Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

IEEE Convention of Electrical and Electronics Engineers in Israel

Probabilistic mobile robot with quantum decision-making

Eugene Kagan, Emanuel Salmona, Irad Ben‐Gal

Published Mar 1, 2008• 6 citations
Publisher

Abstract

This work addresses informational-based methods for decision-making in Hilbert space, widely known as quantum decision-making, and their application to the control of mobile robot. We consider a mobile robot, whose objective is to find objects in a changing environment. We compare the robot performance under both a classical and a quantum decision making (control) approaches. Both approaches are driven by similar information-theory decision criteria. The robot behavior is physically studied by using an NXT Lego Robot at the CIM lab in Tel Aviv University.

DOI
10.1109/eeei.2008.4736561

Related papers

Fuzzy model of control for quantum-controlled mobile robots

Alexander Rybalov, Eugene Kagan, Yochai Manor +1 more

In this work we consider a navigation of quantum-controlled mobile robot by the use of fuzzy methods. A fuzzy model of the robot behavior is obtained by transformation of the robot's states (that appear in a form of qubits) into the pairs of membership functions. For a full description of the robot's turns, we define a reverse fuzzy Hadamard operator that completes the known Hannachi, Hatakeyama and Hirota model. The suggested transformations and methods were comparatively studied by field trials using small mobile robots and by numerical simulations that model the robot's behavior in the presence of noise.

IEEE Convention of Electrical and Electronics Engineers in Israel•Nov 1, 2010•10 citations

Robot localization aided by quantum algorithms

Unai Antero, Basilio Sierra, Jon Oñativia +2 more

Localization is a critical aspect of mobile robotics, enabling robots to navigate their environment efficiently and avoid obstacles. Current probabilistic localization methods, such as the Adaptive-Monte Carlo localization (AMCL) algorithm, are computationally intensive and may struggle with large maps or high-resolution sensor data. This paper explores the application of quantum computing in robotics, focusing on the use of Grover's search algorithm to improve the efficiency of localization in mobile robots. We propose a novel approach to utilize Grover's algorithm in a 2D map, enabling faster and more efficient localization. Despite the limitations of current physical quantum computers, our experimental results demonstrate a significant speedup over classical methods, highlighting the potential of quantum computing to improve robotic localization. This work bridges the gap between quantum computing and robotics, providing a practical solution for robotic localization and paving the way for future research in quantum robotics.

arXiv:2502.00077•Jan 31, 2025•0 citations

Quantum robots and environments

Paul A. Benioff

Quantum robots and their interactions with environments of quantum systems are described, and their study justified. A quantum robot is a mobile quantum system that includes an on-board quantum computer and needed ancillary systems. Quantum robots carry out tasks whose goals include specified changes in the state of the environment, or carrying out measurements on the environment. Each task is a sequence of alternating computation and action phases. Computation phase activites include determination of the action to be carried out in the next phase, and recording of information on neighborhood environmental system states. Action phase activities include motion of the quantum robot and changes in the neighborhood environment system states. Models of quantum robots and their interactions with environments are described using discrete space and time. A unitary step operator $T$ that gives the single time step dynamics is associated with each task. ${T=T}_{a}{+T}_{c}$ is a sum of action phase and computation phase step operators. Conditions that ${T}_{a}$ and ${T}_{c}$ should satisfy are given along with a description of the evolution as a sum over paths of completed phase input and output states. A simple example of a task---carrying out a measurement on a very simple environment---is analyzed in detail. A decision tree for the task is presented and discussed in terms of the sums over phase paths. It is seen that no definite times or durations are associated with the phase steps in the tree, and that the tree describes the successive phase steps in each path in the sum over phase paths.

arXiv:quant-ph/9802067•Physical Review A•Aug 1, 1998•88 citations

Quantum-Driven Constraint Optimization for Complex Robotics

Jincheng Zhang

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.

•Sep 3, 2026•0 citations

Robot localization aided by quantum algorithms

Unai Antero, Basilio Sierra, Jon Oñativia +2 more

Localization is a critical aspect of mobile robotics, enabling robots to navigate their environment efficiently and avoid obstacles. Current probabilistic localization methods , such as the Adaptive Monte Carlo localization (AMCL) algorithm, are computationally intensive and may struggle with large maps or high resolution sensor data. This paper explores the application of quantum computing in robotics, focusing on the use of Grover’s search algorithm to improve the efficiency of localization in mobile robots. We propose a novel approach to utilize Grover’s algorithm in a 2D map, enabling faster and more efficient localization. Despite the limitations of current physical quantum computers , our experimental results demonstrate a significant speedup over classical methods, highlighting the potential of quantum computing to improve robotic localization. This work bridges the gap between quantum computing and robotics, providing a practical solution for robotic localization and paving the way for future research in quantum robotics.

