  Current Deep Learning approaches have been very successful using
convolutional neural networks (CNN) trained on large graphical processing units
(GPU)-based computers. Three limitations of this approach are: 1) they are
based on a simple layered network topology, i.e., highly connected layers,
without intra-layer connections; 2) the networks are manually configured to
achieve optimal results, and 3) the implementation of neuron model is expensive
in both cost and power. In this paper, we evaluate deep learning models using
three different computing architectures to address these problems: quantum
computing to train complex topologies, high performance computing (HPC) to
automatically determine network topology, and neuromorphic computing for a
low-power hardware implementation. We use the MNIST dataset for our experiment,
due to input size limitations of current quantum computers. Our results show
the feasibility of using the three architectures in tandem to address the above
deep learning limitations. We show a quantum computer can find high quality
values of intra-layer connections weights, in a tractable time as the
complexity of the network increases; a high performance computer can find
optimal layer-based topologies; and a neuromorphic computer can represent the
complex topology and weights derived from the other architectures in low power
memristive hardware.
