Quantum annealing belongs to a family of quantum optimization algorithms designed to solve combinatorial optimization problems using programmable quantum hardware. In this thesis, various methods... Show moreQuantum annealing belongs to a family of quantum optimization algorithms designed to solve combinatorial optimization problems using programmable quantum hardware. In this thesis, various methods are developed and tested to understand how to formulate combinatorial optimization problems for quantum annealing. Use of both purely quantum approaches as well as hybrid quantum-classical algorithms are investigated using state-of-the-art quantum hardware. How the developed methods affect the performance of quantum hardware in practice is tested using both real-world and canonical academic problems. Show less
Yarkoni, S.; Raponi, E.; Bäck, T.H.W.; Schmitt, S. 2022
Genetic algorithms have unique properties which are useful when applied to black-box optimization. Using selection, crossover, and mutation operators, candidate solutions may be obtained without... Show moreGenetic algorithms have unique properties which are useful when applied to black-box optimization. Using selection, crossover, and mutation operators, candidate solutions may be obtained without the need to calculate a gradient. In this work, we study results obtained from using quantum-enhanced operators within the selection mechanism of a genetic algorithm. Our approach frames the selection process as a minimization of a binary quadratic model with which we encode fitness and distance between members of a population, and we leverage a quantum annealing system to sample low-energy solutions for the selection mechanism. We benchmark these quantum-enhanced algorithms against classical algorithms over various black-box objective functions, including the OneMax function, and functions from the IOHProfiler library for black-box optimization. We observe a performance gain in the average number of generations to convergence for the quantum-enhanced elitist selection operator in comparison to classical on the OneMax function. We also find that the quantum-enhanced selection operator with ∗Corresponding author email: David.VonDollen@vw.com non-elitist selection outperforms benchmarks on functions with fitness perturbation from the IOHProfiler library. Additionally, we find that in the case of elitist selection, the quantum-enhanced operators outperform classical benchmarks on functions with varying degrees of dummy variables and neutrality Show less
Yarkoni, S.; Huck, A.; Schülldorf, H.; Speitkamp, B.; Tabrizi, M.S.; Leib, M.; Bäck, T.H.W., Neukart, F. 2021
