Quantum and Artificial-Intelligence-Powered Metaverse for Human–Swarm Teaming: An Affordable and Safe Platform

QAI-Swarm is a metaverse for Quantum and Artificial Intelligence (QAI) enabled human-swarm teaming (HST) environments. Today’s advances in sensor technologies make this vision a near reality. Research in HST is hindered by different complexities including the prohibitive costs for operating swarms of land, air or maritime vehicles, and the social and ethical issues related to safety, assurance, and moral hazards that arise from testing swarm research concepts that remain distant from operational readiness. Investigating answers to some of the most profound research questions in HST is unrealistic without an affordable research environment. We extend our previous work on HST Metaverse with a framework to demonstrate a roadmap for implementing QAI-Swarm metaverse. The envisaged metaverse opens opportunities for research groups to prototype and advance technology and engineering solutions in swarm systems research.

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Publication Details

Journal
IEEE Systems Man and Cybernetics Magazine
Published
2025-10-01
DOI
https://doi.org/10.1109/msmc.2024.3467769
Primary Topic
Mobile Crowdsensing and Crowdsourcing
Type
article
Field-Weighted Citation Impact
0.00
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article

Quantum and Artificial-Intelligence-Powered Metaverse for Human–Swarm Teaming: An Affordable and Safe Platform

IEEE Systems Man and Cybernetics Magazine
Mobile Crowdsensing and Crowdsourcing
article

Quantum and Artificial-Intelligence-Powered Metaverse for Human–Swarm Teaming: An Affordable and Safe Platform

article en

Abstract

QAI-Swarm is a metaverse for Quantum and Artificial Intelligence (QAI) enabled human-swarm teaming (HST) environments. Today’s advances in sensor technologies make this vision a near reality. Research in HST is hindered by different complexities including the prohibitive costs for operating swarms of land, air or maritime vehicles, and the social and ethical issues related to safety, assurance, and moral hazards that arise from testing swarm research concepts that remain distant from operational readiness. Investigating answers to some of the most profound research questions in HST is unrealistic without an affordable research environment. We extend our previous work on HST Metaverse with a framework to demonstrate a roadmap for implementing QAI-Swarm metaverse. The envisaged metaverse opens opportunities for research groups to prototype and advance technology and engineering solutions in swarm systems research.

IEEE Systems Man and Cybernetics MagazineVol. 11(4)
University of Canberra (AU), UNSW Sydney (AU), UNSW Canberra
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Mobile Crowdsensing and Crowdsourcing
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