Robotics and Autonomous Systems•Apr 26, 2025•2 citations

Quantum-Enhanced Reinforcement Learning (QRL) for Robotics

Jincheng Zhang

This paper explores the application of Quantum-Enhanced Reinforcement Learning (QRL) to robotics, aiming to accelerate learning speeds and enable more intelligent decision-making in complex environments. Traditional reinforcement learning algorithms often struggle with scalability and convergence, particularly when dealing with high-dimensional state spaces and complex environments. Quantum computing offers the potential to overcome these limitations by leveraging quantum mechanics to perform computations that are intractable for classical computers. We propose a novel QRL framework that utilizes quantum-enhanced reinforcement learning to improve exploration, adaptation, and overall performance. The core mechanism involves employing quantum circuits to represent and manipulate the state space, facilitating more efficient learning and potentially uncovering novel strategies. This research investigates the feasibility and potential impact of QRL on robotic control, focusing on scenarios requiring rapid adaptation and robust decision-making in dynamic environments. We present preliminary results demonstrating the efficacy of our approach in a simulated robotic task involving navigation and obstacle avoidance.

•Sep 3, 2026•0 citations
This work addresses informational-based methods for decision-making in Hilbert space, widely known as quantum decision-making, and their application to the control of mobile robot. We consider a mobile robot, whose objective is to find objects in a changing environment. We compare the robot performance under both a classical and a quantum decision making (control) approaches. Both approaches are driven by similar information-theory decision criteria. The robot behavior is physically studied by using an NXT Lego Robot at the CIM lab in Tel Aviv University.
In this work we consider a navigation of quantum-controlled mobile robot by the use of fuzzy methods. A fuzzy model of the robot behavior is obtained by transformation of the robot's states (that appear in a form of qubits) into the pairs of membership functions. For a full description of the robot's turns, we define a reverse fuzzy Hadamard operator that completes the known Hannachi, Hatakeyama and Hirota model. The suggested transformations and methods were comparatively studied by field trials using small mobile robots and by numerical simulations that model the robot's behavior in the presence of noise.
Localization is a critical aspect of mobile robotics, enabling robots to navigate their environment efficiently and avoid obstacles. Current probabilistic localization methods, such as the Adaptive-Monte Carlo localization (AMCL) algorithm, are computationally intensive and may struggle with large maps or high-resolution sensor data. This paper explores the application of quantum computing in robotics, focusing on the use of Grover's search algorithm to improve the efficiency of localization in mobile robots. We propose a novel approach to utilize Grover's algorithm in a 2D map, enabling faster and more efficient localization. Despite the limitations of current physical quantum computers, our experimental results demonstrate a significant speedup over classical methods, highlighting the potential of quantum computing to improve robotic localization. This work bridges the gap between quantum computing and robotics, providing a practical solution for robotic localization and paving the way for future research in quantum robotics.
Quantum robots and their interactions with environments of quantum systems are described, and their study justified. A quantum robot is a mobile quantum system that includes an on-board quantum computer and needed ancillary systems. Quantum robots carry out tasks whose goals include specified changes in the state of the environment, or carrying out measurements on the environment. Each task is a sequence of alternating computation and action phases. Computation phase activites include determination of the action to be carried out in the next phase, and recording of information on neighborhood environmental system states. Action phase activities include motion of the quantum robot and changes in the neighborhood environment system states. Models of quantum robots and their interactions with environments are described using discrete space and time. A unitary step operator TTT that gives the single time step dynamics is associated with each task. T=Ta+Tc{T=T}_{a}{+T}_{c}T=Ta​+Tc​ is a sum of action phase and computation phase step operators. Conditions that Ta{T}_{a}Ta​ and Tc{T}_{c}Tc​ should satisfy are given along with a description of the evolution as a sum over paths of completed phase input and output states. A simple example of a task---carrying out a measurement on a very simple environment---is analyzed in detail. A decision tree for the task is presented and discussed in terms of the sums over phase paths. It is seen that no definite times or durations are associated with the phase steps in the tree, and that the tree describes the successive phase steps in each path in the sum over phase paths.
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.
Localization is a critical aspect of mobile robotics, enabling robots to navigate their environment efficiently and avoid obstacles. Current probabilistic localization methods , such as the Adaptive Monte Carlo localization (AMCL) algorithm, are computationally intensive and may struggle with large maps or high resolution sensor data. This paper explores the application of quantum computing in robotics, focusing on the use of Grover’s search algorithm to improve the efficiency of localization in mobile robots. We propose a novel approach to utilize Grover’s algorithm in a 2D map, enabling faster and more efficient localization. Despite the limitations of current physical quantum computers , our experimental results demonstrate a significant speedup over classical methods, highlighting the potential of quantum computing to improve robotic localization. This work bridges the gap between quantum computing and robotics, providing a practical solution for robotic localization and paving the way for future research in quantum robotics.
This paper explores the application of Quantum-Enhanced Reinforcement Learning (QRL) to robotics, aiming to accelerate learning speeds and enable more intelligent decision-making in complex environments. Traditional reinforcement learning algorithms often struggle with scalability and convergence, particularly when dealing with high-dimensional state spaces and complex environments. Quantum computing offers the potential to overcome these limitations by leveraging quantum mechanics to perform computations that are intractable for classical computers. We propose a novel QRL framework that utilizes quantum-enhanced reinforcement learning to improve exploration, adaptation, and overall performance. The core mechanism involves employing quantum circuits to represent and manipulate the state space, facilitating more efficient learning and potentially uncovering novel strategies. This research investigates the feasibility and potential impact of QRL on robotic control, focusing on scenarios requiring rapid adaptation and robust decision-making in dynamic environments. We present preliminary results demonstrating the efficacy of our approach in a simulated robotic task involving navigation and obstacle avoidance.