Hybrid Quantum-Classical Metaheuristics for Automated Machine Learning Applications is a specialized academic thesis about the use of quantum, quantum-inspired and hybrid quantum-classical computation within automated machine learning. The work explores topics such as AutoML, feature selection, optimization metaheuristics, evolutionary algorithms, Bayesian optimization, Gaussian process regression and quantum machine learning under NISQ-device limitations. A highly relevant book for PhD students, researchers, AI professionals, computer science students and anyone interested in the intersection of machine learning, quantum computing and optimization. The book is used but looks neat and readable, with light signs of use.

Hybrid Quantum-Classical Metaheuristics for Automated Machine Learning Applications is een gespecialiseerd academisch proefschrift over het gebruik van quantum, quantum-inspired en hybride quantum-klassieke computation binnen automated machine learning. Het boek behandelt onder andere AutoML, feature selection, optimalisatie, evolutionaire algoritmen, Bayesian optimization, Gaussian process regression en quantum machine learning binnen de beperkingen van NISQ-apparaten. Interessant voor promovendi, onderzoekers, AI-professionals, informaticastudenten en lezers die zich richten op machine learning, quantum computing en optimalisatie. Het boek is gebruikt maar oogt netjes en goed leesbaar, met lichte gebruikssporen.