8 research outputs found
Effects of electrode surface roughness on motional heating of trapped ions
Electric field noise is a major source of motional heating in trapped ion
quantum computation. While the influence of trap electrode geometries on
electric field noise has been studied in patch potential and surface adsorbate
models, only smooth surfaces are accounted for by current theory. The effects
of roughness, a ubiquitous feature of surface electrodes, are poorly
understood. We investigate its impact on electric field noise by deriving a
rough-surface Green's function and evaluating its effects on adsorbate-surface
binding energies. At cryogenic temperatures, heating rate contributions from
adsorbates are predicted to exhibit an exponential sensitivity to local surface
curvature, leading to either a large net enhancement or suppression over smooth
surfaces. For typical experimental parameters, orders-of-magnitude variations
in total heating rates can occur depending on the spatial distribution of
absorbates. Through careful engineering of electrode surface profiles, our
results suggests that heating rates can be tuned over orders of magnitudes.Comment: 12 pages, 5 figure
Wave scattering and splitting by magnetic metamaterials
We study experimentally propagation of electromagnetic waves
through a slab of uniaxial magnetic metamaterial. We observe a range of
novel phenomena including partial focusing and splitting into multiple
transmitted beams.We demonstrate that while some of these experimentally
observed effects can be described within the approximation of an effective
medium, a deeper understanding of the experimental results requires a
rigorous study of internal eigenmodes of the lattice of resonators
Noise in Grover's Quantum Search Algorithm
Grover's quantum algorithm improves any classical search algorithm. We show
how random Gaussian noise at each step of the algorithm can be modelled easily
because of the exact recursion formulas available for computing the quantum
amplitude in Grover's algorithm. We study the algorithm's intrinsic robustness
when no quantum correction codes are used, and evaluate how much noise the
algorithm can bear with, in terms of the size of the phone book and a desired
probability of finding the correct result. The algorithm loses efficiency when
noise is added, but does not slow down. We also study the maximal noise under
which the iterated quantum algorithm is just as slow as the classical
algorithm. In all cases, the width of the allowed noise scales with the size of
the phone book as N^-2/3.Comment: 17 pages, 2 eps figures. Revised version. To be published in PRA,
December 199