Practical challenges in simulating quantum systems on classical computers have been
widely recognized in the quantum physics and quantum chemistry communities over the …

Abstract Machine Learning (ML) is one of the most exciting and dynamic areas of modern
research and application. The purpose of this review is to provide an introduction to the core …

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 …

Obtaining accurate properties of many-body interacting quantum matter is a long-standing
challenge in theoretical physics and chemistry, rooting into the complexity of the many-body …

Variational methods have proven to be excellent tools to approximate the ground states of
complex many-body Hamiltonians. Generic tools such as neural networks are extremely …

We introduce a variational wave function for many-body ground states that involves
imaginary-time evolution with two different Hamiltonians in an alternating fashion with …

The projective quantum Monte Carlo (PQMC) algorithms are among the most powerful
computational techniques to simulate the ground-state properties of quantum many-body …

Analyzing quantum many-body problems and elucidating the entangled structure of
quantum states is a significant challenge common to a wide range of fields. Recently, a …

To analyze quantum many-body Hamiltonians, recently, machine learning techniques have
been shown to be quite useful and powerful. However, the applicability of such machine …

We find an exact mapping from the generalized Ising models with many-spin interactions to
equivalent Boltzmann machines, ie, the models with only two-spin interactions between …