In-Depth Excavation and Screening of Triterpene Saponins From the Rhizome of Panax japonicus Using High Performance Liquid Chromatography Coupled to Electrospray Ionization and Quadrupole Time-of-Flight Mass Spectrometry.

The rhizome of Panax japonicus (RPJ) has been used for thousands of years in west China. Triterpene saponins (TSs) were considered to be the main pharmacologically bioactive ingredients in RPJ. However, it is difficult and time-consuming to profile and identify them according to the traditional phytochemical methods. High-performance liquid chromatography coupled to electrospray ionization and quadrupole time-of-flight mass spectrometry (HPLC-ESI-QTOF-MS/MS) was used for chemical identification of TSs from the extract of RPJ in negative ion mode. Their chemical structures were tentatively elucidated based on exact formulas, fragmentation patterns and literature data. In all, 42 TSs were discovered and tentatively characterized in RPJ, of which 12 TSs were identified as potential new compounds according to their molecular mass, fragmentation pattern and chromatographic behavior. The results demonstrated that the developed HPLC-ESI-QTOF-MS/MS method was conducive to the discovery of the active ingredients of RPJ and the establishment of quality standards.

Neural network-based prediction of the secret-key rate of quantum key distribution.

Numerical methods are widely used to calculate the secure key rate of many quantum key distribution protocols in practice, but they consume many computing resources and are too time-consuming. In this work, we take the homodyne detection discrete-modulated continuous-variable quantum key distribution (CV-QKD) as an example, and construct a neural network that can quickly predict the secure key rate based on the experimental parameters and experimental results. Compared to traditional numerical methods, the speed of the neural network is improved by several orders of magnitude. Importantly, the predicted key rates are not only highly accurate but also highly likely to be secure. This allows the secure key rate of discrete-modulated CV-QKD to be extracted in real time on a low-power platform. Furthermore, our method is versatile and can be extended to quickly calculate the complex secure key rates of various other unstructured quantum key distribution protocols.