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Trapped Ion Quantum Computing Quantum Simulation

Non-binary artificial neuron with phase variation implemented on a quantum computer

arXiv

Authors: Jhordan Silveira de Borba, Jonas Maziero

Year

2024

Paper ID

37433

Status

Preprint

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~2 min

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Abstract

The first artificial quantum neuron models followed a similar path to classic models, as they work only with discrete values. Here we introduce an algorithm that generalizes the binary model manipulating the phase of complex numbers. We propose, test, and implement a neuron model that works with continuous values in a quantum computer. Through simulations, we demonstrate that our model may work in a hybrid training scheme utilizing gradient descent as a learning algorithm. This work represents another step in the direction of evaluation of the use of artificial neural networks efficiently implemented on near-term quantum devices.

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This paper contributes to the Quantum Simulation research area in the Quantum Articles archive.
It adds a 2024 reference point for readers tracking recent quantum research.
The first artificial quantum neuron models followed a similar path to classic models, as they work only with discrete values.

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References & Citation Signals

[1] DOI https://doi.org/arXiv:2410.23373 [2] arXiv https://arxiv.org/abs/2410.23373 [3] Publisher https://arxiv.org/abs/2410.23373

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