Ensemble QCML

The Ensemble QCML model uses an ensemble of state vectors in order to build a quantum representation of the problem. For the Ensemble QCML model the most important parameters are the dims and the axes. You can read about the function of these parameters below, they get passed when qcml.ensemble(...) is called.

qcog_python_client.EnsembleSchema

alias of ModelEnsembleParameters

Special Training Parameters

The Ensemble QCML model has additional parameters to be passed to training.

These parameters are additional to the State Parameters and the additional parameters are valid for all methods of state solution.

These parameters are as folows:

learning_rate_axes: float This is the learning rate that the axes use to optimize them. In some sense you can think of the axes as a set of basis vectors that will continually adjust themselves to better represent the data. Recommended values are around 1e-3 during training. This should be set to 0 or omitted during inference or the model will be updated based on partial inference data. This is important to include during training in order to get the best results. normalize_axes: bool, default True Set this to False in order to not enforce the unitarity of the axes vectors.