Quantum Machine Learning and Grover's Algorithm for Quantum Optimization of Robotic Manipulators
Abstract
Optimizing high-degree of freedom robotic manipulators requires searching complex, high-dimensional configuration spaces, a task that is computationally challenging for classical methods. This paper introduces a quantum native framework that integrates quantum machine learning with Grover's algorithm to solve kinematic optimization problems efficiently. A parameterized quantum circuit is trained to approximate the forward kinematics model, which then constructs an oracle to identify optimal configurations. Grover's algorithm leverages this oracle to provide a quadratic reduction in search complexity. Demonstrated on simulated 1-DoF, 2-DoF, and dual-arm manipulator tasks, the method achieves significant speedups-up to 93x over classical optimizers like Nelder Mead as problem dimensionality increases. This work establishes a foundational, quantum-native framework for robot kinematic optimization, effectively bridging quantum computing and robotics problems.
Cite
@article{arxiv.2509.07216,
title = {Quantum Machine Learning and Grover's Algorithm for Quantum Optimization of Robotic Manipulators},
author = {Hassen Nigatu and Shi Gaokun and Li Jituo and Wang Jin and Lu Guodong and Howard Li},
journal= {arXiv preprint arXiv:2509.07216},
year = {2025}
}