The Boulder Opal Toolkits are currently in beta phase of development. Breaking changes may be introduced.
- class NeuralNetwork(seed=None)
The neural network optimizer.
seed (int, optional) – Seed for the random number generator. Use this option to generate deterministic results from the optimizer.
The neural network optimizer builds and trains a neural network to fit the cost landscape with the data it receives. Then a set of test points are returned, which minimize the neural network’s fitted cost landscape. A gradient based optimizer is used to minimize this landscape, with the points starting from different random initial values.
This method is recommended when you can provide a large amount of data about your system.
The network architecture used by this optimizer is chosen for its good performance on a variety of quantum control tasks.
For best results, you should pass an array of initial_test_parameters evenly sampled over the whole parameter space.