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

A Study of Complex Deep Learning Networks on High Performance, Neuromorphic, and Quantum Computers