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Trapped Ion Quantum Computing

Quantum optimization for Nonlinear Model Predictive Control

arXiv
Authors: Carlo Novara, Mattia Boggio, Deborah Volpe

Year

2024

Paper ID

37651

Status

Preprint

Abstract Read

~2 min

Abstract Words

102

Citations

N/A

Abstract

Nonlinear Model Predictive Control (NMPC) is a general and flexible control approach, used in many industrial contexts, and is based on the online solution of a nonlinear optimization problem. This operation requires in general a high computational cost, which may compromise the NMPC implementation in "fast" applications, especially if a large number variables is involved. To overcome this issue, we propose a quantum computing approach for the solution of the NMPC optimization problem. Assuming the availability of an efficient quantum computer, the approach has the potential to considerably decrease the computational time and/or enhance the solution quality compared to classical algorithms.

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  • This paper contributes to the Trapped-Ion Quantum Computing research area in the Quantum Articles archive.
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  • Nonlinear Model Predictive Control (NMPC) is a general and flexible control approach, used in many industrial contexts, and is based on the online solution of a nonlinear...

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References & Citation Signals

[1] DOI https://doi.org/arXiv:2410.19467 [2] arXiv https://arxiv.org/abs/2410.19467 [3] Publisher https://arxiv.org/abs/2410.19467

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