Closed-loop optimization

Toolkit for closed-loop optimizations.

For a quick introduction, see the Find optimal pulses with automated optimization tutorial and the How to automate closed-loop hardware optimization user guide.

The Boulder Opal Toolkits are currently in beta phase of development. Breaking changes may be introduced.

Functions

Methods to perform common tasks. You can access them from the closed_loop namespace of the Qctrl object. For example, to use optimize:

qctrl = Qctrl()
qctrl.closed_loop.optimize(...)

Following is a list of functions in the closed_loop toolkit:

optimize

Run a closed-loop optimization to find a minimum of the given cost function.

Classes

Classes to store common data types for the methods in the toolkit. You can access them from the closed_loop namespace of the Qctrl object. For example, to use Cmaes:

qctrl = Qctrl()
qctrl.closed_loop.Cmaes(...)

Following is a list of classes in the closed_loop toolkit:

`Cmaes`	The covariance matrix adaptation evolution strategy (CMA-ES) optimizer.
`CrossEntropy`	The cross-entropy optimizer.
`GaussianProcess`	The Gaussian process optimizer.
`NeuralNetwork`	The neural network optimizer.
`Optimizer`	Abstract class for optimizers used in closed-loop control.
`SimulatedAnnealing`	The simulated annealing optimizer.