Perform a single step computation for closed-loop optimization.
Use this function to initialize an optimizer, update it with costs from the cost function you define,
and generate more test points for your system to perform a closed-loop optimization.
You can use this approach when your system is too complicated to model, or the computation of
gradient is expensive or impossible.
optimizer (qctrl.dynamic.types.closed_loop_optimization_step.Optimizer) – Option to initialize the optimizer or update the optimization state.
results (List[qctrl.dynamic.types.closed_loop_optimization_step.CostFunctionResult], optional) – Results from evaluating the cost function. You can pass an empty list
(or omit this parameter) if you want to generate more test points but
have not yet evaluated the cost function. Defaults to an empty list.
Note that some optimizers might have extra requirements of results
needed for the first optimization step. You can check the documentation
of the individual optimizers for details.
test_point_count (int, optional) – Requested number of test points to be generated from the optimizer at
this step. This value is a hint to the optimizer, meaning the number of
returned test points might be different to this value. You should set
this value based on the number of test points you can efficiently
calculate as a batch or in parallel. Typically, optimizers will return
at least this many points, but they might return more (for example if a
certain number of points is required in order to move the algorithm to
the next state) or, occasionally, fewer (for example if moving the
algorithm to the next state requires the evaluation of a specific point
and nothing more).
Result from the optimizer.
- Return type
Read our Choosing a control-design (optimization) strategy in Boulder
topic to learn more about control design for your quantum system.
See the How to automate closed-loop hardware optimization