Construction robotics play a crucial role in the architecture, engineering, and construction (AEC) industry, but they face significant computational challenges due to dynamic environments, multi-agent coordination, and largescale combinatorial optimization problems.Quantum computing (QC) offers potential advantages for large-scale search and combinatorial tasks, but its relevance to construction robotics remains unclear.This paper presents a scoping review of QC in robotics.We first collect literature using keyword search from Scopus and Web of Science databases, and then screen through data and include related literature following PRISMA-ScR (Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews).We analyze the 96 included studies using bibliometric and thematic analyses to identify research trends and challenges for construction robotics.Results show growing interest in QC robotics since 2020, with dominant applications in path planning, kinematics and control, multi-robot coordination, and decision-making.We identify key barriers to applying QC in construction robotics, including current hardware limitations, simulation-to-real gaps, stringent safety and explainability requirements, and suggest future research directions, including hybrid quantum-classical approaches, QC robotics benchmarks, and explainable QC.
Construction robotics play a crucial role in the architecture, engineering, and construction (AEC) industry, but they face significant computational challenges due to dynamic environments, multi-agent coordination, and largescale combinatorial optimization problems.Quantum computing (QC) offers potential advantages for large-scale search and combinatorial tasks, but its relevance to construction robotics remains unclear.This paper presents a scoping review of QC in robotics.We first collect literature using keyword search from Scopus and Web of Science databases, and then screen through data and include related literature following PRISMA-ScR (Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews).We analyze the 96 included studies using bibliometric and thematic analyses to identify research trends and challenges for construction robotics.Results show growing interest in QC robotics since 2020, with dominant applications in path planning, kinematics and control, multi-robot coordination, and decision-making.We identify key barriers to applying QC in construction robotics, including current hardware limitations, simulation-to-real gaps, stringent safety and explainability requirements, and suggest future research directions, including hybrid quantum-classical approaches, QC robotics benchmarks, and explainable QC.
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.
More and more, multi-robot systems (MRS) are being used for hard tasks like industrial automation, surveillance, and autonomous navigation. Nevertheless, these systems have significant computation difficulties especially in path planning, task distribution and coordination. Quantum computing is capable of computing information based on such concepts as superposition and entanglement. This provides an alternative solution to optimization problems that are more efficient than the conventional methods. A systematic review approach based on PRISMA was used. We used pre-set search terms to look for useful papers in databases including Google Scholar, Scopus, IEEE Xplore, and Web of Science. We found peer-reviewed articles that had come out in the last 10 to 15 years based on some rules for which ones to include and which ones to leave out. A total of thirty papers, mainly focusing on algorithms, applications and constraints were included in qualitative synthesis. The review demonstrated that quantum algorithms, such as the search and QAOA algorithms created by Grover, can be helpful in optimizing the problems of MRS. These methods have the benefits of parallelism and speed. Nevertheless, they are used in simulations only due to the limitations of technology, scaling issues, and integration challenges with real-time robotic system.
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.
Digital computing and allied communication tools have been phenomenal, but they are fast reaching their technical limits and also their capacity to solve certain extant complex problems. Quantum computing has now emerged with a touted potential to deal with aspects of the latter complex problems and also significantly transform extant computing and communication methods. Yet, little studies exist in the Architectural, Engineering and Construction (AEC) industry related literature on quantum computing to boost awareness and also explore its prospective impact on the AEC industry. This study discusses the underpinning basic concepts, technologies and trends in the quantum computing domain to build awareness to boost Architecture, Engineering and Construction (AEC) industry capacity on the topic. The research dissects the challenges in this novel area. And also examines the near-term practical opportunities and potential impact of quantum computing on the AEC industry. The study concludes with suggestions for practice and future research.