Deep learning is transforming most areas of science and technology, including electron
microscopy. This review paper offers a practical perspective aimed at developers with …

We review the development of generative modeling techniques in machine learning for the
purpose of reconstructing real, noisy, many-qubit quantum states. Motivated by its …

Despite the success of neural networks at solving concrete physics problems, their use as a
general-purpose tool for scientific discovery is still in its infancy. Here, we approach this …

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

Abstract Machine learning techniques enhanced by noisy intermediate-scale quantum
(NISQ) devices and especially variational quantum circuits (VQC) have recently attracted …

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 …

Recently, tremendous progress has been made in the field of quantum science and
technologies: different platforms for quantum simulation as well as quantum computing …

Generative models are a promising technology for the enhancement of quantum simulators.
These machine learning methods are capable of reconstructing a quantum state from …

We discuss quantum state tomography via a stepwise reconstruction of the eigenstates of
the mixed states produced in experiments. Our method is tailored to the experimentally …

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 …