Using the effects of quantum mechanics for computing challenges has been an often discussed topic for decades. The frequent successes and early products in this area, which we have seen in recent years, indicate that we are currently entering a new era of computing. This paradigm shift will also impact the work of robotic scientists and the applications of robotics. New possibilities as well as new approaches to known problems will enable the creation of even more powerful and intelligent robots that make use of quantum computing cloud services or co-processors. In this position paper, we discuss potential application areas and also point out open research topics in quantum computing for robotics. We go into detail on the impact of quantum computing in artificial intelligence and machine learning, sensing and perception, kinematics as well as system diagnosis. For each topic we point out where quantum computing could be applied based on results from current research.
Using the effects of quantum mechanics for computing challenges has been an often discussed topic for decades. The frequent successes and early products in this area, which we have seen in recent years, indicate that we are currently entering a new era of computing. This paradigm shift will also impact the work of robotic scientists and the applications of robotics. New possibilities as well as new approaches to known problems will enable the creation of even more powerful and intelligent robots that make use of quantum computing cloud services or co-processors. In this position paper, we discuss potential application areas and also point out open research topics in quantum computing for robotics. We go into detail on the impact of quantum computing in artificial intelligence and machine learning, sensing and perception, kinematics as well as system diagnosis. For each topic we point out where quantum computing could be applied based on results from current research.
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.
Over the last 65 years, Artificial Intelligence has successfully transformed robots from simple machines into intelligent autonomous agents. However, as robotics enters high-stakes fields like "Quick Commerce" and complex engineering, classical computing relying on binary bits is hitting a massive "computational ceiling." These traditional systems are simply too slow to process the colossal, high-dimensional data required for instant decision-making. This paper reviews the evolution of AI and identifies the specific hardware bottlenecks limiting modern autonomy. To bridge this gap, we propose "Quantum Power" leveraging qubits, superposition, and entanglement as the essential engine for the next generation of robotics. By synthesizing the interplay between AI and Quantum Computing, this research demonstrates how a hybrid approach can overcome current logistical barriers, moving us toward a future of faster, smarter, and truly symbiotic quantum-powered robots. Despite its promising advantages, the implementation of quantum-AI integration faces several challenges, including limited availability of quantum hardware, noise sensitivity, and the need for hybrid quantum-classical frameworks. The study highlights ongoing research efforts and future directions aimed at overcoming these limitations. Overall, the fusion of quantum computing and AI offers a novel and transformative approach to developing intelligent robotic systems with superior learning and decision-making capabilities.
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.
Over the last 65 years, Artificial Intelligence has successfully transformed robots from simple machines into intelligent autonomous agents. However, as robotics enters high-stakes fields like "Quick Commerce" and complex engineering, classical computing relying on binary bits is hitting a massive "computational ceiling." These traditional systems are simply too slow to process the colossal, high-dimensional data required for instant decision-making. This paper reviews the evolution of AI and identifies the specific hardware bottlenecks limiting modern autonomy. To bridge this gap, we propose "Quantum Power" leveraging qubits, superposition, and entanglement as the essential engine for the next generation of robotics. By synthesizing the interplay between AI and Quantum Computing, this research demonstrates how a hybrid approach can overcome current logistical barriers, moving us toward a future of faster, smarter, and truly symbiotic quantum-powered robots. Despite its promising advantages, the implementation of quantum-AI integration faces several challenges, including limited availability of quantum hardware, noise sensitivity, and the need for hybrid quantum-classical frameworks. The study highlights ongoing research efforts and future directions aimed at overcoming these limitations. Overall, the fusion of quantum computing and AI offers a novel and transformative approach to developing intelligent robotic systems with superior learning and decision-making capabilities.
Purpose The purpose of this paper is to provide details of developments in quantum technologies and consider their potential applications in robotics. Design/methodology/approach Following a short introduction, this study first provides an overview of the global quantum technology landscape. It then discusses developments in quantum computing and sensing technologies. Potential applications in robotics are then considered and finally, brief conclusions are drawn. Findings Quantum technologies are the topic of a rapidly growing global R&D effort. Quantum computing has the potential to conduct conventional computations far more rapidly than traditional computers and solve complex problems that are presently challenging or impossible. If realised, robotic applications could include enhanced route planning, machine learning and data fusion. Quantum position and magnetic field sensors have the potential to revolutionise navigation systems in airborne, land and marine robots and overcome limitations of GPS and inertial measurement units. Magnetic sensors also have a role in health care in the control of robotic prostheses and exoskeletons and in brain–computer interface techniques. Quantum radar, lidar and imaging systems stand to outperform their conventional counterparts, and applications are anticipated in military and civilian robots. Quantum technologies are still at an early stage of development, and much progress will be made in the future, opening up many further robotic applications. Originality/value This study provides an insight into quantum technology developments and their potential applications in robotics.
Quantum computing and artificial intelligence (AI) are two of the most transformative technologies of the 21st century. The convergence of these fields promises unprecedented advancements in computational capabilities, enabling solutions to complex problems that are currently intractable for classical computers. This paper explores the intersection of quantum computing and AI, examining the potential impacts, current research, challenges, and future directions. By leveraging quantum phenomena such as superposition, entanglement, and quantum parallelism, quantum computing can enhance AI algorithms, offering exponential speedups and new methodologies for machine learning, optimization, and data analysis.