Quantum computing model of an artificial neuron with continuously valued input data

Year: 2020

Authors: Mangini S., Tacchino F., Gerace D., Macchiavello C., Bajoni D.

Autors Affiliation: Univ Pavia, Dipartimento Fis, Via Bassi 6, I-27100 Pavia, Italy; IBM Res Zurich, IBM Quantum, Saumerstr 4, CH-8803 Ruschlikon, Switzerland; INFN Sez Pavia, Via Bassi 6, I-27100 Pavia, Italy; CNR INO, Largo E Fermi 6, I-50125 Florence, Italy; Univ Pavia, Dipartimento Ingn Ind Informazione, I-27100 Pavia, Italy.

Abstract: Artificial neural networks have been proposed as potential algorithms that could benefit from being implemented and run on quantum computers. In particular, they hold promise to greatly enhance Artificial Intelligence tasks, such as image elaboration or pattern recognition. The elementary building block of a neural network is an artificial neuron, i.e. a computational unit performing simple mathematical operations on a set of data in the form of an input vector. Here we show how the design for the implementation of a previously introduced quantum artificial neuron [npj Quant. Inf. 5, 26], which fully exploits the use of superposition states to encode binary valued input data, can be further generalized to accept continuous-instead of discrete-valued input vectors, without increasing the number of qubits. This further step is crucial to allow for a direct application of gradient descent based learning procedures, which would not be compatible with binary-valued data encoding.

Journal/Review: MACHINE LEARNING-SCIENCE AND TECHNOLOGY

Volume: 1 (4)      Pages from: 45008-1  to: 45008-15

KeyWords: quantum machine learning; quantum artificial neurons; quantum classifiers; quantum algorithms on near term processors
DOI: 10.1088/2632-2153/abaf98

Citations: 25
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