Robust interface between flying and topological qubits

Hybrid architectures, consisting of conventional and topological qubits, have recently attracted much attention due to their capability in consolidating the robustness of topological qubits and the universality of conventional qubits. However, these two kinds of qubits are normally constructed in significantly different energy scales, and thus this energy mismatch is a major obstacle for their coupling that supports the exchange of quantum information between them. Here, we propose a microwave photonic quantum bus for a direct strong coupling between the topological and conventional qubits, in which the energy mismatch is compensated by the external driving field via the fractional ac Josephson effect. In the framework of tight-binding simulation and perturbation theory, we show that the energy splitting of the topological qubits in a finite length nanowire is still robust against local perturbations, which is ensured not only by topology, but also by the particle-hole symmetry. Therefore, the present scheme realizes a robust interface between the flying and topological qubits. Finally, we demonstrate that this quantum bus can also be used to generate multipartitie entangled states with the topological qubits.Comment: Accepted for publication in Scientific Report

Regulating Characteristics Analysis of Boiler Feed-water Pump when 600MW Unit Sliding-pressure Operating

AbstractAccording to the characteristics of utility-type generator set in variable load sliding-pressure operation, the paper comprehensively analyzes the features of feed-water system, in which variable-speed main feed-water and power frequency booster-pump connect first in series and then in parallel. It puts emphasis on fitting characteristics equation of feed-water pump under different operations, determining characteristics of feed-water pipeline under sliding-pressure operation, corresponding resistance coefficient, and finally deducing the equation of lift, efficiency and rotating speed when different loads and different sliding-pressures are adapted only by main feed-water pump variable speed adjusting. It takes one power plant 600MW supercritical unit for example to compare the energy consumption of different operation modes, and thus puts forward a more suitable operation mode under different loads, providing theoretical basis for the practical application of project

Implementing universal nonadiabatic holonomic quantum gates with transmons

Geometric phases are well known to be noise-resilient in quantum evolutions/operations. Holonomic quantum gates provide us with a robust way towards universal quantum computation, as these quantum gates are actually induced by nonabelian geometric phases. Here we propose and elaborate how to efficiently implement universal nonadiabatic holonomic quantum gates on simpler superconducting circuits, with a single transmon serving as a qubit. In our proposal, an arbitrary single-qubit holonomic gate can be realized in a single-loop scenario, by varying the amplitudes and phase difference of two microwave fields resonantly coupled to a transmon, while nontrivial two-qubit holonomic gates may be generated with a transmission-line resonator being simultaneously coupled to the two target transmons in an effective resonant way. Moreover, our scenario may readily be scaled up to a two-dimensional lattice configuration, which is able to support large scalable quantum computation, paving the way for practically implementing universal nonadiabatic holonomic quantum computation with superconducting circuits.Comment: v3 Appendix added, v4 published version, v5 published version with correction

Regulation of Fear Extinction in the Basolateral Amygdala by Dopamine D2 Receptors Accompanied by Altered GluR1, GluR1-Ser845 and NR2B Levels

The amygdala, a critical structure for both Pavlovian fear conditioning and fear extinction, receives sparse but comprehensive dopamine innervation and contains dopamine D1 and D2 receptors. Fear extinction, which involves learning to suppress the expression of a previously learned fear, appears to require the dopaminergic system. The specific roles of D2 receptors in mediating associative learning underlying fear extinction require further study. Intra-basolateral amygdala (BLA) infusions of a D2 receptor agonist, quinpirole, and a D2 receptor antagonist, sulpiride, prior to fear extinction and extinction retention were tested 24 h after fear extinction training for long-term memory (LTM). LTM was facilitated by quinpirole and attenuated by sulpiride. In addition, A-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptor glutamate receptor 1 (GluR1) subunit, GluR1 phospho-Ser845, and N-methyl-D-aspartic acid receptor NR2B subunit levels in the BLA were generally increased by quinpirole and down-regulated by sulpiride. The present study suggests that activation of D2 receptors facilitates fear extinction and that blockade of D2 receptors impairs fear extinction, accompanied by changes in GluR1, GluR1-Ser845 and NR2B levels in the amygdala

Deep Learning for Community Detection: Progress, Challenges and Opportunities

As communities represent similar opinions, similar functions, similar purposes, etc., community detection is an important and extremely useful tool in both scientific inquiry and data analytics. However, the classic methods of community detection, such as spectral clustering and statistical inference, are falling by the wayside as deep learning techniques demonstrate an increasing capacity to handle high-dimensional graph data with impressive performance. Thus, a survey of current progress in community detection through deep learning is timely. Structured into three broad research streams in this domain - deep neural networks, deep graph embedding, and graph neural networks, this article summarizes the contributions of the various frameworks, models, and algorithms in each stream along with the current challenges that remain unsolved and the future research opportunities yet to be explored.Comment: Accepted Paper in the 29th International Joint Conference on Artificial Intelligence (IJCAI 20), Survey Trac