Jesse’s research investigates how quantum computing can assist robotic motion planning by improving sampling within discretized configuration spaces. The current project discretizes continuous configuration space into a collision-labeled grid for 3D manipulator motion planning, enabling systematic identification of collision-free and invalid regions during planning. This approach uses grid-based configuration-space search rather than planning directly in continuous space, allowing valid and invalid configurations to be identified in advance and sampled in a more structured manner. Building on prior quantum rapid-exploring random tree concepts, this work studies how quantum amplitude amplification can bias sampling toward useful regions of the search space during motion planning. Prior work in this area suggests that quantum search can be incorporated into sampling-based planners, including variants designed to improve exploration speed and parallel formulations intended to recover multiple useful samples from a shared database. The present research focuses on evaluating whether this quantum-weighted sampling strategy can improve planning efficiency in practice. Experiments are conducted using both simulated quantum hardware and real quantum hardware to compare idealized and device-limited behavior in the sampling process. The work began with a controlled 2D benchmark and is, at the time of writing, being extended toward higher-dimensional robotic manipulators. An additional research direction examines whether database-construction strategies inspired by annealing can improve exploration by promoting broader search early in planning and denser refinement later in the process.
Jesse’s research investigates how quantum computing can assist robotic motion planning by improving sampling within discretized configuration spaces. The current project discretizes continuous configuration space into a collision-labeled grid for 3D manipulator motion planning, enabling systematic identification of collision-free and invalid regions during planning. This approach uses grid-based configuration-space search rather than planning directly in continuous space, allowing valid and invalid configurations to be identified in advance and sampled in a more structured manner. Building on prior quantum rapid-exploring random tree concepts, this work studies how quantum amplitude amplification can bias sampling toward useful regions of the search space during motion planning. Prior work in this area suggests that quantum search can be incorporated into sampling-based planners, including variants designed to improve exploration speed and parallel formulations intended to recover multiple useful samples from a shared database. The present research focuses on evaluating whether this quantum-weighted sampling strategy can improve planning efficiency in practice. Experiments are conducted using both simulated quantum hardware and real quantum hardware to compare idealized and device-limited behavior in the sampling process. The work began with a controlled 2D benchmark and is, at the time of writing, being extended toward higher-dimensional robotic manipulators. An additional research direction examines whether database-construction strategies inspired by annealing can improve exploration by promoting broader search early in planning and denser refinement later in the process.
This paper explores the application of quantum computing to enhance constraint propagation within robotics. Conventional constraint propagation algorithms struggle with complex environments, often requiring extensive simulations. We propose a novel quantum-enhanced technique that leverages quantum algorithms to efficiently explore potential trajectories and constraints, resulting in improved motion planning and robustness. The core mechanism involves utilizing quantum annealing to accelerate the search for optimal solutions, leading to more efficient and robust motion planning. The research focuses on the potential of quantum computing to overcome the limitations of existing constraint propagation methods, specifically addressing challenges in dynamic environments and high-dimensional constraints. We present a preliminary analysis demonstrating improvements in planning efficiency and robustness compared to classical methods.
In this paper, we present a novel formulation of traditional sampling-based motion planners as database-oracle structures that can be solved via quantum search algorithms. We consider two complementary scenarios: for simpler sparse environments, we formulate the Quantum Full Path Search Algorithm (q-FPS), which creates a superposition of full random path solutions, manipulates probability amplitudes with Quantum Amplitude Amplification (QAA), and quantum measures a single obstacle free full path solution. For dense unstructured environments, we formulate the Quantum Rapidly Exploring Random Tree algorithm, q-RRT, that creates quantum superpositions of possible parent-child connections, manipulates probability amplitudes with QAA, and quantum measures a single reachable state, which is added to a tree. As performance depends on the number of oracle calls and the probability of measuring good quantum states, we quantify how these errors factor into the probabilistic completeness properties of the algorithm. We then numerically estimate the expected number of database solutions to provide an approximation of the optimal number of oracle calls in the algorithm. We compare the q-RRT algorithm with a classical implementation and verify quadratic run-time speedup in the largest connected component of a 2D dense random lattice. We conclude by evaluating a proposed approach to limit the expected number of database solutions and thus limit the optimal number of oracle calls to a given number.
