An oracle that correctly predicts the outcome of every particle physics experiment, the
products of every possible chemical reaction or the function of every protein would …

Artificial intelligence (AI) and machine learning (ML) are expanding in popularity for broad
applications to challenging tasks in chemistry and materials science. Examples include the …

Graphs have provided an expressive mathematical tool to model quantum-mechanical
devices and systems. In particular, it has been recently discovered that graph theory can be …

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 these Lecture Notes, we provide a comprehensive introduction to the most recent
advances in the application of machine learning methods in quantum sciences. We cover …

The task of testing whether two uncharacterized quantum devices behave in the same way
is crucial for benchmarking near-term quantum computers and quantum simulators, but has …

Photons are the physical system of choice for performing experimental tests of the
foundations of quantum mechanics. Furthermore, photonic quantum technology is a main …

Determining Hamiltonian parameters from noisy experimental measurements is a key task
for the control of experimental quantum systems. An interesting experimental platform where …

The core objective of machine-assisted scientific discovery is to learn physical laws from
experimental data without prior knowledge of the systems in question. In the area of …

Identifying phase boundaries of interacting systems is one of the key steps to understanding
quantum many-body models. The development of various numerical and analytical methods …