well can do all video abstract
quantum effects are everywhere l
from a solstice semiconductors to super conductors to chemistry and biology
similarity the field of control is essential for most model technology for example regarding robotics
died in systems micro controllers in chemical engineering
naturally we would also like to extend controlled and then a scale systems and quantum
dynamics in atoms molecules and quantum devices
this can be done by engineering the hamiltonian that governs the evolution of the system
my have the application of optimized extol of electromagnetic fields
many applications of quantum control
in this paper we focus on the implementation of robust quantum gates which ones realized
with pave the way to full scale quantum computers
and major obstacle for the realization of robust quantum gates uncontrollable interactions with the environment
that lead to d coherence eliminating the system's not classical properties
different types of environment in the markovian grapheme system back correlations tk fast so that
the environment has effectively no memory of past interactions with the system
many physical processes of this nature for example the spontaneous emission of falling tones and
phone and what collusion of the facing atomic labels
on the other hand it correlations between the system and that all significant on the
times you know which the system impulse
then the environment is non-markovian
typically the case for small structured environments such as one almost is computed surrounded by
weakly coupled noise q s found solid state systems
both types of environment all important in practice leading us to the key question we
study in this paper
how to the differences between these environments affect our ability to coherently controls the dynamics
specifically we address in the implementation of quantum gates in different environments using optimal control
it's really filters finite time resolution they minimize kate errors using an iterative algorithm
in each iteration the fields are updated four times at once as shell using a
quasi newton message was extracted at gradient which is more efficient than the popular quote
of method
the performance of the algorithm is analyzed in terms of the minimum gate errors obtained
in the convergence behavior
a simple problems little variability is observed for more challenging task however frequent wrapping long
tails a diminishing returns make sensible termination conditions and repeated runs essential
to quantify the likelihood of success of a typical run and the time required on
average to find a suitable field
new notions of success rate and sixty four introduce
density plots show then utilized to study the effects of parameters that risky operation times
and initial field of these performance indicators
we also consider how feels optimize in the closed system setting perform open systems
in the non market in case of fields can be substantially improved by explicitly considering
the environment
well almost no improvement in the markovian case
how does the controls work
in the non-markovian case plots a trajectory plot shows that the improved performance controls
is mainly due to suppression of indirect no skip the expectation that result from the
closed system optimal the others are applied
however certainly factors such as feel they could cause unwanted most cupid excitations that renders
the controls completely ineffective
i can be mitigated by explicitly considering leakage in the optimisation but at the expense
of substantially increase kate operation time to control complexity
as shown this demonstrated limited control robustness an underscore c important the system design and
characterization for control