The paper proposes a new method of quantum computing using control and systems theory as well as matrix-quantum computing. The algorithm developed on the basis of the PR-02 robot’s arm’s movement was implemented in using the Neural Network Toolbox. The application of the neural model instead of the analytic model allowed for obtaining the improvement of the trajectory of the PR-02 robot’s arm movement, while the application of the quantum artificial neural network for the assumed number of quasi-parallel computations equal 1000 did not result in the improvement of the model.
The paper proposes a new method of quantum computing using control and systems theory as well as matrix-quantum computing. The algorithm developed on the basis of the PR-02 robot’s arm’s movement was implemented in using the Neural Network Toolbox. The application of the neural model instead of the analytic model allowed for obtaining the improvement of the trajectory of the PR-02 robot’s arm movement, while the application of the quantum artificial neural network for the assumed number of quasi-parallel computations equal 1000 did not result in the improvement of the model.
This research examines the potential of quantum-inspired neural networks (QNNs) for solving the inverse kinematics of robotic arms, focusing on the six-degree-of-freedom ABB IRB140 robot. Traditional inverse kinematics approaches face challenges such as non-unique solutions and computational complexity, especially with increasing degrees of freedom. While artificial neural networks (ANNs) have shown promise, they require further improvements, particularly in terms of quantum computing integration. This study introduces a quantum-inspired activation function to multi-layer perceptron neural networks. We compared ANNs and QNNs with and without singularity avoidance, finding that QNNs significantly outperformed ANNs in mean absolute error (MAE), achieving a 15.60% lower MAE in singularity-free models and a 16.67% lower MAE in singularity-avoidance models. The QNNs demonstrated superior precision, with a position error of 1.64 mm and an orientation error of 0.00179 radians when avoiding singularities. These results highlight the potential of QNNs to enhance the precision, efficiency, and performance of robotic arm manipulation. Quantum computing offers advantages including parallelism, quantum entanglement, and quantum annealing, which contribute to the QNNs’ superior performance. Overall, this study represents a practical contribution to robotics and quantum computing, paving the way for future research into applying quantum principles to neural network models for robotics.
Artificial Neural Networks (ANNs) are powerful tools that can be used to model and investigate various complex and non-linear phenomena. In this study, we construct a new ANN, which is based on Multi-Agent System (MAS) theory and quantum computing algorithm. All nodes in this new ANN are presented as Quantum Computational (QC) agents, and these agents have learning ability. A novel ANN training method was proposed via implementing QCMAS reinforcement learning. This new ANN has powerful parallel-work ability and its training time is shorter than classic algorithm. Experiment results show that this method is effective.
This chapter offers a clear and accessible introduction to the field of quantum-inspired neural computing. This area combines ideas from quantum mechanics with traditional neural network models to create new possibilities in machine learning and optimization metaheuristic algorithms. Unlike actual quantum computing, quantum-inspired approaches work on classical computers but borrow key concepts like superposition, entanglement, and probabilistic behaviour to improve how neural networks learn and process information. The study begins by explaining why researchers are interested in using quantum principles in neural networks. It then introduces the basic mathematical ideas behind these concepts, such as quantum states and transformations, and shows how they can be used to design more powerful and flexible learning systems. It focuses on discussing current models, outlining the distinctions between quantum-inspired and true quantum systems, and evaluating the potential benefits and challenges of this approach. In addition, Quantum-inspired neural computing introduces a novel framework that bridges classical artificial intelligence with principles derived from quantum mechanics. By simulating quantum behaviours on classical hardware, it opens new avenues for developing more adaptive and efficient learning models. This emerging approach offers promising advantages in areas such as optimization, parallel processing, and high-dimensional data representation. Finally, despite its potential, the field faces notable limitations and challenges. These include the lack of limited theoretical formalization, and difficulties in scaling models for complex real-world applications. Moreover, distinguishing genuine quantum-inspired advantages from conservative algorithmic improvements remains an current research apprehension.
The increasing convergence of quantum computing (QC) and advanced robotic control is giving rise to a new class of intelligent automation systems. Utilizing quantum-inspired computational principles, contemporary robotic platforms demonstrate greater optimization efficiency, superior perceptual processing, and improved decision-making performance. This survey outlines the principal quantum-oriented control methodologies utilized in robotics, with particular emphasis on approaches that incorporate Quantum Neural Networks (QNN), Quantum Machine Learning (QML), and Quantum-behaved Particle Swarm Optimization (QPSO). The investigation systematically reviews recent advances in quantum-assisted adaptive control, trajectory planning, and optimization frameworks, spotlighting their contributions to improved robustness, precision, and energy efficiency. By analyzing research in quantum reinforcement learning, distributed Noisy Intermediate-Scale Quantum (NISQ) computing, and hybrid quantum-classical approaches, this review clarifies how quantum principles are transforming both theoretical and practical aspects of robotic control. Likewise, several real-world implementations ranging from industrial manipulators to autonomous and cognitive robotic systems demonstrate the growing feasibility of quantum-enhanced decision-making. The review concludes that while remarkable progress has been made in quantum-inspired control for robotics, most existing studies remain constrained to simulations with restricted experimental validation on physical platforms. Addressing this gap will be essential in future research to transition from theoretical and simulated models toward hardware-level implementations, enabling the realization of scalable, fault-tolerant quantum control architectures.
A quantum computer employing a single quantum as a qubit executes real parallel computation and has various applications. Several algorithms have been proposed for quantum computation. However, these algorithms are applicable only to a limited number of applications. Therefore, a general purpose algorithm should be studied and developed for practical use in the near future. In this paper, we focus on the adiabatic evolution algorithm for general purpose quantum computation and discuss how to use this algorithm for solving an optimization problem. We show a new algorithm incorporating an artificial neural network (ANN)-like method in order to compose another Hamiltonian. The new algorithm is helpful for reducing computation cost and is easy to implement. Successful simulation results are shown.
A future quantum technological infrastructure demands the development of quantum cyber-physical-cognitive systems, merging quantum artificial intelligence, quantum robotics and quantum information and communication technologies. To support such a development, the current work introduces a new interpretation of quantum mechanics, grounded on a link between quantum computer science, systems science and field-based computation. This new interpretation is applied to quantum artificial neural networks, with examples implemented experimentally on IBM's five qubit transmon bowtie chip, accessed via cloud using IBM Q Experience, illustrating how quantum neural computing can be implemented on actual quantum computers. A new form of quantum neural machine learning, based on a quantum optimization of a conditional utility function is also introduced and applied to quantum robotics, where a quantum robot, characterized by an interface and a multilayer quantum artificial neural network, interacts with a quantum target, changing the target's dynamics adaptively, based upon the quantum optimization dynamics, computing the optima for a performance measure and changing the target's dynamics accordingly.