Forward propagation in Deep Neural Networks
In the "Forward Propagation in a Deep Network" video on Coursera, Andrew NG mentions that there's no way to avoid a for loop to loop through the different layers of the network during forward propagation.
See image showing a deep network with 4 layers, and the requirement of a forloop to compute activations for each layer during forward propagation: https://nimb.ws/CkRVLT
This makes intuitive sense since each layer's activation depends on the previous layer's output.
Warning: start of speculation
My rudimentary understanding of quantum computing is that it somehow "magically" can bypass computing intermediate states -> this is why supposedly quantum computers can break cryptography... or something like that.
I'm wondering if a quantum computer could perform vectorized forward propagation on an L layer deep neural network.
