  Gradient descent methods have long been the de facto standard for training
deep neural networks. Millions of training samples are fed into models with
billions of parameters, which are slowly updated over hundreds of epochs.
Recently, it's been shown that large, randomly initialized neural networks
contain subnetworks that perform as well as fully trained models. This insight
offers a promising avenue for training future neural networks by simply pruning
weights from large, random models. However, this problem is combinatorically
hard and classical algorithms are not efficient at finding the best subnetwork.
In this paper, we explore how quantum algorithms could be formulated and
applied to this neuron selection problem. We introduce several methods for
local quantum neuron selection that reduce the entanglement complexity that
large scale neuron selection would require, making this problem more tractable
for current quantum hardware.
