Transition role of entangled data in quantum machine learning.

Entanglement serves as the resource to empower quantum computing. Recent progress has highlighted its positive impact on learning quantum dynamics, wherein the integration of entanglement into quantum operations or measurements of quantum machine learning (QML) models leads to substantial reductions in training data size, surpassing a specified prediction error threshold. However, an analytical understanding of how the entanglement degree in data affects model performance remains elusive. In this study, we address this knowledge gap by establishing a quantum no-free-lunch (NFL) theorem for learning quantum dynamics using entangled data. Contrary to previous findings, we prove that the impact of entangled data on prediction error exhibits a dual effect, depending on the number of permitted measurements. With a sufficient number of measurements, increasing the entanglement of training data consistently reduces the prediction error or decreases the required size of the training data to achieve the same prediction error. Conversely, when few measurements are allowed, employing highly entangled data could lead to an increased prediction error. The achieved results provide critical guidance for designing advanced QML protocols, especially for those tailored for execution on early-stage quantum computers with limited access to quantum resources.

Impact of Morphological Inhomogeneity on Excitonic States in Highly Mismatched Alloy ZnSe1-XTeX Nanocrystals.

ZnSe1-XTeX nanocrystals (NCs) are promising photon emitters with tunable emission across the violet to orange range and near-unity quantum yields. However, these NCs suffer from broad emission line widths and multiple exciton decay dynamics, which discourage their practicable use. Here, we explore the excitonic states in ZnSe1-XTeX NCs and their photophysical characteristics in relation to the morphological inhomogeneity of highly mismatched alloys. Ensemble and single-dot spectroscopic analysis of a series of ZnSe1-XTeX NC samples with varying Te ratios coupled with computational calculations shows that, due to the distinct electronegativity between Se and Te, nearest-neighbor Te pairs in ZnSe1-XTeX alloys create localized hole states spectrally distributed approximately 130 meV above the 1Sh level of homogeneous ZnSe1-XTeX NCs. This forms spatially separated excitons (delocalized electron and localized hole in trap), accounting for both inhomogeneous and homogeneous line width broadening with delayed recombination dynamics. Our results identify photophysical characteristics of excitonic states in NCs made of highly mismatched alloys and provide future research directions with potential implications for photonic applications.

The Dilemma of Quantum Neural Networks.

The core of quantum machine learning is to devise quantum models with good trainability and low generalization error bounds than their classical counterparts to ensure better reliability and interpretability. Recent studies confirmed that quantum neural networks (QNNs) have the ability to achieve this goal on specific datasets. In this regard, it is of great importance to understand whether these advantages are still preserved on real-world tasks. Through systematic numerical experiments, we empirically observe that current QNNs fail to provide any benefit over classical learning models. Concretely, our results deliver two key messages. First, QNNs suffer from the severely limited effective model capacity, which incurs poor generalization on real-world datasets. Second, the trainability of QNNs is insensitive to regularization techniques, which sharply contrasts with the classical scenario. These empirical results force us to rethink the role of current QNNs and to design novel protocols for solving real-world problems with quantum advantages.

Screening and isolation of a nematicidal sesquiterpene from Magnolia grandiflora L.

The ethanolic extracts from 30 plant species were tested for their nematicidal activity against nematodes Bursaphelenchus xylophilus (Steiner & Buhrer) Nickle and Panagrellus redivivus (L.) Goodey. The leaf extract of Magnolia grandiflora L. exhibited the strongest nematicidal activity against both nematodes, causing 73 and 100% mortality respectively within 48 h at 5 mg mL(-1). A new nematicidal sesquiterpene was obtained from the leaves of M. grandiflora. The compound was determined to be 4,5-epoxy-1(10)E,11(13)-germacradien-12,6-olide, based on spectroscopic methods including 2D NMR techniques. The median lethal concentrations (LC50) of the compound against B. xylophilus and P. redivivus were 71 and 46 mg L(-1) respectively at 48 h. This is the first report of Magnoliaceae species with nematicidal activity.