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.
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.
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.
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 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.
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.
Quantum computing has emerged as a transformative paradigm, leveraging principles of quantum mechanics to tackle computationally intractable problems. This manuscript explores the potential of quantum computing for solving complex engineering problems—specifically optimization, simulation, and data analysis tasks—using technologies and methodologies available up to 2022. We examine both gate-based and quantum annealing approaches, outline experimental frameworks, and compare performance against classical counterparts. Results from simulated benchmark problems demonstrate promising speedups in select cases, though practical adoption is constrained by hardware limitations such as qubit coherence times and noise. The study concludes by highlighting avenues for integrating hybrid quantum-classical workflows in engineering, along with a discussion of current constraints and future directions. Over the past decade, research efforts have focused on understanding how quantum phenomena like superposition and entanglement can be harnessed to realize computational advantages for specific problem classes. In particular, combinatorial optimization problems—ubiquitous in engineering design and logistics—offer fertile ground for quantum algorithms such as the Quantum Approximate Optimization Algorithm (QAOA) and quantum annealing. Additionally, quantum simulation techniques, including the Variational Quantum Eigensolver (VQE), promise to accelerate the modeling of physical systems at the molecular and materials level, potentially informing the design of next-generation structural and functional materials. Beyond raw algorithmic performance, this paper carefully assesses the end‑to‑end workflow, accounting for overheads in problem encoding, error mitigation, and classical post‑processing. We also present a detailed analysis of resource utilization—quantified by qubit counts, circuit depths, and annealing schedules—highlighting trade‑offs between solution quality and execution time. Ultimately, our findings underscore that, while pure quantum speedups remain rare in 2022, hybrid approaches harnessing both quantum and classical resources can yield practical benefits for small‑to‑medium scale engineering tasks. This expanded evaluation provides a roadmap for researchers and practitioners interested in applying quantum computing to real‑world engineering challenges.
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.