Quantum-Driven Constraint Optimization for Robotics

Jincheng Zhang

Published Sep 6, 2026• 0 citations

Abstract

This paper investigates the application of quantum-enhanced constraint optimization to robotics, specifically focusing on dynamic adjustment of robot control parameters to maximize task performance. Traditional optimization methods often struggle with complex, dynamic environments, and quantum computing offers the potential for significant speed-ups in solving optimization problems. We propose a novel algorithm leveraging a quantum-inspired optimization technique – specifically, a modified version of Grover's algorithm – to rapidly identify optimal control parameters. The core claim is that this algorithm can dramatically improve the efficiency and responsiveness of robotic systems, particularly in tasks requiring real-time adaptation. This work demonstrates the feasibility of using quantum mechanics principles to accelerate optimization processes, ultimately enabling more sophisticated and adaptable robotic control.

DOI: 10.5281/zenodo.22474310

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