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

arXiv:2506.03205

Q-ARDNS-Multi: A Multi-Agent Quantum Reinforcement Learning Framework with Meta-Cognitive Adaptation for Complex 3D Environments

Umberto Gonçalves de Sousa

Published Jun 2, 2025• 0 citations
arXivHTMLPDF

Abstract

This paper presents Q-ARDNS-Multi, an advanced multi-agent quantum reinforcement learning (QRL) framework that extends the ARDNS-FN-Quantum model, where Q-ARDNS-Multi stands for "Quantum Adaptive Reward-Driven Neural Simulator - Multi-Agent". It integrates quantum circuits with RY gates, meta-cognitive adaptation, and multi-agent coordination mechanisms for complex 3D environments. Q-ARDNS-Multi leverages a 2-qubit quantum circuit for action selection, a dual-memory system inspired by human cognition, a shared memory module for agent cooperation, and adaptive exploration strategies modulated by reward variance and intrinsic motivation. Evaluated in a $10 \times 10 \times 3$ GridWorld environment with two agents over 5000 episodes, Q-ARDNS-Multi achieves success rates of 99.6\% and 99.5\% for Agents 0 and 1, respectively, outperforming Multi-Agent Deep Deterministic Policy Gradient (MADDPG) and Soft Actor-Critic (SAC) in terms of success rate, stability, navigation efficiency, and collision avoidance. The framework records mean rewards of $-304.2891 \pm 756.4636$ and $-295.7622 \pm 752.7103$, averaging 210 steps to goal, demonstrating its robustness in dynamic settings. Comprehensive analyses, including learning curves, reward distributions, statistical tests, and computational efficiency evaluations, highlight the contributions of quantum circuits and meta-cognitive adaptation. By bridging quantum computing, cognitive science, and multi-agent RL, Q-ARDNS-Multi offers a scalable, human-like approach for applications in robotics, autonomous navigation, and decision-making under uncertainty.

DOI
10.48550/arxiv.2506.03205

Related papers

ARDNS-FN-Quantum: A Quantum-Enhanced Reinforcement Learning Framework with Cognitive-Inspired Adaptive Exploration for Dynamic Environments

Umberto Gonçalves de Sousa

Reinforcement learning (RL) has transformed sequential decisionmaking, yet traditional algorithms like Deep Q-Networks (DQNs) and Proximal Policy Optimization (PPO) often struggle with efficient exploration, stability, and adaptability in dynamic environments. This study presents ARDNS-FN-Quantum (Adaptive Reward-Driven Neural Simulator with Quantum enhancement), a novel framework that integrates a 2-qubit quantum circuit for action selection, a dual-memory system inspired by human cognition, and adaptive exploration strategies modulated by reward variance and curiosity. Evaluated in a 10X10 grid-world over 20,000 episodes, ARDNS-FN-Quantum achieves a 99.5% success rate (versus 81.3% for DQN and 97.0% for PPO), a mean reward of 9.0528 across all episodes (versus 1.2941 for DQN and 7.6196 for PPO), and an average of 46.7 steps to goal (versus 135.9 for DQN and 62.5 for PPO). In the last 100 episodes, it records a mean reward of 9.1652 (versus 7.0916 for DQN and 9.0310 for PPO) and 37.2 steps to goal (versus 52.7 for DQN and 53.4 for PPO). Graphical analyses, including learning curves, steps-to-goal trends, reward variance, and reward distributions, demonstrate ARDNS-FN-Quantum's superior stability (reward variance 5.424 across all episodes versus 252.262 for DQN and 76.583 for PPO) and efficiency. By bridging quantum computing, cognitive science, and RL, ARDNS-FN-Quantum offers a scalable, human-like approach to adaptive learning in uncertain environments, with potential applications in robotics, autonomous systems, and decision-making under uncertainty.

arXiv:2505.06300•May 12, 2025•1 citation

A Quantum-Enhanced Framework for Human-Like Reinforcement Learning: ARDNS-P-Quantum with Piagetian Stages

Umberto Gonçalves de Sousa

Traditional reinforcement learning (RL) models like Deep Q-Networks (DQNs) often lack human-like capabilities such as multi-timescale memory, adaptive plasticity, and cognitive developmental stages. We introduce ARDNS-P-Quantum, an enhanced version of the Adaptive Reward-Driven Neural Simulator with Piagetian Developmental Stages (ARDNS-P), integrating quantum computing to improve action selection. Using a 2-qubit quantum circuit with RY rotations and 16 shots, ARDNS-P-Quantum leverages a dual-memory system and Piaget's developmental stages to achieve superior performance in a 10×10 grid-world. Over 20,000 episodes, ARDNS-P Quantum attains a 99.2% goal-reaching success rate (vs. 91.9% for ARDNS-P and 84.5% for DQN), reduces steps to goal to 33.3 (vs. 149.2 for ARDNS-P and 72.1 for DQN), and increases mean rewards to 9.1169 (vs. 9.12 for ARDNS-P and 3.2207 for DQN) in the last 100 episodes. The quantum circuit design, with fewer shots and optimized depth, enhances exploration efficiency. This framework bridges RL, neuroscience, developmental psychology, and quantum computing, offering a scalable approach for human-like learning in dynamic environments.

