Self-driving laboratories can accelerate materials discovery by achieving scientific
objectives hundreds of times faster than automation alone. However, a self-driving …

Machine learning is emerging as a technology that can enhance physics experiment
execution and data analysis. Here, we apply machine learning to accelerate the production …

We introduce an optimisation method for variational quantum algorithms and experimentally
demonstrate a 100-fold improvement in efficiency compared to naive implementations. The …

Machine learning (ML) is an effective tool to interrogate complex systems to find optimal
parameters more efficiently than through manual methods. This efficiency is particularly …

Abstract Machine-learning (ML) techniques are emerging as a valuable tool in experimental
physics, and among them, reinforcement learning (RL) offers the potential to control high …

We demonstrate single-site-resolved fluorescence imaging of ultracold 87 Rb atoms in a
triangular optical lattice by employing Raman sideband cooling. Combining a Raman …

Control of quantum systems is a central element of high-precision experiments and the
development of quantum technological applications. Control pulses that are typically …

The ability to prepare nonclassical states in a robust manner is essential for quantum
sensors beyond the standard quantum limit. We demonstrate that Bayesian optimal control …

Absorption imaging is the most common probing technique in experiments with ultracold
atoms. The standard procedure involves the division of two frames acquired at successive …

A remarkable consequence of spontaneously breaking the time translational symmetry in a
system, is the emergence of time crystals. In periodically driven systems, discrete time …