I’m now evaluating the performance of quantum nerural networks in simulation.
However, the computiational time easily reachs to a few hours.
It is difficult to exploit the hyper parameter (e.g. num. of layers, qubits, gate type).
The condition is,
 Laptop PC
 2 qubits
 12 variational parameters (Two StronglyEntanglingLayers)
 2 dimentional input feature x 16384 data (corresponds to 1 epoch)
 loss func. : MSE
 lightning.qubit
 diff_method = ‘adjoint’
 optimizer: Adam (from qml.optimizers)
The computational time is approximately 100 sec /epoch.
Therefore, in case of ~100 epochs, the total time becomes 2~3 hours.
I have tested a lot of ways.

qulacs.simulator is fast, but the diff_method should be paramershift. Threrefore, it is not fast in case of gradientdescent based learning.
To date, lightning.qubit would be fastest. 
A fastest diff_method is adjoint. “Backprop” is not fast, at least, when the number of parameter is not so large.
How can I do for further speeding up?
“Using better machine as GPU cluster” is only solution?