•Apr 18, 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

MADQRL: Distributed Quantum Reinforcement Learning Framework for Multi-Agent Environments

Abhishek Sawaika, Samuel Yen-Chi Chen, Udaya Parampalli +1 more

Reinforcement learning (RL) is one of the most practical ways to learn from real-life use-cases. Motivated from the cognitive methods used by humans makes it a widely acceptable strategy in the field of artificial intelligence. Most of the environments used for$\mathbf{R L}$are often high-dimensional, and traditional RL algorithms becomes computationally expensive and challenging to effectively learn from such systems. Recent advancements in practical demonstration of quantum computing (QC) theories, such as compact encoding, enhanced representation and learning algorithms, random sampling, or the inherent stochastic nature of quantum systems, have opened up new directions to tackle these challenges. Quantum reinforcement learning (QRL) is seeking significant traction over the past few years. However, the current state of quantum hardware is not enough to cater for such highdimensional environments with complex multi-agent setup. To tackle this issue, we propose a distributed framework for QRL where multiple agents learn independently, distributing the load of joint training from individual machines. Our method works well for environments with disjoint sets of action and observation spaces, but can also be extended to other systems with reasonable approximations. We analyze the proposed method on cooperativepong environment and our results indicate$\sim 10 \%$improvement from other distribution strategies, and$\sim 5 \%$improvement from classical models of policy representation.

arXiv:2604.11131•International Conference on Quantum Communications, Networking, and Computing (QCNC)•Apr 6, 2026•0 citations

A Hybrid Quantum-Cognitive Framework for Personalized Learning: Simulation-Based Validation and System Design

Özel Sebetçi

The intersection of quantum computing, cognitive science, and artificial intelligence is fostering a new generation of neuroadaptive educational technologies that move beyond the algorithmic rigidity of conventional adaptive learning systems. The Quantum-Cognitive Adaptive Learning (QCAL) framework is introduced as a hybrid approach unifying quantum cognition, quantum reinforcement learning (QRL) optimized through the Quantum Natural Gradient (QNG), and real-time biometric feedback (EEG and eye-tracking). QCAL represents each learner’s cognitive state as a quantum wavefunction (ψ), enabling probabilistic modeling of conceptual uncertainty, superposition, and dynamic knowledge transitions. A meta-adaptive reward correction loop adjusts instructional policies using biometric indicators of cognitive load and engagement. Ablation and simulation studies with 500 synthetic learners demonstrated that QCAL-Full significantly outperformed classical adaptive models in learning efficiency (ΔM = +14%), cognitive-load stabilization (p < 0.001), and adaptive responsiveness. Pilot biometric validation (n = 10) confirmed the reliability of EEG-derived load indices (ICC = 0.89) and showed real-time engagement recovery via the reward-correction mechanism. Comparative analyses with Web of Science-indexed hybrid models and recent LLM + EEG tutoring systems (TutorLLM 2025; NeuroChat 2025) indicate that QCAL uniquely integrates quantum-cognitive state modeling with ethical, human-centered personalization compliant with GDPR/KVKK. By aligning NISQ-compatible quantum architectures with biologically informed and ethically explainable learning strategies, QCAL establishes a reproducible, privacy-aware foundation for the next generation of neuro-quantum educational systems.

•Aug 1, 2026•0 citations

Survey Paper on Quantum Deep Reinforcement Learning for Robot Navigation Tasks

Shruti Doiphode, Prof. Mukul Jagtap

Abstract: Quantum deep reinforcement learning is an integration of the principles of quantum computing with deep reinforcement learning in an effort to advance robot navigation tasks. Due to the explosion of the state and action spaces, traditionally DRL methods are limited in scalability and efficiency. QDRL, however, uses quantum superposition and entanglement for more efficient processing of big amounts of information-it enables robots to learn optimal policies for navigating complex environments. We introduce here a new QDRL framework in which quantum neural networks encode policy functions and value estimates. We demonstrate how quantum circuits can take advantage of multi-dimensional state representations in order to strengthen the power of a robot in changing its environment over time. Benchmark experiments on navigation tasks reflect phenomenal acceleration in learning performance along with better decision-making accuracy compared to classical DRL methods. We then consider integrating such quantum algorithms, as Grover's search, to improve exploration strategies in sparse reward settings. The experiments show that QDRL speeds up convergence rates and provides robots with the possibility to react more rapidly to unexpected obstacles. It discusses new applications for quantum computing of autonomous systems, potentially applied to robotics, autonomous vehicles, and other environments which require complex decision making. Some lines of work in the future are optimization of the resources needed for quantum computers and hybrid architectures for the resolution of classical problems in navigation

