用量子算法加速机器人运动规划，提速最高达93倍。
Quantum Machine Learning and Grover's Algorithm for Quantum Optimization of Robotic Manipulators
- 用量子电路拟合机械臂正运动学模型，构建搜索用的量子查找器。
- 结合格罗弗算法，在高维空间中实现搜索复杂度的二次加速。
- 适合对机器人优化速度有极致要求的研究者或量子计算初学者。
高自由度机器人机械臂的运动规划需在复杂的高维配置空间中搜索，传统方法计算成本高昂。本文提出一种原生量子框架，将量子机器学习与格罗弗算法结合，高效求解运动学优化问题。通过参数化量子电路训练以逼近正运动学模型，并据此构造量子查询算子，利用格罗弗算法实现搜索复杂度的平方级降低。在模拟的一自由度、二自由度及双臂机械臂任务中验证，当问题维度增加时，相比经典优化器如Nelder-Mead，速度提升最高达93倍。该工作建立了一个面向机器人运动学优化的原生量子计算基础框架，有效连接了量子计算与机器人学问题。
原文摘要 · Abstract (English)
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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