Entanglement

As one of the fundamental aspects of quantum computing, entanglement plays also an important role in quantum machine learning. Our package offers methods for calculating the entangling capability of a particular model. Currently, only the "Meyer-Wallach" measure is implemented, but other will be added soon!

In the simplest case, this could look as follows:

from qml_essentials.model import Model
from qml_essentials.entanglement import Entanglement

model = Model(
            n_qubits=2,
            n_layers=1,
            circuit_type="HardwareEfficient",
        )

ent_cap = Entanglement.meyer_wallach(
    model, n_samples=1000, seed=1000
)

Here, n_samples is the number of samples for the parameters, sampled according to the default initialization strategy of the model, and seed is the random number generator seed. Note, that every function in this class accepts keyword-arguments which are being passed to the model call, so you could e.g. enable caching by

ent_cap = Entanglement.meyer_wallach(
    model, n_samples=1000, seed=1000, cache=True
)

If you set n_samples=None, we will use the currently stored parameters of the model to estimate the degree of entanglement.