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arXiv · 1811.02266

An Artificial Neuron Implemented on an Actual Quantum Processor

Francesco Tacchino·Chiara Macchiavello·Dario Gerace·Daniele Bajoni
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Abstract

Artificial neural networks are the heart of machine learning algorithms and artificial intelligence protocols. Historically, the simplest implementation of an artificial neuron traces back to the classical Rosenblatt's `perceptron', but its long term practical applications may be hindered by the fast scaling up of computational complexity, especially relevant for the training of multilayered perceptron networks. Here we introduce a quantum information-based algorithm implementing the quantum computer version of a perceptron, which shows exponential advantage in encoding resources over alternative realizations. We experimentally test a few qubits version of this model on an actual small-scale quantum processor, which gives remarkably good answers against the expected results. We show that this quantum model of a perceptron can be used as an elementary nonlinear classifier of simple patterns, as a first step towards practical training of artificial quantum neural networks to be efficiently implemented on near-term quantum processing hardware.

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Francesco Tacchino, Chiara Macchiavello, Dario Gerace, Daniele Bajoni. 2018-11-06. An Artificial Neuron Implemented on an Actual Quantum Processor. https://doi.org/10.1038/s41534-019-0140-4

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