Implementing Any Nonlinear Quantum Neuron

Fernando M. de Paula Neto

doi:10.1109/TNNLS.2019.2938899

2020-09-01

PRE

Fernando M. de Paula Neto *

Teresa B. Ludermir

W. Oliveira

Adenilton J. da Silva

DOI：10.1109/TNNLS.2019.2938899

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摘要

En 中文

The ability of artificial neural networks (ANNs) to adapt to input data and perform generalizations is intimately connected to the use of nonlinear activation and propagation functions. Quantum versions of ANN have been proposed to take advantage of the possible supremacy of quantum over classical computing. To date, all proposals faced the difficulty of implementing nonlinear activation functions since quantum operators are linear. This brief presents an architecture to simulate the computation of an arbitrary nonlinear function as a quantum circuit. This computation is performed on the phase of an adequately designed quantum state, and quantum phase estimation recovers the result, given a fixed precision, in a circuit with linear complexity in function of ANN input size.

Keyword:

Qubit

Neurons

Registers

Integrated circuit modeling

Computational modeling

Biological neural networks

Quantum computing

quantum Fourier transform (QFT)

quantum neural networks

quantum neuron

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期刊

IEEE Transactions on Neural Networks and Learning Systems

IF:

8.9

论文数:

7.6K

被引数:

7.2W

机构

Universidade Federal de Pernambuco

学者数:

1.3W

论文数: 7.3K

被引数: 5.3K

universidade federal rural de pernambuco (ufrpe)

学者数:

3.7K

论文数: 2.1K

被引数: 0

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