Over the past few years, machine learning has emerged as a powerful computational tool to
tackle complex problems in a broad range of scientific disciplines. In particular, artificial …

Many important challenges in science and technology can be cast as optimization problems.
When viewed in a statistical physics framework, these can be tackled by simulated …

Quantum enhanced optimization of classical cost functions is a central theme of quantum
computing due to its high potential value in science and technology. The variational …

Artificial neural networks have been widely adopted as ansatzes to study classical and
quantum systems. However, for some notably hard systems, such as those exhibiting …

A continuous-time projection quantum Monte Carlo algorithm is employed to simulate the
ground state of a short-range quantum spin-glass model, namely, the two-dimensional …

In recent years, generative artificial neural networks based on restricted Boltzmann
machines (RBMs) have been successfully employed as accurate and flexible variational …

Predicting the output of quantum circuits is a hard computational task that plays a pivotal role
in the development of universal quantum computers. Here we investigate the supervised …

Over the past years, machine learning has emerged as a powerful computational tool to
tackle complex problems over a broad range of scientific disciplines. In particular, artificial …

A nonperturbative theory of zero-phonon transitions in impurity centers in crystals is
proposed in the case of arbitrary linear and quadratic vibronic interaction with a phonon …

Polaron tunneling is a prominent example of a problem characterized by different energy
scales, for which the standard quantum Monte Carlo methods face a slowdown problem. We …