. The miniaturisation of components and devices has achieved a level whereby quantum effects are not negligible, such that testing and measurement tasks require enhanced resolution and accuracies. In another context, we are observing a surge in the need for computational power, driven by the development of novel automated technologies. These are but two examples of areas where what can be achieved by mere incremental advances in classical protocols and toolsets might soon incur in physical as well as information theoretical limitations. Research efforts were thus driven towards the realm of quantum sensing and computation, respectively. However, such efforts are often frustrated by the important role played by noise against a successful deployment of quantum technologies, at a time when the scale of quantum devices does not satisfy the requirements for full error correction mechanisms (i.e. a pre–threshold regime). This Thesis puts forward several methods, especially based on machine learning protocols, to mitigate the effects of noise in. We begin our work by reviewing fundamental concepts in quantum information, as well as in integrated photonics and solid-state atomic defects, here adopted as experimental platforms. Using quantum photonic chips, a novel proposal for a fundamental building block of quantum computers is here demonstrated. Moreover, we propose and demonstrate a quantum protocol for the efficient characterisation of untrusted quantum devices, automated via machine learning, and a variational algorithm for solving important instances of the eigenproblem, which is of fundamental importance to quantum chemistry. These novel protocols, and their efficiency, leverage upon the known principles of quantum simulation. We also apply machine learning methods to demonstrate efficient quantum sensing with a solid-state defect. Throughout these investigations, we deal with real–world pre–threshold devices, intended to be deployed in applications of crucial importance: this makes them practical regardless of achieving fundamentally superior performances over their classical counterparts. Our approaches contribute towards a positive answer to a crucial research question: whether near-term quantum devices can deliver useful applications.
Supervisor: Laing, Anthony ; Barreto, Jorge ; Thompson, Mark Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available