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 …

Boltzmann machines (BMs) are graphical models with interconnected binary units,
employed for the unsupervised modeling of data distributions. When trained on real data …

In this paper we investigate the equilibrium properties of bidirectional associative memories
(BAMs). Introduced by Kosko in 1988 as a generalization of the Hopfield model to a bipartite …

Abstract Restricted Boltzmann Machines are energy-based models capable of learning
probability distributions. In practice, though, it is seriously limited by the fact that the …

Tensor networks, widely used for providing efficient representations of low-energy states of
local quantum many-body systems, have been recently proposed as machine learning …

We consider restricted Boltzmann machines with a binary visible layer and a Gaussian
hidden layer trained by an unlabelled dataset composed of noisy realizations of a single …

Machine learning systems are becoming more and more ubiquitous in increasingly complex
areas, including cutting-edge scientific research. The opposite is also true: the interest in …

We provide a robust defence to adversarial attacks on discriminative algorithms. Neural
networks are naturally vulnerable to small, tailored perturbations in the input data that lead …

Driven by growing computational power and algorithmic developments, machine learning
methods have become valuable tools for analyzing vast amounts of data. Simultaneously …

The human brain is a complex system that is fascinating scientists since a long time. Its
remarkable capabilities include categorization of concepts, retrieval of memories and …