This study explores how quantum computing can reshape the intelligence, adaptability, and learning capacity of humanoid robotics. It examines how quantum principles such as superposition and entanglement allow robots to process and evaluate information in parallel, leading to faster, more flexible responses than those built on classical computing. The paper connects ideas from quantum machine learning (QML), quantum optimization, and quantum reinforcement learning (QRL) to practical scenarios in humanoid robotics, where rapid reasoning and context awareness are essential. Within a hybrid quantum-classical framework, the study outlines how these methods can enhance robotic perception, decision-making, and natural-language interaction, making cognitive robotics more adaptive in complex domains such as healthcare, manufacturing, and disaster response. Rather than presenting a full solution, this work defines a pathway for integrating quantum algorithms into real robotic architectures. The results indicate that combining quantum computing with humanoid robotics through hybrid quantum-classical systems could lead to a new stage of robotic intelligence machines able to handle uncertainty, learn continuously, and reason in ways that reflect deeper, more human-like awareness.
This study explores how quantum computing can reshape the intelligence, adaptability, and learning capacity of humanoid robotics. It examines how quantum principles such as superposition and entanglement allow robots to process and evaluate information in parallel, leading to faster, more flexible responses than those built on classical computing. The paper connects ideas from quantum machine learning (QML), quantum optimization, and quantum reinforcement learning (QRL) to practical scenarios in humanoid robotics, where rapid reasoning and context awareness are essential. Within a hybrid quantum-classical framework, the study outlines how these methods can enhance robotic perception, decision-making, and natural-language interaction, making cognitive robotics more adaptive in complex domains such as healthcare, manufacturing, and disaster response. Rather than presenting a full solution, this work defines a pathway for integrating quantum algorithms into real robotic architectures. The results indicate that combining quantum computing with humanoid robotics through hybrid quantum-classical systems could lead to a new stage of robotic intelligence machines able to handle uncertainty, learn continuously, and reason in ways that reflect deeper, more human-like awareness.
Today's robots are controlled with classical computers that make decisions based on algorithms designed for a classical computing architecture.However, the amount of data increases continuously -a fact which pushes classical computers that control these robots to their limits.In comparison to classical computing, fundamentally different concepts of physics are the basis of quantum computing and thereby quantum computing has the potential to solve specific computational problems more efficiently.In addition to the aforementioned complications, the subgroup of robots that are subject to the research of this paper, namely humanoid robots, not only need more computational capacity but also require an algorithmic design based on a behavioral model which avoids causing antipathy when interacting with human beings.Using a real quantum computer with quantum phenomena such as superposition, entanglement, and quantum parallelism, new behavioral models and algorithms can be designed to improve human robot interaction and speed up machine learning processes.This paper promotes a hybrid-controlled robot using the combination of a classical computer-and quantum computer.With four quantum circuits to define a behavioral model and three different quantum machine learning algorithms we show the advantage of the hybrid-controlled robot.