International Journal for Research in Applied Science and Engineering Technology•Oct 18, 2024•0 citations
This paper presents Q-ARDNS-Multi, an advanced multi-agent quantum reinforcement learning (QRL) framework that extends the ARDNS-FN-Quantum model, where Q-ARDNS-Multi stands for "Quantum Adaptive Reward-Driven Neural Simulator - Multi-Agent". It integrates quantum circuits with RY gates, meta-cognitive adaptation, and multi-agent coordination mechanisms for complex 3D environments. Q-ARDNS-Multi leverages a 2-qubit quantum circuit for action selection, a dual-memory system inspired by human cognition, a shared memory module for agent cooperation, and adaptive exploration strategies modulated by reward variance and intrinsic motivation. Evaluated in a 10×10×310 \times 10 \times 310×10×3 GridWorld environment with two agents over 5000 episodes, Q-ARDNS-Multi achieves success rates of 99.6% and 99.5% for Agents 0 and 1, respectively, outperforming Multi-Agent Deep Deterministic Policy Gradient (MADDPG) and Soft Actor-Critic (SAC) in terms of success rate, stability, navigation efficiency, and collision avoidance. The framework records mean rewards of −304.2891±756.4636-304.2891 \pm 756.4636−304.2891±756.4636 and −295.7622±752.7103-295.7622 \pm 752.7103−295.7622±752.7103, averaging 210 steps to goal, demonstrating its robustness in dynamic settings. Comprehensive analyses, including learning curves, reward distributions, statistical tests, and computational efficiency evaluations, highlight the contributions of quantum circuits and meta-cognitive adaptation. By bridging quantum computing, cognitive science, and multi-agent RL, Q-ARDNS-Multi offers a scalable, human-like approach for applications in robotics, autonomous navigation, and decision-making under uncertainty.
Reinforcement learning (RL) has transformed sequential decisionmaking, yet traditional algorithms like Deep Q-Networks (DQNs) and Proximal Policy Optimization (PPO) often struggle with efficient exploration, stability, and adaptability in dynamic environments. This study presents ARDNS-FN-Quantum (Adaptive Reward-Driven Neural Simulator with Quantum enhancement), a novel framework that integrates a 2-qubit quantum circuit for action selection, a dual-memory system inspired by human cognition, and adaptive exploration strategies modulated by reward variance and curiosity. Evaluated in a 10X10 grid-world over 20,000 episodes, ARDNS-FN-Quantum achieves a 99.5% success rate (versus 81.3% for DQN and 97.0% for PPO), a mean reward of 9.0528 across all episodes (versus 1.2941 for DQN and 7.6196 for PPO), and an average of 46.7 steps to goal (versus 135.9 for DQN and 62.5 for PPO). In the last 100 episodes, it records a mean reward of 9.1652 (versus 7.0916 for DQN and 9.0310 for PPO) and 37.2 steps to goal (versus 52.7 for DQN and 53.4 for PPO). Graphical analyses, including learning curves, steps-to-goal trends, reward variance, and reward distributions, demonstrate ARDNS-FN-Quantum's superior stability (reward variance 5.424 across all episodes versus 252.262 for DQN and 76.583 for PPO) and efficiency. By bridging quantum computing, cognitive science, and RL, ARDNS-FN-Quantum offers a scalable, human-like approach to adaptive learning in uncertain environments, with potential applications in robotics, autonomous systems, and decision-making under uncertainty.
Traditional reinforcement learning (RL) models like Deep Q-Networks (DQNs) often lack human-like capabilities such as multi-timescale memory, adaptive plasticity, and cognitive developmental stages. We introduce ARDNS-P-Quantum, an enhanced version of the Adaptive Reward-Driven Neural Simulator with Piagetian Developmental Stages (ARDNS-P), integrating quantum computing to improve action selection. Using a 2-qubit quantum circuit with RY rotations and 16 shots, ARDNS-P-Quantum leverages a dual-memory system and Piaget's developmental stages to achieve superior performance in a 10×10 grid-world. Over 20,000 episodes, ARDNS-P Quantum attains a 99.2% goal-reaching success rate (vs. 91.9% for ARDNS-P and 84.5% for DQN), reduces steps to goal to 33.3 (vs. 149.2 for ARDNS-P and 72.1 for DQN), and increases mean rewards to 9.1169 (vs. 9.12 for ARDNS-P and 3.2207 for DQN) in the last 100 episodes. The quantum circuit design, with fewer shots and optimized depth, enhances exploration efficiency. This framework bridges RL, neuroscience, developmental psychology, and quantum computing, offering a scalable approach for human-like learning in dynamic environments.
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.
