ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)

Training and Meta-Training Binary Neural Networks with Quantum Computing

Abdulah Fawaz, Paul Klein, Sebastien Piat, Simone Severini B, Peter Mountney

Published Jul 25, 2019• 17 citations

Abstract

Quantum computers promise significant advantages over classical computers for a number of different applications. We show that the complete loss function landscape of a neural network can be represented as the quantum state output by a quantum computer. We demonstrate this explicitly for a binary neural network and, further, show how a quantum computer can train the network by manipulating this state using a well-known algorithm known as quantum amplitude amplification. We further show that with minor adaptation, this method can also represent the meta-loss landscape of a number of neural network architectures simultaneously. We search this meta-loss landscape with the same method to simultaneously train and design a binary neural network.

DOI: 10.1145/3292500.3330953

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