Quantum physics has changed the way we understand our environment, and one of its
branches, quantum mechanics, has demonstrated accurate and consistent theoretical …

Realizing the full potential of quantum technologies requires precise real-time control on
time scales much shorter than the coherence time. Model-free reinforcement learning …

For complex flow problems such as wall-bounded turbulence with multi-scale and strongly
nonlinear characteristics, the non-orthogonal and non-uniform meshes commonly used in …

Quantum computing has emerged as a promising technology capable of solving complex
computational problems more efficiently than classical computers. Among the various …

Two-qubit gates in trapped-ion quantum computers are generated by applying spin-
dependent forces that temporarily entangle the internal state of the ion with its motion. Laser …

In this work, we develop a supervised learning model for implementing robust quantum
control in composite-pulse systems, where the training parameters can be either phases …

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 …

Polar molecules are a promising platform for achieving scalable quantum information
processing because of their long-range electric dipole–dipole interactions. Here, we take the …

In the realm of quantum control, reinforcement learning, a prominent branch of machine
learning, emerges as a competitive candidate for computer-assisted optimal experiment …

There recently has been increasing interest in the research and application of the rotational
Doppler effect (RDE), which paves a promising way to detect rotating objects remotely. In …