Quantum Deformed Neural Networks

Roberto Bondesan, Max Welling

Keywords: Quantum machine learning, Binary neural networks, Bayesian deep learning

Abstract: We develop a new quantum neural network layer designed to run efficiently on a quantum computer but that can be simulated on a classical computer when restricted in the way it entangles input states. We first ask how a classical neural network architecture, both fully connected or convolutional, can be executed on a quantum computer using quantum phase estimation. We then deform the classical layer into a quantum design which entangles activations and weights into quantum superpositions. While the full model would need the exponential speedups delivered by a quantum computer, a restricted class of designs represent interesting new classical network layers that still use quantum features. We show that these quantum deformed neural networks can be trained and executed on normal data such as images, and even classically deliver modest improvements over standard architectures.

One-sentence Summary: We develop a new quantum neural network and simulate a restricted version classically for real world data sizes for the first time, showing modest improvements over standard architectures.

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Reviewed Version (pdf): https://openreview.net/references/pdf?id=2vDXgxwIZo