Algorithm selection and configuration for Noisy Intermediate Scale Quantum methods for industrial applications

Moussa, C. (2023)

Doctoral Thesis

Quantum hardware comes with a different computing paradigm and new ways to tackle applications. Much effort has to be put into understanding how to leverage this technology to give real-world advantages in areas of interest for industries such as combinatorial optimization or machine learning. Variational quantum algorithms (VQAs) have been introduced as a way to work within the scope of reach of the current imperfect and unstable hardware. A VQA boils down to a parameterized quantum circuit, which is a quantum circuit with adjustable real-valued parameters. These parameters are generally tweaked by a classical optimization algorithm to optimize a quantity of interest. As VQAs are most often used as heuristics, it is not clear whether they genuinely outperform current classical state-of-the-art algorithms in relevant domains of application. Plus, a VQA can come with many components (which can also be called
hyperparameters) making it a (growing) complex system to analyze performances on many considered tasks. Consequently, we will face the issues of algorithm selection and configuration, which we tackle through this thesis on many examples relevant to industrial applications. In this thesis, VQAs for combinatorial optimization, chemistry/material science, and machine learning problems were considered.

• Quantum computing applications
• Algorithm selection
• Algorithm configuration
• Combinatorial Optimization
• Machine Learning
• Quantum chemistry

• All authors: Moussa, C.
• Supervisor: Dunjko, V.; Bäck, T.W.H.
• Qualification: Doctor (dr.)
• Awarding Institution: Leiden Institute of Advanced Computer Science (LIACS), Faculty of Science, Leiden University
• Date: 2023-10-11
• Sponsorship: Plaat, A.; Bonsangue, M.M.; Trautman, H.; Wang, H.; Möller, M.