I am currently looking into hybrid neural networks. I would like to apply a different optimizer to quantum circuit compared to the classical layers.
The quantum circuit has classical input and parameters so it can be optimised by an classical optimiser.
My network looks like
class NeuralNet(nn.Module) def __init__(self): super(NeuralNet, self).__init__() self.pre_net = nn.Linear(28*28, 4) self.q_params = nn.Parameter(0.01 * torch.randn(q_depth * 4)) #Quantum circuit parameters self.post_net = nn.Linear(4, 10)
1.) How can I apply the same optimiser to all layers, but have a different step size for the quantum circuit parameters
2.) How can I apply a completely different optimiser to the quantum circuit parameters?
Thanks for your help