Extending the ideas of quantum Braitenberg vehicles from (A. Raghuvanshi, 2006), we present here a family of Lego robots controlled by multiple-valued quantum circuits. The robots have at most 6 degrees of freedom (motors) and 6 sensors. Their basic architecture is a generalization of robots from (Ch. Brawn et al, 2006) to more versatile multiple-valued quantum automata (simulated in software). We believe that building robots with "quantum brains" is an excellent future application of quantum computing and now it helps students to learn principles of quantum circuits. We present a one-year project in "quantum robotics for teenagers". Our project brings research and educational perspectives, which are both presented in this paper.
Extending the ideas of quantum Braitenberg vehicles from (A. Raghuvanshi, 2006), we present here a family of Lego robots controlled by multiple-valued quantum circuits. The robots have at most 6 degrees of freedom (motors) and 6 sensors. Their basic architecture is a generalization of robots from (Ch. Brawn et al, 2006) to more versatile multiple-valued quantum automata (simulated in software). We believe that building robots with "quantum brains" is an excellent future application of quantum computing and now it helps students to learn principles of quantum circuits. We present a one-year project in "quantum robotics for teenagers". Our project brings research and educational perspectives, which are both presented in this paper.
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.
This dissertation introduces a new type of quantum automata, their encoding and circuit realization. I concentrate on possible applications in robotics. Several methods and application of quantum automata and quantum circuit-based controllers for elementary robotic systems, with a focus on humanoid robot motion, emotion, and behavior generation are illustrated in detail. The research introduces several novel methodologies that bridge quantum computing principles with robotic control, aiming to overcome the limitations of classical deterministic and probabilistic approaches. The dissertation first presents a quantum-circuit-based framework for generating non-repetitive and expressive (e)motions in a humanoid robot actor, using superposition and entanglement to produce families of synchronized yet probabilistic behaviors. It then introduces a flowchart-based synthesis method for quantum automata using one-hot encoding, which significantly reduces quantum circuit cost by leveraging EXOR-based logic and restricting gate sets to NOT, CNOT, and Toffoli gates. Finally, the work expands into ternary quantum logic, developing hybrid quantum finite automata (QFA) using Chrestenson-family gates and uniformization ternary quantum gates, enabling more efficient pattern and language recognition with smaller state spaces. The key contributions of this dissertation are multifold. First, it presents the first practical implementation of a quantum-controlled humanoid robot for theatrical performance, demonstrating visually distinct, expressive, and entertaining behaviors that extend beyond the capabilities of classical control systems. Second, it introduces a direct synthesis methodology for quantum sequential circuits derived from high-level flowcharts, achieving up to a 95% reduction in quantum cost compared to existing design approaches. Third, the dissertation develops the generalized hybrid quantum finite automata (GHQFA), integrating ternary quantum logic with classical automata to enable complex pattern recognition and robotic gesture interpretation while requiring minimal quantum cost. Although the illustrative example of a robot car simulated in the CoppeliaSim environment is very simple based only on Chrestenson gates, the integration of all methods proposed herein establishes a foundation for controlling highly expressive humanoid robotic systems. Furthermore, the proposed models can be applied to real-world robotic tasks, including gesture recognition, motion synthesis, and adaptive behavior generation, with validation through both physical robotic performances and simulation studies. Finally, this work introduces an r-of-n encoding method that can enable to encode large machines for quantum circuits and can be integrated with the entanglement and superposition methods presented in this dissertation. This dissertation lays the groundwork for scalable and practical quantum-robotic systems, offering a pathway toward more intelligent, adaptive, and engaging robotic agents through quantum-enhanced control architectures.
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.
A brand-new paradigm of robots–quantum robots–is proposed through the fusion of quantum theory with robot technology. A quantum robot is essentially a complex quantum system which generally consists of three fundamental components: multi-quantum computing units (MQCU), quantum controller/actuator, and information acquisition units. Corresponding to the system structure, several learning control algorithms, including quantum searching algorithms and quantum reinforcement learning algorithms, are presented for quantum robots. The theoretical results show that quantum robots using quantum searching algorithms can reduce the complexity of the search problem from O( N2) in classical robots to O( NN) . Simulation results demonstrate that quantum robots are also superior to classical robots in efficient learning under novel quantum reinforcement learning algorithms. Considering the advantages of quantum robots, some important potential applications are also analyzed and prospected.
This book chapter explores the emerging field of quantum robotics and its potential to revolutionize the design and functionality of intelligent and adaptive robotic systems. It discusses the fundamental principles of quantum mechanics and their application to robotics, highlighting the advantages that quantum technologies offer in terms of computational power, information processing, and communication. The chapter presents various approaches to incorporating quantum elements into robotics, such as quantum sensors, quantum control systems, and quantum-inspired algorithms. Furthermore, it examines the challenges and opportunities in the development of quantum robotics, including hardware constraints, algorithm design, and the integration of classical and quantum components. Overall, this chapter provides an overview of the state-of-the-art in quantum robotics and identifies future directions for research and development.