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Scientific Reports

Quantum computation for robot posture optimization

Takuya Otani, Atsuo Takanishi, Nobuyuki Hara, Yutaka Takita, Kôichi Kimura

Published Aug 8, 2025• 3 citations
Publisher

Abstract

Quantum computing has gained attention for its potential to surpass classical computing in large-scale computations. In this study, we propose a method for solving the inverse kinematics of a robot using quantum computing. The approach leverages the ability of qubits to represent points on a sphere in three-dimensional space. Forward kinematics calculations are performed using qubits that encode the posture of each robot link, while inverse kinematics solutions are obtained through iterative optimization on a classical computer. Furthermore, we demonstrate that the robot's end-effector position can be effectively represented using a 2-qubit rotation gate, where the root joint angle influences the tip joint angle, resulting in accelerated convergence during inverse kinematics optimization. The proposed method was validated on an actual quantum computer, confirming its feasibility and efficiency. These findings suggest that hybrid quantum-classical approaches can enhance robotic motion planning and optimization in future quantum computing applications.

DOI
10.1038/s41598-025-12109-0

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The integration of quantum computing into robotics requires mathematical representations that preserve the geometric structure and configuration dependence of robot motion. This paper introduces a structure-preserving quantum representation of rigid-body transformations and serial-manipulator kinematics, establishing a correspondence between classical kinematic models and parameterized quantum circuits. Building on the Bloch-sphere representation, spatial directions are encoded into single-qubit states, while quantum rotations reproduce the ordered composition of reference-frame transformations. Translational contributions are combined through quantum-state aggregation, with their metric scales retained classically, enabling the reconstruction of end-effector position and orientation from Pauli expectation values. The formulation preserves the original joint variables and accommodates different kinematic conventions and joint types. Beyond forward kinematics, we establish that the manipulator geometric Jacobian can be reconstructed from quantum-observable derivatives using the parameter-shift rule, with analytical equivalence to its classical counterpart under ideal readout. To demonstrate the applicability of this representation to robot-level algorithms, we introduce Quantum Closed Loop Inverse Kinematics, a hybrid closed-loop method that exploits quantum-derived pose and Jacobian information. The proposed framework provides a geometrically interpretable foundation for developing and investigating quantum and hybrid computational methods for robotic kinematics and motion.
Particle Swarm Optimization (PSO) is a widely used solver for the Inverse Kinematics (IK) problem, offering competitive convergence rates compared to methods like Damped Least Squares and Forward and Backward Reaching Inverse Kinematics. However, PSO’s high computational cost per iteration remains a challenge. While many PSO variants focus on reducing the number of iterations, this work introduces a novel approach to decrease the computational cost per iteration by minimizing direct kinematics calculations. Leveraging quantum computing algorithms, specifically Quantum Amplitude Amplification (QAA), we developed Quantum-Grover PSO (QG-PSO), which identifies a suboptimal particle to guide the swarm. This approach capitalizes on QAA’s quadratic speedup for unstructured search problems. Simulated experiments demonstrate that QG-PSO significantly reduces computational costs compared to classical PSO when solving the IK problem for robotic manipulators with 7 and 15 joints, highlighting the potential of quantum computing in robotics. In addition to position correction, we considered orientation correction while respecting joint limits and avoiding obstacles.
This paper explores the application of quantum computing to enhance constraint propagation within robotics. Conventional constraint propagation algorithms struggle with complex environments, often requiring extensive simulations. We propose a novel quantum-enhanced technique that leverages quantum algorithms to efficiently explore potential trajectories and constraints, resulting in improved motion planning and robustness. The core mechanism involves utilizing quantum annealing to accelerate the search for optimal solutions, leading to more efficient and robust motion planning. The research focuses on the potential of quantum computing to overcome the limitations of existing constraint propagation methods, specifically addressing challenges in dynamic environments and high-dimensional constraints. We present a preliminary analysis demonstrating improvements in planning efficiency and robustness compared to classical methods.
A novel quantum-based kinematic model for computing the position and the orientation of the six-jointed IRB 140 ABB robotic arm is presented. Position is determined through successive elementary translations and rotations from the end effector down to the base frame, whereas orientation is derived from quaternion representation. The model is based on the equivalence between quaternions and Pauli matrices or gates. Based on comparison to experimental results, the quantum kinematic model is valid. Its main advantage is that it uses only one qubit to model both position and orientation, which represents a major saving in terms of computing resources and time. This model could guide development of other quantum models and simulation software to be used in future quantum computers and could also inform classical computing theory for robotic arm kinematics modeling.
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 (QML) 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.
Abstract Quantum computing and quantum information science is a burgeoning engineering field at the cusp of solving challenging robotic applications. This paper introduces a hybrid (gate-based) quantum computing and classical computing architecture to solve the motion propagation problem for a robotic system. This paper presents the quantum-classical architecture for linear differential equations defined by two types of linear operators: unitary and non-unitary system matrices, thereby solving any linear ordinary differential equations. The ability to encode information using bits or qubits—is essential in any computation problem. The results in this paper also introduce two novel approaches to encoding any arbitrary state vector or any arbitrary linear operator using qubits. Unlike other algorithms that solve ordinary differential equations (ODEs) using purely quantum or classical architectures, the ODE solver presented in this paper leverages the best of quantum and classical computing paradigms.