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 …

We present a coherent counter-diabatic quantum protocol to prepare ground states in the
lattice gauge map** of all-to-all Ising models (LHZ) with considerably enhanced final …

Generative modeling with machine learning has provided a new perspective on the data-
driven task of reconstructing quantum states from a set of qubit measurements. As …

Single-component ultracold atomic Fermi gases are usually described using noninteracting
many-fermion models. However, recent experiments reached a regime where p-wave …

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

Population control is an essential component of any projector Monte Carlo algorithm. This
control mechanism usually introduces a bias in the sampled quantities that is inversely …

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 …

We investigate the use of variational wave functions that mimic stochastic recurrent neural
networks, specifically, unrestricted Boltzmann machines, as guiding functions in projective …

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 …