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Grain boundaries (GBs) frequently emerge in a CVD-grown large-scale transition metal dichalcogenides monolayer thin film, which affect the electronic and optical properties of the material. Photoluminescence (PL) can be easily quenched/enhanced at GBs, which are, however, merely investigated in relatively large tilt angles ([Formula: see text]) in previous research. Here, we experimentally examine the PL properties of monolayer WS2 GBs with tilt angles as small as a few degrees. Contrary to conventional wisdom, we find that PL intensity remains intact by the GBs when their tilt angles [Formula: see text]. The abnormal PL behavior is elucidated by a detailed structure analysis on the dislocation cores. For a small tilt angle, the strain fields introduced by the defective cores are sparsely distributed without mutual coupling, and the chemical stoichiometry along the GBs preserves very well. These two key structural features of the small-tilt-angle GBs allow excitons to diffuse transparently across the GB, leading to a neglectable influence on the optical and electronic properties, as verified by our first-principle simulations. The PL invariant of the small-tilt-angle GBs sheds light on the future development of CVD-grown wafer-scale techniques and their optical applications.

With the advancement of computing power and algorithms, machine learning has been a powerful tool in numerous applications nowadays. However, the hardware limitation of classical computers and the increasing size of datasets urge the community to explore new techniques for machine learning. Quantum‐enhanced machine learning is such a rapidly growing field. It refers to quantum algorithms that are implemented in quantum computers, which can improve the computational speed of classical machine learning tasks and often promises an exponential speedup. In the past few years, the development of experimental quantum technologies leads to many experimental demonstrations of quantum‐enhanced machine learning in diverse physical systems. Here, the recent experimental progress in this field in two typical spin‐based quantum systems—nuclear magnetic resonance and nitrogen‐vacancy centers in diamond—is reviewed, and the ongoing challenges are discussed.

Entanglement is a key property in the development of quantum technologies and in the study of quantum many-body simulations. However, entanglement measurement typically requires quantum full-state tomography (FST). Here we present a neural network-assisted protocol for measuring entanglement in equilibrium and non-equilibrium states of local Hamiltonians. Instead of FST, it can learn comprehensive entanglement quantities from single-qubit or two-qubit Pauli measurements, such as Rényi entropy, partially-transposed (PT) moments, and coherence. It is also exciting that our neural network is able to learn the future entanglement dynamics using only single-qubit traces from the previous time. In addition, we perform experiments using a nuclear spin quantum processor and train an adoptive neural network to study entanglement in the ground and dynamical states of a one-dimensional spin chain. Quantum phase transitions (QPT) are revealed by measuring static entanglement in ground states, and the entanglement dynamics beyond measurement time is accurately estimated in dynamical states. These precise results validate our neural network. Our work will have a wide range of applications in quantum many-body systems, from quantum phase transitions to intriguing non-equilibrium phenomena such as quantum thermalization.