207 research outputs found
GTRACE-RS: Efficient Graph Sequence Mining using Reverse Search
The mining of frequent subgraphs from labeled graph data has been studied
extensively. Furthermore, much attention has recently been paid to frequent
pattern mining from graph sequences. A method, called GTRACE, has been proposed
to mine frequent patterns from graph sequences under the assumption that
changes in graphs are gradual. Although GTRACE mines the frequent patterns
efficiently, it still needs substantial computation time to mine the patterns
from graph sequences containing large graphs and long sequences. In this paper,
we propose a new version of GTRACE that enables efficient mining of frequent
patterns based on the principle of a reverse search. The underlying concept of
the reverse search is a general scheme for designing efficient algorithms for
hard enumeration problems. Our performance study shows that the proposed method
is efficient and scalable for mining both long and large graph sequence
patterns and is several orders of magnitude faster than the original GTRACE
Efficient decoherence-free entanglement distribution over lossy quantum channels
We propose and demonstrate a scheme for boosting up the efficiency of
entanglement distribution based on a decoherence-free subspace (DFS) over lossy
quantum channels. By using backward propagation of a coherent light, our scheme
achieves an entanglement-sharing rate that is proportional to the transmittance
T of the quantum channel in spite of encoding qubits in multipartite systems
for the DFS. We experimentally show that highly entangled states, which can
violate the Clauser-Horne-Shimony-Holt inequality, are distributed at a rate
proportional to T.Comment: 5pages, 5figure
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