Quantum optimal control, a toolbox for devising and implementing the shapes of external
fields that accomplish given tasks in the operation of a quantum device in the best way …

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 …

Compiling quantum algorithms for near-term quantum computers (accounting for
connectivity and native gate alphabets) is a major challenge that has received significant …

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

Optimal control theory is a powerful mathematical tool, which has known a rapid
development since the 1950s, mainly for engineering applications. More recently, it has …

Emerging reinforcement learning techniques using deep neural networks have shown great
promise in control optimization. They harness non-local regularities of noisy control …

The ability to prepare a physical system in a desired quantum state is central to many areas
of physics such as nuclear magnetic resonance, cold atoms, and quantum computing. Yet …

We present a major update to QuSpin, SciPostPhys. 2.1. 003--an open-source Python
package for exact diagonalization and quantum dynamics of arbitrary boson, fermion and …

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 …

Reinforcement learning has been widely used in many problems, including quantum control
of qubits. However, such problems can, at the same time, be solved by traditional, non …