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.
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.
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.
Quantum computing is a high-powered computational model that possess the capability to solve problems that are non-detectable by traditional algorithms. These algorithms primarily exploit well-known rules and principles of quantum mechanics i.e., superposition and entanglement to implement and deploy solutions for sophisticated tasks. Its applications have revolutionized various domains like optimization, cryptography, machine learning, and simulation. With the increasing research in this significant computational field, it is becoming more essential to critically assess that how quantum algorithms are being employed in real-world problem-solving contexts. The comprehensive literature review that has been conducted in this study is primarily based upon the critical assessment of four key quantum algorithms, which are 1) Shor's Algorithm, 2) Grover's Algorithm, 3) the Quantum Approximate Optimization Algorithm (QAOA), and the 4) Quantum Fourier Transform (QFT). We have primarily focused upon their uses in secure communication, machine learning, chemistry, cryptography, and optimization. 30 excellent studies published between 2015 and 2024 were found through a systematic search of IEEE Xplore, SpringerLink, ScienceDirect, arXiv, and Google Scholar. Our results identify major patterns in the usage of algorithms like Grover's, Shor's, QAOA, and VQE and elucidate their applications in tackling theoretical and pragmatic problems. Unlike previous reviews that concentrate narrowly on algorithm design or on particular areas, this SLR presents a wide but organized synthesis that highlights problem-driven applications. Moreover, we have also highlighted the primary research gaps in this area and also suggested possible future directions for further investigation. This review provides a basis for researchers who would like to apply quantum algorithms to new or interdisciplinary problems.
Transport research has significant potential to benefit from quantum computing. The rise of intelligent transport systems, autonomous vehicles, and the Internet of Things has created an unprecedented demand for efficient information processing and computational optimisation. Accordingly, transport engineers and scientists have explored the ever-improving capabilities of quantum computers in an effort to meet this demand. Motivated by this growing interest, this paper sets out three aims: (1) to introduce the fundamental aspects of quantum computing relevant to the transport domain; (2) to develop a pipeline for identifying transport-related problems which are suitable for quantum advantage, solving them, and assessing their performance fairly; (3) to provide a systematic review of the existing literature and a perspective. For the latter, a systematic search of the Scopus and IEEE Xplore databases, supplemented by additional citation sources, identified 91 studies for inclusion following PRISMA 2020 guidelines. While a diverse set of use cases have been proposed, we conclude that future research should prioritise problems where quantum computation offers a clearer practical benefit. To this end, we suggest promising directions to guide further work in this burgeoning subfield.
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.
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.