Francesco Petruccione

Quantum machine learning investigates how quantum computers can be used for data-driven prediction and decision making. The books summarises and conceptualises ideas of this relatively young discipline for an audience of computer scientists and physicists from a graduate level upwards. Meer

This book treats modern aspects of open systems, measurement, and decoherence in relativistic quantum theory. It starts with a comprehensive introduction to the problems related to measuring local and nonlocal observables and the constraints imposed by the causality principle. Meer

Based on eight extensive lectures selected from those given at the renowned Chris Engelbrecht Summer School in Theoretical Physics in South Africa, this text on the theoretical foundations of quantum information processing and communication covers an array of topics, including quantum probabilities, open systems, and non-Markovian dynamics and decoherence. Meer

This book offers an introduction into quantum machine learning research, covering approaches that range from "near-term" to fault-tolerant quantum machine learning algorithms, and from theoretical to practical techniques that help us understand how quantum computers can learn from data. Meer