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Durham e-Theses
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New Roads to String Theory: From a New On-shell Formalism to Applications of Quantum Computing

NUTRICATI, LUCA,ARMANDO (2023) New Roads to String Theory: From a New On-shell Formalism to Applications of Quantum Computing. Doctoral thesis, Durham University.



This thesis discusses new approaches to string theory, aiming to open novel perspectives to connect string models with low energy phenomena. We approach this problem from two complementary perspectives. Firstly, we take a formal approach, seeking to uncover universal properties inherent in all closed string theories. Secondly, we head towards a more computational direction, leveraging the power of quantum computing to develop innovative techniques. These techniques serve as powerful tools for exploring and identifying viable string theory vacua, significantly enhancing our search capabilities in this complex domain.

In particular, in the first part we conduct a general, model-independent analysis of the running of gauge couplings within closed string theories. In doing so, we develop a new framework which is completely general and can be in principle used to compute one-loop corrections to all physical quantities in a given string model.

The second part of this study is dedicated to pioneering the development of novel search methodologies, marking the groundbreaking integration of quantum computing as a powerful and previously unexploited resource in our quest to explore the landscape of string vacua. We investigate its efficiency and effectiveness in the model discovery process. Through a thorough comparison with traditional methods such as simulated annealing, random scans, and genetic algorithms, we highlight the potential advantages offered by quantum annealers, which promised to be roughly fifty times faster than random scans and genetic algorithm and approximately four times faster than simulated annealing.

In this context, we also propose an enhanced version of a class of metaheuristic algorithms called Genetic Algorithms (GAs). This enhanced version integrates GAs with quantum annealing techniques, promising a significant boost in performance and problem-solving capabilities. We have employed both genetic and genetic quantum annealing algorithms as tools to search for particular string models which go under the name of heterotic line bundle models. We shall discuss in which extent this new tool promises to outperform search scans based on classical GAs.

Item Type:Thesis (Doctoral)
Award:Doctor of Philosophy
Faculty and Department:Faculty of Science > Mathematical Sciences, Department of
Thesis Date:2023
Copyright:Copyright of this thesis is held by the author
Deposited On:06 Dec 2023 12:41

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