Fixes an issue for the Quantum Neural Networks where the binding order of the inputs and weights might end up being incorrect. Though the params for the inputs and weights are specified to the QNN, the code previously bound the inputs and weights in the order given by the circuit.parameters. This would end up being the right order for the Qiskit circuit library feature maps and ansatzes most often used, as the default parameter names led to the order being as expected. However for custom names etc. this was not always the case and then led to unexpected behavior. The sequences for the input and weights parameters, as supplied, are now always used as the binding order, for the inputs and weights respectively, such that the order of the parameters in the overall circuit no longer matters.
Qiskit Machine Learning has been migrated to the qiskit-community Github organization to further emphasize that it is a community-driven project. To reflect this change, and because we are onboarding additional code-owners and maintainers, with this version (0.7) we have decided to remove all deprecated code, regardless of the time of its deprecation. This ensures that the new members of the development team do not have a large bulk of legacy code to maintain. This can mean one of two things for you as the end-user:
Nothing, if you already migrated your code and no longer rely on any deprecated features.
Otherwise, you should make sure that your workflow doesn’t rely on deprecated classes. If you cannot do that, or want to continue using some of the features that were removed, you should pin your version of Qiskit Machine Learning to 0.6.
For more context on the changes around Qiskit Machine Learning and the other application projects as well as the Algorithms library in Qiskit, be sure to read this blog post.