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

2025-09-08

AI Summary

To address the computational bottleneck of kinematic optimization for high-degree-of-freedom (DoF) robotic manipulators in high-dimensional configuration spaces, this paper proposes the first quantum-native framework integrating quantum machine learning with Grover’s search algorithm. Our method models forward kinematics using a parameterized quantum circuit and embeds it into a Grover oracle, enabling quadratic-speedup search over feasible configurations. Crucially, configuration space is encoded directly in quantum states, eliminating classical discretization and sampling overhead. We validate the framework on 1-DoF, 2-DoF, and bimanual cooperative tasks. Compared to classical optimizers—including Nelder–Mead—the approach achieves up to 93× speedup, with acceleration scaling favorably with increasing DoF. This work establishes a scalable, quantum-computational paradigm for complex robot motion planning, marking a foundational step toward quantum-accelerated robotics.

Technology Category

• Machine Learning: Quantum Machine Learning
• Intelligent Robots: Learning & Optimization for ROB
• Search and Optimization: Learning to Search

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

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 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.

Problem

• Optimizing high-dimensional robotic manipulator configuration spaces
• Solving kinematic optimization with quantum machine learning
• Reducing search complexity using Grover's algorithm

Innovation

• Quantum-native framework integrating machine learning and Grover's algorithm
• Parameterized quantum circuit trained to approximate forward kinematics model
• Grover's oracle enables quadratic reduction in search complexity