Controlling the Intrinsic Josephson Junction Number in a Bi2Sr2CaCu2O8+δ\mathbf{Bi_2Sr_2CaCu_2O_{8+\delta}} Mesa

In fabricating $\mathrm{Bi_2Sr_2CaCu_2O_{8+\delta}}$ intrinsic Josephson junctions in 4-terminal mesa structures, we modify the conventional fabrication process by markedly reducing the etching rates of argon ion milling. As a result, the junction number in a stack can be controlled quite satisfactorily as long as we carefully adjust those factors such as the etching time and the thickness of the evaporated layers. The error in the junction number is within $\pm 1$. By additional ion etching if necessary, we can controllably decrease the junction number to a rather small value, and even a single intrinsic Josephson junction can be produced.Comment: to bu published in Jpn. J. Appl. Phys., 43(7A) 200

Quantum computing through electron propagation in the edge states of quantum spin Hall systems

We propose to implement quantum computing based on electronic spin qubits by controlling the propagation of the electron wave packets through the helical edge states of quantum spin Hall systems (QSHs). Specfically, two non-commutative single-qubit gates, which rotate a qubit around z and y axes, can be realized by utilizing gate voltages either on a single QSH edge channel or on a quantum point contact structure. The more challenging two-qubit controlled phase gate can be implemented through the on-demand capacitive Coulomb interaction between two adjacent edge channels from two parallel QSHs. As a result, a universal set of quantum gates can be achieved in an all-electrical way. The fidelity and purity of the two-qubit gate are calculated with both time delay and finite width of the wave packets taken into consideration, which can reach high values with the existing high-quality single electron source

Modeling and Detecting Network Communities with the Fusion of Node Attributes

As a fundamental structure in real-world networks, communities can be reflected by abundant node attributes with the fusion of graph topology. In attribute-aware community detection, probabilistic generative models (PGMs) have become the mainstream fusion method due to their principled characterization and interpretation. Here, we propose a novel PGM without imposing any distributional assumptions on attributes, which is superior to existing PGMs that require attributes to be categorical or Gaussian distributed. Based on the famous block model of graph structure, our model fuses the attribute by describing its effect on node popularity using an additional term. To characterize the effect quantitatively, we analyze the detectability of communities for the proposed model and then establish the requirements of the attribute-popularity term, which leads to a new scheme for the model selection problem in attribute-aware community detection. With the model determined, an efficient algorithm is developed to estimate the parameters and to infer the communities. The proposed method is validated from two aspects. First, the effectiveness of our algorithm is theoretically guaranteed by the detectability condition, whose correctness is verified by numerical experiments on artificial graphs. Second, extensive experiments show that our method outperforms the competing approaches on a variety of real-world networks.Comment: other authors do not want to preprin