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

doi:10.48550/arxiv.2509.07216

Papers

qcr:2606.53578.1

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

arXiv

Hassen Nigatu, Shi Gaokun, Li Jituo, +3 more

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.

10.48550/arxiv.2509.07216

Published 2025

Uploaded 3 days ago

3

Views

View Publication

Citing this entry? Use this QCR ID

Uploaded by

QCR Librarian

Join the Discussion

Comments (0)

No comments yet. Be the first to share your thoughts!

Indexed by QCR Librarian

This entry was created automatically from publicly available records. QCR links to public sources and only stores repository content where the license permits redistribution.

Claim this entry →