We study the quantum evolution of a non-Hermitian qubit realized as a sub-manifold of a dissipative superconducting transmon circuit. Real-time tuning of the system parameters results in non-reciprocal quantum state transfer associated with proximity
to the exceptional points of the effective Floquet Hamiltonian. We observe chiral geometric phases accumulated under state transport, verifying the quantum coherent nature of the evolution in the complex energy landscape and distinguishing between coherent and incoherent effects associated with exceptional point encircling. Our work demonstrates an entirely new method for control over quantum state vectors, highlighting new facets of quantum bath engineering enabled through time-periodic (Floquet) non-Hermitian control.
Graph classification is a highly impactful task that plays a crucial role in a myriad of real-world applications such as molecular property prediction and protein function prediction.Aiming to handle the new classes with limited labeled graphs, few-s
hot graph classification has become a bridge of existing graph classification solutions and practical usage.This work explores the potential of metric-based meta-learning for solving few-shot graph classification.We highlight the importance of considering structural characteristics in the solution and propose a novel framework which explicitly considers global structure and local structure of the input graph. An implementation upon GIN, named SMF-GIN, is tested on two datasets, Chembl and TRIANGLES, where extensive experiments validate the effectiveness of the proposed method. The Chembl is constructed to fill in the gap of lacking large-scale benchmark for few-shot graph classification evaluation, which is released together with the implementation of SMF-GIN at: https://github.com/jiangshunyu/SMF-GIN.
Recent studies on Graph Convolutional Networks (GCNs) reveal that the initial node representations (i.e., the node representations before the first-time graph convolution) largely affect the final model performance. However, when learning the initial
representation for a node, most existing work linearly combines the embeddings of node features, without considering the interactions among the features (or feature embeddings). We argue that when the node features are categorical, e.g., in many real-world applications like user profiling and recommender system, feature interactions usually carry important signals for predictive analytics. Ignoring them will result in suboptimal initial node representation and thus weaken the effectiveness of the follow-up graph convolution. In this paper, we propose a new GCN model named CatGCN, which is tailored for graph learning when the node features are categorical. Specifically, we integrate two ways of explicit interaction modeling into the learning of initial node representation, i.e., local interaction modeling on each pair of node features and global interaction modeling on an artificial feature graph. We then refine the enhanced initial node representations with the neighborhood aggregation-based graph convolution. We train CatGCN in an end-to-end fashion and demonstrate it on semi-supervised node classification. Extensive experiments on three tasks of user profiling (the prediction of user age, city, and purchase level) from Tencent and Alibaba datasets validate the effectiveness of CatGCN, especially the positive effect of performing feature interaction modeling before graph convolution.
Internet of Things (IoT) employs a large number of spatially distributed wireless sen-sors to monitor physical environments, e.g., temperature, humidity, and air pressure, have found wide applications including environmental monitoring, health care m
onitoring, smart cities and precision agriculture. A wireless sensor can collect, analyze, and transmit measurements of its environment. To date, wireless sensors used in IoT are predominately based on electronic devices that may suffer from electromagnetic interference in many circumstances. Immune to the electromagnetic interference, optical sensors provide a significant advantage in harsh environments. Furthermore, by introducing optical resonance to enhanced light-matter interactions, optical sensors based on resonators exhibit small footprints, extreme sensitivity and versatile functionalities, which can signifi-cantly enhance the capability and flexibility of wireless sensors. Here we provide the first demonstration of a wireless photonic sensor node based on whispering-gallery-mode (WGM) optical resonators. The sensor node is controlled via a customized iOS app. Its per-formance was studied in two practical scenarios: (1) real-time measurement of air tempera-ture over 12 hours and (2) aerial mapping of temperature distribution by a sensor node mounted on an unmanned drone. Our work demonstrates the capability of WGM optical sensors in practical applications and may pave the way for large-scale deployments of WGM sensors in IoT.
Electromagnetically-induced-transparency (EIT) and Autler-Townes splitting (ATS) are two prominent examples of coherent interactions between optical fields and multilevel atoms. They have been observed in various physical systems involving atoms, mol
ecules, meta-structures and plasmons. In recent years, there has been an increasing interest in the implementations of all-optical analogues of EIT and ATS via the interacting resonant modes of one or more optical microcavities. Despite the differences in their underlying physics, both EIT and ATS are quantified by the appearance of a transparency window in the absorption or transmission spectrum, which often leads to a confusion about its origin. While in EIT the transparency window is a result of Fano interference among different transition pathways, in ATS it is the result of strong field-driven interactions leading to the splitting of energy levels. Being able to tell objectively whether a transparency window observed in the spectrum is due to EIT or ATS is crucial for clarifying the physics involved and for practical applications. Here we report a systematic study of the pathways leading to EIT, Fano, and ATS, in systems of two coupled whispering-gallery-mode (WGM) microtoroidal resonators. Moreover, we report for the first time the application of the Akaike Information Criterion discerning between all-optical analogues of EIT and ATS, and clarifying the transition between them.
