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International Conference on Artificial Intelligence, Computer, Data Sciences and Applications (ACDSA)

Exploring Quantum-Inspired Control Strategies for Robotic Manipulation: A Comparative Analysis

Mohamed Salah Dahassa, Mehdi Fazilat, Nadjet Zioui

Published Feb 5, 2026• 0 citations
Publisher

Abstract

This study evaluates quantum-inspired control strategies on classical hardware, specifically Quantum Sliding Mode Control (QSMC), Quantum Genetic Algorithm (QGA), and a Grover-based quantum search controller, to improve control of a six-degree-of-freedom robotic arm with nonlinear dynamics. Building upon quantum-analogous probabilistic principles, these methods offer significant potential across industries. To clarify the basis of these approaches, the analysis centers on quantum-computation concepts, such as probabilistic amplitude encoding and superposition, which can enhance robustness, trajectory smoothness, and energy efficiency in classical control systems. Consistent, noise-free simulation conditions and multiple independent trials ensured fairness and reproducibility throughout the study. Performance assessment relied on Integrated Absolute Error (IAE), which measures total error over time, and Average Control Energy ($\mathrm{E}_{\text{av}} \mathrm{g}$) for mean energy consumption. Compared with classical Sliding Mode Control (SMC), QSMC achieved an 11 % faster transient response. Additionally, QGA delivered 21 % lower IAE and 12.3 % lower average control energy, while the Grover-based controller provided balanced performance among convergence speed, tracking accuracy, and energy efficiency. Overall, these results suggest that quantum-inspired approaches can significantly advance trajectory precision, robustness, and energy efficiency, supporting the development of nextgeneration intelligent robotics.

DOI
10.1109/acdsa67686.2026.11468181

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This paper introduces a novel theoretical framework that reformulates optimal control as a quantum search problem using Grover’s algorithm, leveraging its quadratic speedup. Specifically, the method encodes all possible control inputs into a quantum superposition state and uses a reference value interpreted as a candidate minimum to evaluate which inputs yield a lower control cost. To guide the search, we integrate a quantum comparator circuit to identify the inputs below this reference, and quantum counting to estimate their number. The reference is iteratively updated using a sigmoid-based rule until only one input satisfies the condition, thereby ensuring convergence to the global minimum within the discretized control space. Although full quantum implementation is currently infeasible due to oracle complexity and hardware limitations, we simulate the process using a classical controller as a pseudo-oracle to illustrate the algorithmic structure. This work does not aim to demonstrate performance gains but rather to establish a foundational method for embedding control synthesis within Grover-based quantum circuits. The framework paves the way for scalable quantum control systems once hardware resources permit full realization.
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This research explores the application of quantum-inspired neural networks (QNNs) to address the inverse kinematics problem in robotic arms, explicitly focusing on the ABB IRB140, an articulated robot arm with six degrees of freedom. The primary objective is to develop a quantum-inspired activation function for multilayer perceptron (MLP) neural networks. The study evaluates their performance in avoiding singularities by comparing artificial neural networks (ANNs) with quantum neural networks (QNNs). The findings demonstrate that QNNs outperform ANNs in terms of mean absolute error (MAE), achieving a 15.60% lower MAE in the model without singularities and a 16.67% reduction in the Jacobian-based MAE in the model designed to avoid singularities. The study demonstrates that QNNs offer superior accuracy in predicting the robot arm's inverse kinematics, achieving a position error of 1.64 mm and an orientation error of 0.00179 radians while effectively avoiding singularities. These outcomes underscore the potential of quantum-inspired neural networks to enhance robotic arm manipulations' precision, efficiency, and performance.