5,137 research outputs found
Controlling the Intrinsic Josephson Junction Number in a Mesa
In fabricating 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
. 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
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