MetaQ: A Quantum Approach for Secure and Optimized Metaverse Environment
- Authors
- Kwon, Hyuk-Jun; El Azzaoui, Abir; Park, Jong Hyuk
- Issue Date
- Sep-2022
- Publisher
- Springer Science + Business Media
- Keywords
- Metaverse; Learning; Quantum Information Technology; Quantum Kernels; Hybrid-Quantum-Classical Machine 
- Citation
- Human-centric Computing and Information Sciences, v.12
- Journal Title
- Human-centric Computing and Information Sciences
- Volume
- 12
- URI
- https://scholarworks.bwise.kr/sch/handle/2021.sw.sch/21452
- DOI
- 10.22967/HCIS.2022.12.042
- ISSN
- 2192-1962
- Abstract
- Recently, Metaverse technology became the topic of today's following the news of major companies intending to create their Metaverse environment for various application such as gaming, assets, virtual meetings, and so on. The success of Metaverse-based application is highly depending on fast and secure connectivity, integrated high-end technologies such as virtual reality (VR), augmented reality (AR), and mixed reality (MR). The current Metaverse applications has critical challenges in both hardware and software that urge immediate mitigation. Issues such as security, privacy, connectivity, and computation complexity are the main reasons behind the slow integration of Metaverse. On the other hand, Quantum technology promises fast, optimized, and scalable computation results due to its exponentially fast processing power. To this end, in this paper we propose a comprehensive and detailed review of all the possible cases of Quantum implementation into Metaverse environment. Moreover, we propose as a case scenario the deployment of a hybrid Quantum kernels approach to apply an optimized linear statistical method and fed the results to a classical supervised vector machine model to improve the scalability and performance of Metaverse applications. We believe this work would be a steppingstone for future research direction in order to develop Quantum-based Metaverse applications.
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Collections - College of Global Business > Department of Economics and Finance > 1. Journal Articles
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