aqora

Toward Intelligent Agents on Quantum Computers

Matthias Klusch

We present an innovative perspective of usage of intelligent agents and multi-agent systems in quantum computing, and summarize preliminary results of our initial research in this direction.

Adaptive Agents and Multi-Agents SystemsJul 19, 20041 citation

An Intelligent Multi-Turn Conversational Agent for Adaptive Quantum Computing Education Using Large Language Models and Quantum Circuit Simulation

Adithya N Reddy, V S S Renesh, Achu Jayan +1 more

International Conference on Emerging Smart Computing and Informatics (ESCI)Mar 11, 20260 citations

Toward intelligent agents on quantum computers

Klusch

Adaptive Agents and Multi-Agents SystemsJan 1, 20040 citations

Quantum strategies, quantum games, agent-based simulation, and intelligent systems for business, management and engineering

Shuliang Li

This keynote speech covers quantum games, quantum models, intelligent systems and simulation for business and management. In particular, the following research work will be presented. \n-\tA survey of the current status of research on quantum games (Dingxuan Huang & Shuliang Li)\n-\tQuantum games and models for competition (Dingxuan Huang & Shuliang Li)\n-\tQuantum games and models for cooperation (Dingxuan Huang & Shuliang Li)\n-\tSocial media for brand virality and open innovation\n-\tIntelligent systems and multi-agent-based systems for strategic decision making (Shuliang Li & Jim Zheng Li)\n-\tModelling & computer simulation of enterprise carbon intangible asset evolution; and a resilient multi-factor elasticity framework for low carbon cities (Yin Zeng & Shuliang Li)This keynote speech covers quantum games, quantum models, intelligent systems and simulation for business and management. In particular, the following research work will be presented. \n-\tA survey of the current status of research on quantum games (Dingxuan Huang & Shuliang Li)\n-\tQuantum games and models for competition (Dingxuan Huang & Shuliang Li)\n-\tQuantum games and models for cooperation (Dingxuan Huang & Shuliang Li)\n-\tSocial media for brand virality and open innovation\n-\tIntelligent systems and multi-agent-based systems for strategic decision making (Shuliang Li & Jim Zheng Li)\n-\tModelling & computer simulation of enterprise carbon intangible asset evolution; and a resilient multi-factor elasticity framework for low carbon cities (Yin Zeng & Shuliang Li)

Jan 1, 20180 citations

Quantum Computing and AI

Raveesh Gupta -

This paper offers a comprehensive review of the evolving landscape at the intersection of quantum mechanics and artificial intelligence (AI). Focusing on current trends and advancements, it explores the profound impact of quantum computing on AI methodologies and applications.

International Journal For Multidisciplinary ResearchMay 4, 20243 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.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:2505.15836May 16, 20251 citation

Toward Intelligent Agents on Quantum Computers

Abstract

Related papers

Toward Intelligent Agents on Quantum Computers

Abstract

Related papers