Abstract: This research explores the emerging paradigm of Quantum Intelligence (QI), which unifies artificial intelligence and quantum computing to enable next-generation cognitive systems. By leveraging quantum principles such as superposition, entanglement, and quantum tunneling, QI frameworks transcend classical AI limitations in learning efficiency, contextual awareness, and problem-solving complexity. The study implements a hybrid architecture integrating variational quantum circuits with reinforcement learning agents, tested across multi-goal environments. Experimental results reveal faster convergence, greater entropy reduction, and enhanced generalization compared to classical deep Q-networks. These findings demonstrate the transformative potential of QI to replicate and surpass human-like cognition, offering scalable, efficient, and adaptable models for future autonomous reasoning systems. Keywords: Quantum intelligence, Artificial intelligence, Quantum computing, Hybrid AI systems, Reinforcement learning, Variational quantum circuits, Cognitive systems, Quantum-enhanced learning, Entropy optimization, Next-generation AI
The convergence of quantum computing and robotics represents a paradigm shift in autonomous systems design, promising to overcome fundamental computational limitations that have constrained classical robotic capabilities. This comprehensive review surveys the rapidly evolving field of quantum machine learning (QML) for robotics, systematically analyzing 107 peer-reviewed contributions spanning quantum algorithms, machine learning techniques, and their robotic applications. We provide a detailed historical perspective on the field's development, from the early theoretical foundations established by Grover's search algorithm and quantum reinforcement learning proposals to current experimental implementations on noisy intermediate-scale quantum (NISQ) devices. The review systematically examines five major application domains: (1) quantum reinforcement learning for navigation and control, (2) quantum search and path planning algorithms, (3) quantum-enhanced localization and mapping, (4) quantum optimization for inverse kinematics and task allocation, and (5) multi-robot coordination and swarm intelligence. For each domain, we present detailed algorithmic formulations, computational complexity analyses, and critical assessments of experimental results. Our analysis reveals that while provable quantum advantage in robotics remains largely prospective, recent demonstrations show promising results: quantum deep reinforcement learning achieving 40\% parameter reduction in navigation tasks, Grover-based kinematic optimization demonstrating up to 93x speedups, and quantum annealing providing practical solutions for multi-robot routing. We identify key technical challenges, including barren plateaus in variational circuits, noise-induced performance degradation, and the quantum-classical integration bottleneck. Finally, we outline a strategic roadmap for quantum robotics research, distinguishing near-term opportunities in hybrid algorithms from long-term goals requiring fault-tolerant quantum computation. This paper serves as both an authoritative technical reference and a comprehensive guide for researchers working at the intersection of quantum computing and autonomous systems.
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.
This paper explores the application of Quantum-Enhanced Reinforcement Learning (QRL) to robotics, aiming to accelerate learning speeds and enable more intelligent decision-making in complex environments. Traditional reinforcement learning algorithms often struggle with scalability and convergence, particularly when dealing with high-dimensional state spaces and complex environments. Quantum computing offers the potential to overcome these limitations by leveraging quantum mechanics to perform computations that are intractable for classical computers. We propose a novel QRL framework that utilizes quantum-enhanced reinforcement learning to improve exploration, adaptation, and overall performance. The core mechanism involves employing quantum circuits to represent and manipulate the state space, facilitating more efficient learning and potentially uncovering novel strategies. This research investigates the feasibility and potential impact of QRL on robotic control, focusing on scenarios requiring rapid adaptation and robust decision-making in dynamic environments. We present preliminary results demonstrating the efficacy of our approach in a simulated robotic task involving navigation and obstacle avoidance.
The convergence of quantum computing and robotics represents a paradigm shift in autonomous systems design, promising to overcome fundamental computational limitations that have constrained classical robotic capabilities. This comprehensive review surveys the rapidly evolving field of quantum machine learning (QML) for robotics, systematically analyzing 107 peer-reviewed contributions spanning quantum algorithms, machine learning techniques, and their robotic applications. We provide a detailed historical perspective on the field's development, from the early theoretical foundations established by Grover's search algorithm and quantum reinforcement learning proposals to current experimental implementations on noisy intermediate-scale quantum (NISQ) devices. The review systematically examines five major application domains: (1) quantum reinforcement learning for navigation and control, (2) quantum search and path planning algorithms, (3) quantum-enhanced localization and mapping, (4) quantum optimization for inverse kinematics and task allocation, and (5) multi-robot coordination and swarm intelligence. For each domain, we present detailed algorithmic formulations, computational complexity analyses, and critical assessments of experimental results. Our analysis reveals that while provable quantum advantage in robotics remains largely prospective, recent demonstrations show promising results: quantum deep reinforcement learning achieving 40\% parameter reduction in navigation tasks, Grover-based kinematic optimization demonstrating up to 93x speedups, and quantum annealing providing practical solutions for multi-robot routing. We identify key technical challenges including barren plateaus in variational circuits, noise-induced performance degradation, and the quantum-classical integration bottleneck. Finally, we outline a strategic roadmap for quantum robotics research, distinguishing near-term opportunities in hybrid algorithms from long-term goals requiring fault-tolerant quantum computation. This paper serves as both an authoritative technical reference and a comprehensive guide for researchers working at the intersection of quantum computing and autonomous systems.