Quantum Machine Learning and Grover�s Algorithm for Quantum Optimization of Robotic Manipulators
Hassen Nigatu Sirag, Gaokun Shi, Jituo Li, Jin Wang, GuoDong Lu, Howard Li
AI summary
Problem
Classical optimization methods face exponential computational bottlenecks when solving inverse kinematics for high-degree-of-freedom robotic manipulators. Existing quantum approaches remain fragmented, lacking a unified, end-to-end quantum-native pipeline.
Approach
A parameterized quantum circuit is trained to approximate forward kinematics and construct a cost oracle, which then guides Grover’s algorithm to efficiently amplify and identify optimal joint configurations within a quantum-native search space.
Key results
- Validated on simulated 1-DoF, 2-DoF, and dual-arm manipulator tasks
- Achieves up to 93x speedup over classical optimizers like Nelder-Mead as dimensionality increases
- Delivers quadratic reduction in search complexity via Grover’s amplitude amplification
- Establishes a unified quantum-native pipeline that eliminates classical-quantum data transfer overhead during computation
Why it matters
Provides a foundational blueprint for leveraging near-term quantum hardware to solve complex robotic kinematic optimization problems that are currently intractable for classical computing.
Abstract
Optimizing high-degree-of-freedom robotic manip- ulators requires searching complex, high-dimensional configu- ration spaces, a task that is computationally challenging for classical methods. This paper introduces a quantum-native framework that integrates Quantum Machine Learning (QML) with Grover’s algorithm to solve kinematic optimization prob- lems efficiently. A parameterized quantum circuit is trained to approximate the forward kinematics model, which then con- structs 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.