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

Applied Soft Computing

Quantum-inspired ensemble approach to multi-attributed and multi-agent decision-making

Ishwarya M.S., Aswani Kumar Cherukuri

Published Mar 18, 2021• 23 citations
Publisher
DOI
10.1016/j.asoc.2021.107283

Related papers

Quantum Decision Making by Social Agents

Vyacheslav I. Yukalov, Didier Sornette

•Jan 1, 2012•8 citations

A Study of Multiagent Reinforcement Learning based on Quantum Theory

Meng Xiangping, Yuzhen Pi, Yuan Quande +1 more

The Multiconference on Computational Engineering in Systems Applications•Oct 1, 2006•1 citation

272 Enhancing Predictive Accuracy Through Quantum AIquantum AI

Anitha Raju, Disha Ramesh, Praveen M Naik +1 more

In decision-making decision-making , conventional approaches often fail when faced with complexities of complex landscapes. This chapter discusses a new line of work known as quantum-inspired quantum-inspired multi-agent decision-making, which adapts concepts from quantum mechanics quantum mechanics (e.g., superposition, interference, and entangled), making it a fit candidate to explore quantum-inspired paradigms. These ideas have their origin in physics and chemistry as they are used to analyze complex data with multiple parameters. This study establishes an ensemble algorithm inspired by quantum technology, developing a real-time decision-making system to inform decision-making, integrating insights from various sources. This approach combines classical and quantum rules of nature to enhance data processing and interpretation, thereby providing highly accurate predictions. It marks a significant advancement for decision science, applying quantum principles to make light work of difficult decisions.

De Gruyter eBooks•Apr 28, 2025•0 citations

Quantum-Evolutionary Neural Networks for Multi-Agent Federated Learning

Lala, Aarav, Cherukuri, Kalyan

As artificial intelligence continues to drive innovation in complex, decentralized environments, the need for scalable, adaptive, and privacy-preserving decision-making systems has become critical. This paper introduces a novel framework combining quantum-inspired neural networks with evolutionary algorithms to optimize real-time decision-making in multi-agent systems (MAS). The proposed Quantum-Evolutionary Neural Network (QE-NN) leverages quantum computing principles -- such as quantum superposition and entanglement -- to enhance learning speed and decision accuracy, while integrating evolutionary optimization to continually refine agent behaviors in dynamic, uncertain environments. By utilizing federated learning, QE-NN ensures privacy preservation, enabling decentralized agents to collaborate without sharing sensitive data. The framework is designed to allow agents to adapt in real-time to their environments, optimizing decision-making processes for applications in areas such as autonomous systems, smart cities, and healthcare. This research represents a breakthrough in merging quantum computing, evolutionary optimization, and privacy-preserving techniques to solve complex problems in multi-agent decision-making systems, pushing the boundaries of AI in real-world, privacy-sensitive applications.

arXiv:2505.15836•May 16, 2025•1 citation

QiMARL: Quantum-Inspired Multi-Agent Reinforcement Learning Strategy for Efficient Resource Energy Distribution in Nodal Power Stations

Sapthak Mohajon Turjya, Anjan Bandyopadhyay, M. Shamim Kaiser +1 more

The coupling of quantum computing with multi-agent reinforcement learning (MARL) provides an exciting direction to tackle intricate decision-making tasks in high-dimensional spaces. This work introduces a new quantum-inspired multi-agent reinforcement learning (QiMARL) model, utilizing quantum parallelism to achieve learning efficiency and scalability improvement. The QiMARL model is tested on an energy distribution task, which optimizes power distribution between generating and demanding nodal power stations. We compare the convergence time, reward performance, and scalability of QiMARL with traditional Multi-Armed Bandit (MAB) and Multi-Agent Reinforcement Learning methods, such as Greedy, Upper Confidence Bound (UCB), Thompson Sampling, MADDPG, QMIX, and PPO methods with a comprehensive ablation study. Our findings show that QiMARL yields better performance in high-dimensional systems, decreasing the number of training epochs needed for convergence while enhancing overall reward maximization. We also compare the algorithm’s computational complexity, indicating that QiMARL is more scalable to high-dimensional quantum environments. This research opens the door to future studies of quantum-enhanced reinforcement learning (RL) with potential applications to energy optimization, traffic management, and other multi-agent coordination problems.

AI•Sep 1, 2025•5 citations

A survey of quantum-like approaches to decision making and cognition

Mehrdad Ashtiani, Mohammad Abdollahi Azgomi

Mathematical Social Sciences•Mar 3, 2015•96 citations
In decision-making decision-making , conventional approaches often fail when faced with complexities of complex landscapes. This chapter discusses a new line of work known as quantum-inspired quantum-inspired multi-agent decision-making, which adapts concepts from quantum mechanics quantum mechanics (e.g., superposition, interference, and entangled), making it a fit candidate to explore quantum-inspired paradigms. These ideas have their origin in physics and chemistry as they are used to analyze complex data with multiple parameters. This study establishes an ensemble algorithm inspired by quantum technology, developing a real-time decision-making system to inform decision-making, integrating insights from various sources. This approach combines classical and quantum rules of nature to enhance data processing and interpretation, thereby providing highly accurate predictions. It marks a significant advancement for decision science, applying quantum principles to make light work of difficult decisions.
As artificial intelligence continues to drive innovation in complex, decentralized environments, the need for scalable, adaptive, and privacy-preserving decision-making systems has become critical. This paper introduces a novel framework combining quantum-inspired neural networks with evolutionary algorithms to optimize real-time decision-making in multi-agent systems (MAS). The proposed Quantum-Evolutionary Neural Network (QE-NN) leverages quantum computing principles -- such as quantum superposition and entanglement -- to enhance learning speed and decision accuracy, while integrating evolutionary optimization to continually refine agent behaviors in dynamic, uncertain environments. By utilizing federated learning, QE-NN ensures privacy preservation, enabling decentralized agents to collaborate without sharing sensitive data. The framework is designed to allow agents to adapt in real-time to their environments, optimizing decision-making processes for applications in areas such as autonomous systems, smart cities, and healthcare. This research represents a breakthrough in merging quantum computing, evolutionary optimization, and privacy-preserving techniques to solve complex problems in multi-agent decision-making systems, pushing the boundaries of AI in real-world, privacy-sensitive applications.
The coupling of quantum computing with multi-agent reinforcement learning (MARL) provides an exciting direction to tackle intricate decision-making tasks in high-dimensional spaces. This work introduces a new quantum-inspired multi-agent reinforcement learning (QiMARL) model, utilizing quantum parallelism to achieve learning efficiency and scalability improvement. The QiMARL model is tested on an energy distribution task, which optimizes power distribution between generating and demanding nodal power stations. We compare the convergence time, reward performance, and scalability of QiMARL with traditional Multi-Armed Bandit (MAB) and Multi-Agent Reinforcement Learning methods, such as Greedy, Upper Confidence Bound (UCB), Thompson Sampling, MADDPG, QMIX, and PPO methods with a comprehensive ablation study. Our findings show that QiMARL yields better performance in high-dimensional systems, decreasing the number of training epochs needed for convergence while enhancing overall reward maximization. We also compare the algorithm’s computational complexity, indicating that QiMARL is more scalable to high-dimensional quantum environments. This research opens the door to future studies of quantum-enhanced reinforcement learning (RL) with potential applications to energy optimization, traffic management, and other multi-agent coordination problems.