In the recent years, a dramatic boost of the research is observed at the junction of photonics,
machine learning and artificial intelligence. A new methodology can be applied to the …

Effectively manipulating quantum computing (QC) hardware in the presence of imperfect
devices and control systems is a central challenge in realizing useful quantum computers …

In this work, we put forward an approach of constructing the optimal composite pulse
sequence for robust population transfer in two-level systems. This approach is quite …

In the noisy intermediate-scale quantum era, optimal digitized pulses are requisite for
efficient quantum control. This goal is translated into dynamic programming, in which a deep …

Quantum control methods, like rapid adiabatic passage, stimulated Raman adiabatic
passage, shortcuts to adiabaticity and optimal control, have become an integral part of …

Quantum metrology aims to measure physical quantities based on fundamental quantum
principles, enhancing measurement precision through resources like quantum …

We experimentally investigate deep reinforcement learning (DRL) as an artificial intelligence
approach to control a quantum system. We verify that DRL explores fast and robust digital …

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 …

Disorder in condensed matter and atomic physics is responsible for a great variety of
fascinating quantum phenomena, which are still challenging for understanding, not to …

The exotic nature of quantum mechanics differentiates machine learning applications in the
quantum realm from classical ones. Stream learning is a powerful approach that can be …