Quantum machine learning is a rapidly growing field at the intersection of quantum
technology and artificial intelligence. This review provides a two-fold overview of several key …

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 …

Neural quantum states (NQS) attract a lot of attention due to their potential to serve as a very
expressive variational ansatz for quantum many-body systems. Here we study the main …

Due to the exponential growth of the Hilbert space dimension with system size, the
simulation of quantum many-body systems has remained a persistent challenge until today …

We demonstrate quantum many-body state reconstruction from experimental data generated
by a programmable quantum simulator by means of a neural-network model incorporating …

Neural quantum states (NQS) are a promising approach to study many-body quantum
physics. However, they face a major challenge when applied to lattice models: convolutional …

We study the performance of efficient quantum state tomography methods based on neural-
network quantum states using measured data from a two-photon experiment. Machine …

Combining insights from machine learning and quantum Monte Carlo, the stochastic
reconfiguration method with neural network Ansatz states is a promising new direction for …

The measurement precision of modern quantum simulators is intrinsically constrained by the
limited set of measurements that can be efficiently implemented on hardware. This …

The task of classifying the entanglement properties of a multipartite quantum state poses a
remarkable challenge due to the exponentially increasing number of ways in which quantum …