The challenge posed by the many-body problem in quantum physics originates from the
difficulty of describing the nontrivial correlations encoded in the exponential complexity of …

As the name implies, Monte Carlo (MC) methods employ random numbers to solve
problems. The range of problems that may be treated by MC is substantial; these include …

In this review, we discuss the recent progress in develo** geminal-based theories for
challenging problems in quantum chemistry. Specifically, we focus on the antisymmetrized …

A quantum generalization of Natural Gradient Descent is presented as part of a general-
purpose optimization framework for variational quantum circuits. The optimization dynamics …

Machine learning, one of today's most rapidly growing interdisciplinary fields, promises an
unprecedented perspective for solving intricate quantum many-body problems …

Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural
sciences. Today, AI has started to advance natural sciences by improving, accelerating, and …

Over the past several decades, computational approaches to studying strongly-interacting
systems have become increasingly varied and sophisticated. This book provides a …

In experimentally realistic situations, quantum systems are never perfectly isolated and the
coupling to their environment needs to be taken into account. Often, the effect of the …

Neural-network quantum states have been successfully used to study a variety of lattice and
continuous-space problems. Despite a great deal of general methodological developments …

The possibility to simulate the properties of many-body open quantum systems with a large
number of degrees of freedom (dof) is the premise to the solution of several outstanding …