Changelog

New Features

Added a max_circuits_per_job parameter to the FidelityQuantumKernel used in the case that if more circuits are submitted than the job limit for the backend, the circuits are split up and run through separate jobs.
Removed QuantumKernelTrainer dependency on copy.deepcopy that was throwing an error with real backends. Now, it modifies the TrainableKernel in place. If you would like to use the initial kernel, please call assign_training_parameters() of the TrainableKernel using the initial_point attribute of QuantumKernelTrainer.
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.