Modern robotic systems are solving increasingly complex tasks, imposing higher demands on the speed and efficiency of onboard computing systems. Traditional methods of increasing performance (scaling hardware, parallel computing, etc.) are approaching their limits, necessitating the search for fundamentally new approaches. Quantum computing is considered a promising direction that could significantly surpass classical computational capabilities in certain tasks. In this regard, the goal of this study is to explore the applicability of quantum computing for onboard computing systems in robotic complexes (RCs). To achieve this goal, a comprehensive analysis of the requirements (performance, energy consumption, size and weight constraints, reliability, etc.) for onboard computing systems of RCs has been conducted. The potential of quantum algorithms in solving typical robotic tasks, including optimization problems and machine learning, has been assessed, followed by simulation modeling and comparison with classical methods. Additionally, current limitations of modern quantum computers (e.g., limited qubit count and decoherence issues) have been examined, and a forecast has been made regarding their development in the coming years based on technological trends. The study confirms the promising application of quantum computing for solving optimization and machine learning problems, which are critical for intelligent RCs. However, current technological limitations (size, operational conditions, and instability of quantum processors) do not yet allow for their direct use onboard. Nevertheless, directions for further research have been proposed, and possible scenarios for the gradual integration of quantum computing into RC architectures over the next 5–15 years have been considered, particularly as quantum processors become more compact and methods for integrating them into onboard systems improve. Thus, as existing barriers are overcome, quantum computers may eventually become an integral part of onboard control systems for RCs, providing a significant leap in their performance.
Modern robotic systems are solving increasingly complex tasks, imposing higher demands on the speed and efficiency of onboard computing systems. Traditional methods of increasing performance (scaling hardware, parallel computing, etc.) are approaching their limits, necessitating the search for fundamentally new approaches. Quantum computing is considered a promising direction that could significantly surpass classical computational capabilities in certain tasks. In this regard, the goal of this study is to explore the applicability of quantum computing for onboard computing systems in robotic complexes (RCs). To achieve this goal, a comprehensive analysis of the requirements (performance, energy consumption, size and weight constraints, reliability, etc.) for onboard computing systems of RCs has been conducted. The potential of quantum algorithms in solving typical robotic tasks, including optimization problems and machine learning, has been assessed, followed by simulation modeling and comparison with classical methods. Additionally, current limitations of modern quantum computers (e.g., limited qubit count and decoherence issues) have been examined, and a forecast has been made regarding their development in the coming years based on technological trends. The study confirms the promising application of quantum computing for solving optimization and machine learning problems, which are critical for intelligent RCs. However, current technological limitations (size, operational conditions, and instability of quantum processors) do not yet allow for their direct use onboard. Nevertheless, directions for further research have been proposed, and possible scenarios for the gradual integration of quantum computing into RC architectures over the next 5–15 years have been considered, particularly as quantum processors become more compact and methods for integrating them into onboard systems improve. Thus, as existing barriers are overcome, quantum computers may eventually become an integral part of onboard control systems for RCs, providing a significant leap in their performance.
This paper evaluates the prospects for applying quantum computing to the onboard computing systems of robots amid rapidly growing computational demands that strain conventional scaling and parallelism. We outline robot-specific requirements—performance, real-time guarantees, size-weight-power (SWaP), and reliability—and survey quantum algorithms relevant to core workloads, including combinatorial optimization (e.g., Grover, variational methods), selected linear-algebra subroutines (e.g., HHL), spectral transforms, and elements of quantum machine learning. A modeling frame-work is described for benchmarking these methods against classical baselines and for integrating quantum simulation into existing toolchains.Results indicate promising acceleration for search/optimization and certain perception and estimation subroutines, while end-to-end gains depend critically on state preparation, oracle construction, error rates, and measurement overhead. Given current hardware and SWaP constraints, immediate onboard deployment is unrealistic; instead, we propose a staged path: near-term offboard/cloud assistance for pre- and post-processing, medium-term hybrid classical–quantum pipelines, and longer-term integration of compact quantum coprocessors as devices mature. The paper concludes with practical guidance on workload selection and evaluation criteria for robots.
