Machine learning (ML) encompasses a broad range of algorithms and modeling tools used
for a vast array of data processing tasks, which has entered most scientific disciplines in …

Abstract Machine Learning (ML) is one of the most exciting and dynamic areas of modern
research and application. The purpose of this review is to provide an introduction to the core …

Predicting the properties of complex, large-scale quantum systems is essential for
develo** quantum technologies. We present an efficient method for constructing an …

Quantum information technologies, on the one hand, and intelligent learning systems, on the
other, are both emergent technologies that are likely to have a transformative impact on our …

The experimental realization of increasingly complex synthetic quantum systems calls for the
development of general theoretical methods to validate and fully exploit quantum resources …

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 …

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 …

Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural
sciences. Today, AI has started to advance natural sciences by improving, accelerating, and …

A major bottleneck in the development of scalable many-body quantum technologies is the
difficulty in benchmarking state preparations, which suffer from an exponential 'curse of …

Quantum matter, the research field studying phases of matter whose properties are
intrinsically quantum mechanical, draws from areas as diverse as hard condensed matter …