An integrated complex T-spherical fuzzy set and soft set model for quantum computing and energy resource planning

• Published in: Information sciences 2024-03, Vol.661, p.120101, Article 120101
• Main Authors: Jan, Naeem, Gwak, Jeonghwan, Pamucar, Dragan, Kang, Hyoungku
• Format: Article
• Language: English
• Subjects: Complex spherical fuzzy soft relations; Complex T-spherical fuzzy soft relations; Energy resources; Fuzzy soft set; Picture fuzzy soft set; Quantum computing
• ISSN: 0020-0255; 1872-6291
• DOI: 10.1016/j.ins.2024.120101

This study introduces the Complex T-Spherical Fuzzy Soft Set (CTSFSS), a novel and practical model for representing two-dimensional ambiguous information. This is achieved by integrating the Complex T-Spherical Fuzzy Set (CTSFS) with the Soft Set (SS). The development of the CTSFSS theory is motivated by the absence of neutral membership in complex q-rung orthopair fuzzy soft sets and the limitations of Traditional Spherical Fuzzy Soft Sets (TSFSS) in capturing two-dimensional data. Data storage and processing in the modern technological landscape necessitate sophisticated algorithmic computations and robust representation of information. To this end, computational systems predominantly rely on a binary system structured on two fundamental states: 0 (off) and 1 (on). Moreover, the significance of energy resources and computer-aided tools is underscored in this domain, especially considering the potential impact of quantum computing and advances in energy resources on the evolution of digital technology. In addressing these complexities, we propose Complex T-Spherical Fuzzy Soft Relations (CTSFSRs), formulated through the Cartesian product of two CTSFSSs. These relations are adept at handling complex and uncertain data in real-world scenarios. We define and categorize various types of CTSFSRs, followed by an in-depth analysis of their outcomes. Additionally, this paper introduces innovative visualization techniques based on CTSFSRs, tailored for decision-making processes involving energy resources and quantum computing applications. The efficacy and applicability of these proposed methodologies are substantiated through comparative analysis with existing models.