Quantum computing is poised to revolutionize the future of advanced computational systems, offering unprecedented speed and efficiency for solving complex problems that are beyond the capabilities of classical computers. Traditional computational models, based on bits representing either a 0 or 1, are approaching their physical and performance limits. Quantum computers, however, leverage the principles of quantum mechanics—specifically, superposition and entanglement—to operate on quantum bits, or qubits, which can exist in multiple states simultaneously. This capability allows quantum systems to process vast amounts of data in parallel, potentially solving problems in seconds that would take classical systems millions of years. As the demands on computational systems grow in fields like cryptography, artificial intelligence (AI), drug discovery, and materials science, quantum computing offers solutions that can overcome the bottlenecks faced by classical computing architectures. This paper explores the foundational principles of quantum computing, its advantages over classical systems, and its potential applications in various industries. Additionally, we analyze the current challenges that quantum computing faces, such as error rates, quantum decoherence, and hardware scalability. We also consider the future trajectory of quantum computing, including the potential for quantum supremacy and the development of hybrid quantum-classical systems. Although quantum computing is still in its nascent stages, with practical quantum computers requiring further advancements in qubit coherence and error correction, its future impact on computational systems is undeniable. This paper aims to provide a comprehensive review of how quantum computing is shaping the future of computational systems, detailing the technologies, applications, and challenges ahead. With continuous technological advancements, quantum computing has the potential to redefine global industries, solve previously unsolvable problems, and fundamentally alter our understanding of computation in the coming decades.
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.
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.
This textbook provides a comprehensive foundation for the emerging interdisciplinary field of quantum robotics. It systematically addresses the core question of how quantum computational principles can be formulated to address fundamental challenges in robotics: planning, optimization, perception, and control. The work is structured to build a rigorous bridge between the abstract mathematics of quantum information and the concrete computational problems of autonomous systems. The first part establishes the necessary foundations, introducing the computational models of quantum circuits alongside the classical formulations of robotic kinematics, dynamics, and state estimation. The second part is dedicated to quantum algorithmic primitives, with detailed exposition on quantum search, quantum linear algebra, and quantum optimization algorithms. Each algorithmic chapter explicitly maps these primitives to robotic problem classes, such as motion planning, sensor data processing, and trajectory optimization, discussing both potential advantages and current practical constraints. The third part focuses on system integration and forward-looking perspectives. It explores quantum machine learning models for robotic learning, outlines hybrid quantum-classical computer architectures required for real-world applications, and rigorously examines the ethical and societal implications of increasingly capable quantum-optimized autonomous systems. The textbook concludes with practical appendices detailing software tools, benchmark problems, and problem sets. Designed for graduate students and researchers in robotics, computer science, and quantum engineering, this book emphasizes clear problem formulation, realistic resource analysis, and verifiable claims. Its goal is to equip the next generation of scientists and engineers with the principles and critical perspective needed to advance the field beyond theoretical potential into practical, validated technology.
ABSTRACT Quantum computing represents a fundamental shift in the way computational problems are approached and solved. Unlike classical computing systems that rely on binary bits to process information, quantum computing utilizes quantum bits, or qubits, which exploit principles of quantum mechanics such as superposition and entanglement. This transition from classical bits to qubits has opened new possibilities for solving complex problems that are computationally infeasible for traditional systems. This paper explores the evolution of computing paradigms from classical architectures to quantum-based models, highlighting the theoretical foundations and technological advancements that have driven this transformation. It discusses the core concepts of quantum computing, including qubit representation, quantum gates, and computational models, along with an overview of current quantum hardware architectures. Furthermore, the paper examines key application areas such as cryptography, optimization, artificial intelligence, and scientific simulations, where quantum computing demonstrates significant potential. Despite its promise, quantum computing faces substantial challenges related to hardware stability, error correction, scalability, and resource requirements, which are also analyzed. Finally, the paper outlines future research directions and the potential impact of quantum computing on next-generation information processing systems, emphasizing its role in redefining the boundaries of modern computation. Keywords : Quantum Computing, Qubits, Quantum Algorithms, Quantum Applications, Future Computing Technologies