qat.plugins

Plugins are objects to manipulate quantum jobs (circuits, observables) prior to execution and post-process the results.

Currently, the following plugins are offered to the users in myQLM.

Circuit rewriting plugins

`qat.plugins.CircuitInliner`	Inlining circuit inside a stack
`qat.plugins.ObservableSplitter`	Turning observable sampling into qubit sampling

`qat.plugins.CostFunctionPlugin`	Perform variational optimization without observable
`qat.plugins.PSOMinimizePlugin`	Minimizer based on the Particle Swarm Optimization (PSO) algorithm
`qat.plugins.ScipyMinimizePlugin`	Hybrid quantum classical optimization based on the Scipy
`qat.plugins.SPSAMinimizePlugin`	Minimizer based on Simultaneous Perturbation Stochastic Approximation (SPSA) algorithm

`qat.plugins.AdaptVQEPlugin`	Plugin building iteratively efficient ansatze
`qat.plugins.GradientDescentOptimizer`	Natural gradient descent optimizer
`qat.plugins.MultipleLaunchesAnalyzer`	Plugin running several optimizations and keeping the best one
`qat.plugins.SeqOptim`	Iteratively transform into Natural Orbitals basis
`qat.plugins.TransformObservable`	Plugin performing a transformation on the Observable
`qat.plugins.ZeroNoiseExtrapolator`	Plugin performing Zero-Noise Extrapolation

`qat.plugins.AbstractPlugin`	Abstract class of all plugins
`qat.plugins.Display`	Display a quantum circuit in a terminal
`qat.plugins.Junction`	Specialized abstract plugin allowing iterations inside a Qaptiva stack
`qat.plugins.Optimizer`	Specialized abstract junction performing variational optimization
`qat.plugins.QuameleonPlugin`	Plugin emulating hardware constraints
`qat.plugins.RemotePlugin`	Connect to a plugin running in a remote server