问题描述：
英语翻译
Question 34.Can stability analyses be performed on closed loop quantum learning algorithms
under uncertainties and disturbances in the measurements and control fields for
general classes of quantum systems and control objectives?
Such an analysis could give insight into how best to operate the laboratory experiments.
In principle,any optimization algorithm can be applied to quantum learning control.
For example,gradient descent and simulated annealing algorithms have been explored
in simulations [111],but the GA outperformed them in several test cases.However,
this subject has not received a thorough examination:
Question 35.Do there exist algorithms that converge with greater efficiency or robustness
than the genetic algorithm for certain classes of quantum mechanical learning control
problems?
In treating question 35 it is important to consider the ability to perform very large numbers
of quantum control experiments,which may overcome certain algorithmic shortcomings
found under more common conditions.This ability is almost unprecedented in
other applications of learning algorithms.
Another approach to quantum learning control is provided by the use of input → output mapping techniques [113,114].These methods develop an effective map between
the inputs (i.e.,the parameters or features defining the control laws) and the outputs (i.e.,
the expectation values of objective operators).A map from the control input space C to
the space of possible expectation values may be determined directly from the laboratory
input and output data; a series of these maps may be needed to cover a sufficiently large
portion of C.The control law that optimally satisfies the objectives can be identified from
these maps using a suitable learning algorithm.Logical next steps in the development of
these methods include answering:
Question 36.What methods can be used to extend the linear input–output learning control
techniques developed in [113,114] to generate nonlinear maps?
中括号的公式编号保留,能帮我翻译的,我之后可以再追加悬赏,
Question 34.Can stability analyses be performed on closed loop quantum learning algorithms
under uncertainties and disturbances in the measurements and control fields for
general classes of quantum systems and control objectives?
Such an analysis could give insight into how best to operate the laboratory experiments.
In principle,any optimization algorithm can be applied to quantum learning control.
For example,gradient descent and simulated annealing algorithms have been explored
in simulations [111],but the GA outperformed them in several test cases.However,
this subject has not received a thorough examination:
Question 35.Do there exist algorithms that converge with greater efficiency or robustness
than the genetic algorithm for certain classes of quantum mechanical learning control
problems?
In treating question 35 it is important to consider the ability to perform very large numbers
of quantum control experiments,which may overcome certain algorithmic shortcomings
found under more common conditions.This ability is almost unprecedented in
other applications of learning algorithms.
Another approach to quantum learning control is provided by the use of input → output mapping techniques [113,114].These methods develop an effective map between
the inputs (i.e.,the parameters or features defining the control laws) and the outputs (i.e.,
the expectation values of objective operators).A map from the control input space C to
the space of possible expectation values may be determined directly from the laboratory
input and output data; a series of these maps may be needed to cover a sufficiently large
portion of C.The control law that optimally satisfies the objectives can be identified from
these maps using a suitable learning algorithm.Logical next steps in the development of
these methods include answering:
Question 36.What methods can be used to extend the linear input–output learning control
techniques developed in [113,114] to generate nonlinear maps?
中括号的公式编号保留,能帮我翻译的,我之后可以再追加悬赏,
问题解答：
我来补答展开全文阅读