The future development of quantum technologies relies on creating and manipulating
quantum systems of increasing complexity, with key applications in computation, simulation …

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 …

Machine learning (ML) has emerged as a formidable force for identifying hidden but
pertinent patterns within a given data set with the objective of subsequent generation of …

Quantum state tomography (QST) is a challenging task in intermediate-scale quantum
devices. Here, we apply conditional generative adversarial networks (CGANs) to QST. In the …

Quantum tomography is currently ubiquitous for testing any implementation of a quantum
information processing device. Various sophisticated procedures for state and process …

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 …

We build a general quantum state tomography framework that makes use of machine
learning techniques to reconstruct quantum states from a given set of coincidence …

We revisit the application of neural networks to quantum state tomography. We confirm that
the positivity constraint can be successfully implemented with trained networks that convert …

In recent decades, transportation planning researchers have used diverse types of machine
learning (ML) algorithms to research a wide range of topics. This review paper starts with a …

Classical shadow tomography provides an efficient method for predicting functions of an
unknown quantum state from a few measurements of the state. It relies on a unitary channel …