Quantum machine learning is a rapidly growing field at the intersection of quantum
technology and artificial intelligence. This review provides a two-fold overview of several key …

In this book, we provide a comprehensive introduction to the most recent advances in the
application of machine learning methods in quantum sciences. We cover the use of deep …

In recent years, the interest in leveraging quantum effects for enhancing machine learning
tasks has significantly increased. Many algorithms speeding up supervised and …

Quantum error correction is widely thought to be the key to fault-tolerant quantum
computation. However, determining the most suited encoding for unknown error channels or …

Machine learning can help us in solving problems in the context of big-data analysis and
classification, as well as in playing complex games such as Go. But can it also be used to …

Quantum algorithms have been successfully applied to provide computational speed ups to
various machine-learning tasks and methods. A notable exception to this has been deep …

Finding optical setups producing measurement results with a targeted probability distribution
is hard, as a priori the number of possible experimental implementations grows …

The last decade has seen an unprecedented growth in artificial intelligence and photonic
technologies, both of which drive the limits of modern-day computing devices. In line with …

According to a mainstream position in contemporary cognitive science and philosophy, the
use of abstract compositional concepts is amongst the most characteristic indicators of …

To make progress in science, we often build abstract representations of physical systems
that meaningfully encode information about the systems. Such representations ignore …