In recent years the dramatic progress in machine learning has begun to impact many areas
of science and technology significantly. In the present perspective article, we explore how …

In recent years, neural-network quantum states have emerged as powerful tools for the study
of quantum many-body systems. Electronic structure calculations are one such canonical …

The last decades saw a huge rise of artificial intelligence (AI) as a powerful tool to boost
industrial and scientific research in a broad range of fields. AI and photonics are develo** …

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 …

Identifying phase transitions is one of the key challenges in quantum many-body physics.
Recently, machine learning methods have been shown to be an alternative way of localising …

We develop a variational approach to simulating the dynamics of open quantum many-body
systems using deep autoregressive neural networks. The parameters of a compressed …

Closed loop quantum control uses measurement to control the dynamics of a quantum
system to achieve either a desired target state or target dynamics. In the case when the …

We apply deep-neural-network-based techniques to quantum state classification and
reconstruction. Our methods demonstrate high classification accuracies and reconstruction …

Informationally complete (IC) positive operator-valued measures (POVMs) are generalized
quantum measurements that offer advantages over the standard computational basis …

We perform quantum process tomography (QPT) for both discrete-and continuous-variable
quantum systems by learning a process representation using Kraus operators. The Kraus …