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arXiv · 2509.07216

Quantum Machine Learning and Grover's Algorithm for Quantum Optimization of Robotic Manipulators

Hassen Nigatu·Shi Gaokun·Li Jituo·Wang Jin·Lu Guodong·Howard Li
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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.

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Hassen Nigatu, Shi Gaokun, Li Jituo, Wang Jin, Lu Guodong, Howard Li. 2025-09-08. Quantum Machine Learning and Grover's Algorithm for Quantum Optimization of Robotic Manipulators. https://doi.org/10.1109/lra.2025.3627071

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