The Applications of Quantum Computing in the Growing Field of Robotics

Haricharan Yarlagadda

Rob Bogue

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.

Ci‐Jyun Liang, Logan Norton-Lapsley, Zhaofeng Hu

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.

Fei Yan, Abdullah M. Iliyasu, Nianqiao Li +2 more

Anandakumar Haldorai

Quantum computing is an advanced computing area that utilizes the principles of quantum mechanics to do certain operations at much faster rates compared to traditional computers. Quantum bits, or qubits, have the ability to exist in multiple states simultaneously, unlike traditional bits, which have a state of 0 or 1. This unique property was created by a process known as superposition. This article reviews the various quantum computing applications within the field of robotics. It further discusses the principles of quantum computing such as superposition and qubits, and puts more focus on exponential processing capacity of it. Various quantum algorithms are reviewed in comparison to traditional methods used on completing machine learning tasks and handling robotics. In addition, this paper reviews potential applications of quantum computing within the field of artificial intelligence, data mining, and image process. Lastly, the paper highlights the necessity of effectively integrating robotics with quantum computing, considering application-based protocols, scale-up capacity, and hardware-free algorithms.

Daniel Udekwe