The increasing convergence of quantum computing (QC) and advanced robotic control is giving rise to a new class of intelligent automation systems. Utilizing quantum-inspired computational principles, contemporary robotic platforms demonstrate greater optimization efficiency, superior perceptual processing, and improved decision-making performance. This survey outlines the principal quantum-oriented control methodologies utilized in robotics, with particular emphasis on approaches that incorporate Quantum Neural Networks (QNN), Quantum Machine Learning (QML), and Quantum-behaved Particle Swarm Optimization (QPSO). The investigation systematically reviews recent advances in quantum-assisted adaptive control, trajectory planning, and optimization frameworks, spotlighting their contributions to improved robustness, precision, and energy efficiency. By analyzing research in quantum reinforcement learning, distributed Noisy Intermediate-Scale Quantum (NISQ) computing, and hybrid quantum-classical approaches, this review clarifies how quantum principles are transforming both theoretical and practical aspects of robotic control. Likewise, several real-world implementations ranging from industrial manipulators to autonomous and cognitive robotic systems demonstrate the growing feasibility of quantum-enhanced decision-making. The review concludes that while remarkable progress has been made in quantum-inspired control for robotics, most existing studies remain constrained to simulations with restricted experimental validation on physical platforms. Addressing this gap will be essential in future research to transition from theoretical and simulated models toward hardware-level implementations, enabling the realization of scalable, fault-tolerant quantum control architectures.
This study evaluates quantum-inspired control strategies on classical hardware, specifically Quantum Sliding Mode Control (QSMC), Quantum Genetic Algorithm (QGA), and a Grover-based quantum search controller, to improve control of a six-degree-of-freedom robotic arm with nonlinear dynamics. Building upon quantum-analogous probabilistic principles, these methods offer significant potential across industries. To clarify the basis of these approaches, the analysis centers on quantum-computation concepts, such as probabilistic amplitude encoding and superposition, which can enhance robustness, trajectory smoothness, and energy efficiency in classical control systems. Consistent, noise-free simulation conditions and multiple independent trials ensured fairness and reproducibility throughout the study. Performance assessment relied on Integrated Absolute Error (IAE), which measures total error over time, and Average Control Energy (Eavg) for mean energy consumption. Compared with classical Sliding Mode Control (SMC), QSMC achieved an 11 % faster transient response. Additionally, QGA delivered 21 % lower IAE and 12.3 % lower average control energy, while the Grover-based controller provided balanced performance among convergence speed, tracking accuracy, and energy efficiency. Overall, these results suggest that quantum-inspired approaches can significantly advance trajectory precision, robustness, and energy efficiency, supporting the development of nextgeneration intelligent robotics.
This paper investigates the potential of quantum computing to revolutionize complex robotics, specifically focusing on optimization of robot control and movement. Traditional robotic control systems often struggle with complex environments and constraints, limiting the ability to achieve optimal performance. We propose a novel quantum algorithm designed to address these challenges by leveraging a dynamic, quantum-based simulation of the physical environment to generate constraints. This approach aims to enable robots to navigate and manipulate objects with unprecedented precision and adaptability. The core mechanism centers on utilizing a quantum solver to efficiently explore the search space of possible robot motions, guided by the simulation's feedback. This contrasts with traditional methods, which often struggle with the high dimensionality and complexity of robot dynamics. The research demonstrates the feasibility of this quantum-driven approach through simulations and preliminary experiments, showcasing potential improvements in robot control and movement.
In the field of robotics, optimization problems with high computational costs, such as motion planning, multi-robot cooperation, and manipulation, are constrained by the exponential growth of the complexity of classical algorithms. This paper discusses practical methods for robot optimization, specifically using noise-aware variational quantum algorithms (VQAs) on current noisy intermediate-scale quantum hardware. To address the inherent errors of quantum devices, a specific framework has been proposed. The framework clearly defines noise modeling, adjusts error mitigation methods, and creates quantum-classical workflows. Using actual quantum computers and high-fidelity simulations, this paper will systematically evaluate four benchmark robotic domains. The statistical data from numerous experiments (up to 100 trials per scenario) indicate that, in the presence of significant hardware noise, noise-aware VQAs can improve the target value by up to 14% compared to standard quantum methods and reduce the number of convergence iterations by 45%. Cross-platform tests indicate that all six robotic tasks run stably, with an accuracy rate exceeding 90% for all devices. In real-world environments, this trajectory generation method performs excellently under noisy conditions when using physical robots. In order to ensure the accuracy of quantum accelerated optimization in robotics, it is necessary to reduce noise. According to the aforementioned research, reducing noise can be practically applied outside of laboratory conditions.