12,946 research outputs found
Indirect (source-free) integration method. II. Self-force consistent radial fall
We apply our method of indirect integration, described in Part I, at fourth
order, to the radial fall affected by the self-force. The Mode-Sum
regularisation is performed in the Regge-Wheeler gauge using the equivalence
with the harmonic gauge for this orbit. We consider also the motion subjected
to a self-consistent and iterative correction determined by the self-force
through osculating stretches of geodesics. The convergence of the results
confirms the validity of the integration method. This work complements and
justifies the analysis and the results appeared in Int. J. Geom. Meth. Mod.
Phys., 11, 1450090 (2014).Comment: To appear in Int. J. Geom. Meth. Mod. Phy
Decoherence-protected memory for a single-photon qubit
The long-lived, efficient storage and retrieval of a qubit encoded on a
photon is an important ingredient for future quantum networks. Although systems
with intrinsically long coherence times have been demonstrated, the combination
with an efficient light-matter interface remains an outstanding challenge. In
fact, the coherence times of memories for photonic qubits are currently limited
to a few milliseconds. Here we report on a qubit memory based on a single atom
coupled to a high-finesse optical resonator. By mapping and remapping the qubit
between a basis used for light-matter interfacing and a basis which is less
susceptible to decoherence, a coherence time exceeding 100 ms has been measured
with a time-independant storage-and-retrieval efficiency of 22%. This
demonstrates the first photonic qubit memory with a coherence time that exceeds
the lower bound needed for teleporting qubits in a global quantum internet.Comment: 3 pages, 4 figure
Investigation of topographical stability of the concave and convex Self-Organizing Map variant
We investigate, by a systematic numerical study, the parameter dependence of
the stability of the Kohonen Self-Organizing Map and the Zheng and Greenleaf
concave and convex learning with respect to different input distributions,
input and output dimensions
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