Optimizers

Optimizers are objects which can be used to automatically update the parameters of a quantum or hybrid machine learning model. The optimizers you should use are dependent on your choice of classical interface (NumPy, PyTorch, and TensorFlow), and are available from different access points.

Regardless of their origin, all optimizers provide the same core functionality, and PennyLane is fully compatible with all of them.

NumPy Interface

When using the standard NumPy interface, PennyLane offers some built-in optimizers. Some of these are specific to quantum optimization, such as the QNGOptimizer, RotosolveOptimizer, RotoselectOptimizer, and ShotAdaptiveOptimizer.

AdagradOptimizer	Gradient-descent optimizer with past-gradient-dependent learning rate in each dimension.
AdamOptimizer	Gradient-descent optimizer with adaptive learning rate, first and second moment.
GradientDescentOptimizer	Basic gradient-descent optimizer.
MomentumOptimizer	Gradient-descent optimizer with momentum.
NesterovMomentumOptimizer	Gradient-descent optimizer with Nesterov momentum.
QNGOptimizer	Optimizer with adaptive learning rate, via calculation of the diagonal or block-diagonal approximation to the Fubini-Study metric tensor.
RMSPropOptimizer	Root mean squared propagation optimizer.
RotosolveOptimizer	Rotosolve gradient-free optimizer.
RotoselectOptimizer	Rotoselect gradient-free optimizer.
ShotAdaptiveOptimizer	Optimizer where the shot rate is adaptively calculated using the variances of the parameter-shift gradient.

PyTorch Interface

If you are using the PennyLane PyTorch interface, you should import one of the native PyTorch optimizers (found in torch.optim).

TensorFlow Interface

When using the PennyLane TensorFlow interface, you will need to leverage one of the TensorFlow optimizers (found in tf.keras.optimizers).