Reinforcement learning (RL) is one of the most practical ways to learn from real-life use-cases. Motivated from the cognitive methods used by humans makes it a widely acceptable strategy in the field of artificial intelligence. Most of the environments used forRL\mathbf{R L}RLare often high-dimensional, and traditional RL algorithms becomes computationally expensive and challenging to effectively learn from such systems. Recent advancements in practical demonstration of quantum computing (QC) theories, such as compact encoding, enhanced representation and learning algorithms, random sampling, or the inherent stochastic nature of quantum systems, have opened up new directions to tackle these challenges. Quantum reinforcement learning (QRL) is seeking significant traction over the past few years. However, the current state of quantum hardware is not enough to cater for such highdimensional environments with complex multi-agent setup. To tackle this issue, we propose a distributed framework for QRL where multiple agents learn independently, distributing the load of joint training from individual machines. Our method works well for environments with disjoint sets of action and observation spaces, but can also be extended to other systems with reasonable approximations. We analyze the proposed method on cooperativepong environment and our results indicate∼10%\sim 10 \%∼10%improvement from other distribution strategies, and∼5%\sim 5 \%∼5%improvement from classical models of policy representation.
The intersection of quantum computing, cognitive science, and artificial intelligence is fostering a new generation of neuroadaptive educational technologies that move beyond the algorithmic rigidity of conventional adaptive learning systems. The Quantum-Cognitive Adaptive Learning (QCAL) framework is introduced as a hybrid approach unifying quantum cognition, quantum reinforcement learning (QRL) optimized through the Quantum Natural Gradient (QNG), and real-time biometric feedback (EEG and eye-tracking). QCAL represents each learner’s cognitive state as a quantum wavefunction (ψ), enabling probabilistic modeling of conceptual uncertainty, superposition, and dynamic knowledge transitions. A meta-adaptive reward correction loop adjusts instructional policies using biometric indicators of cognitive load and engagement. Ablation and simulation studies with 500 synthetic learners demonstrated that QCAL-Full significantly outperformed classical adaptive models in learning efficiency (ΔM = +14%), cognitive-load stabilization (p < 0.001), and adaptive responsiveness. Pilot biometric validation (n = 10) confirmed the reliability of EEG-derived load indices (ICC = 0.89) and showed real-time engagement recovery via the reward-correction mechanism. Comparative analyses with Web of Science-indexed hybrid models and recent LLM + EEG tutoring systems (TutorLLM 2025; NeuroChat 2025) indicate that QCAL uniquely integrates quantum-cognitive state modeling with ethical, human-centered personalization compliant with GDPR/KVKK. By aligning NISQ-compatible quantum architectures with biologically informed and ethically explainable learning strategies, QCAL establishes a reproducible, privacy-aware foundation for the next generation of neuro-quantum educational systems.
Abstract: Quantum deep reinforcement learning is an integration of the principles of quantum computing with deep reinforcement learning in an effort to advance robot navigation tasks. Due to the explosion of the state and action spaces, traditionally DRL methods are limited in scalability and efficiency. QDRL, however, uses quantum superposition and entanglement for more efficient processing of big amounts of information-it enables robots to learn optimal policies for navigating complex environments. We introduce here a new QDRL framework in which quantum neural networks encode policy functions and value estimates. We demonstrate how quantum circuits can take advantage of multi-dimensional state representations in order to strengthen the power of a robot in changing its environment over time. Benchmark experiments on navigation tasks reflect phenomenal acceleration in learning performance along with better decision-making accuracy compared to classical DRL methods. We then consider integrating such quantum algorithms, as Grover's search, to improve exploration strategies in sparse reward settings. The experiments show that QDRL speeds up convergence rates and provides robots with the possibility to react more rapidly to unexpected obstacles. It discusses new applications for quantum computing of autonomous systems, potentially applied to robotics, autonomous vehicles, and other environments which require complex decision making. Some lines of work in the future are optimization of the resources needed for quantum computers and hybrid architectures for the resolution of classical problems in navigation