Quantum Machine Learning and Grover's Algorithm for Quantum Optimization of Robotic Manipulators
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915584548012032 |
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| author | Nigatu, Hassen Gaokun, Shi Jituo, Li Jin, Wang Guodong, Lu Li, Howard |
| author_facet | Nigatu, Hassen Gaokun, Shi Jituo, Li Jin, Wang Guodong, Lu Li, Howard |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_07216 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Quantum Machine Learning and Grover's Algorithm for Quantum Optimization of Robotic Manipulators Nigatu, Hassen Gaokun, Shi Jituo, Li Jin, Wang Guodong, Lu Li, Howard Robotics 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. |
| title | Quantum Machine Learning and Grover's Algorithm for Quantum Optimization of Robotic Manipulators |
| topic | Robotics |
| url | https://arxiv.org/abs/2509.07216 |