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Qiskit Machine Learning 0.2.1
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@manoelmarques manoelmarques released this 24 Aug 12:33
· 7 commits to stable/0.2 since this release
0.2.1
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Learn about vigilant mode.

Changelog

Added

  • The class TrainableModel, and its sub-classes NeuralNetworkClassifier, NeuralNetworkRegressor, VQR, VQC, have a new optional argument callback. User can optionally provide a callback function that can access the intermediate training data to track the optimization process, else it defaults to None. The callback function takes in two parameters: the weights for the objective function and the computed objective value. For each iteration an optimizer invokes the callback and passes current weights and computed value of the objective function.
  • Classification models (i.e. models that extend the NeuralNetworkClassifier class like VQC) can now handle categorical target data in methods like fit() and score(). Categorical data is inferred from the presence of string type data and is automatically encoded using either one-hot or integer encodings. Encoder type is determined by the one_hot argument supplied when instantiating the model.

Fixed

  • Fix a bug, where qiskit_machine_learning.circuit.library.RawFeatureVector.copy() didn’t copy all internal settings which could lead to issues with the copied circuit. As a consequence qiskit_machine_learning.circuit.library.RawFeatureVector.bind_parameters() is also fixed.

  • The QNN weight parameter in TorchConnector is now registered in the torch DAG as weight, instead of _weights. This is consistent with the PyTorch naming convention and the weight property used to get access to the computed weights.

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