Quantum optimization for Nonlinear Model Predictive Control

TL;DR

This paper proposes a quantum computing approach to solve nonlinear model predictive control optimization problems, aiming to significantly reduce computational time and improve solution quality in fast, industrial applications.

Contribution

It introduces a novel quantum algorithm for NMPC optimization, addressing computational challenges and potentially enabling real-time control in complex systems.

Findings

Quantum approach can reduce optimization time

Potential for improved solution quality

Applicable to fast industrial control systems

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.