Quantum-Fuzzy Tensor Operators and Uncertainty-Band Bifurcation for Symmetry-Preserving State Discrimination

Year : 2026 | Volume : 02 | Issue : 01 | Page : 08 15
    By

    Tejas Bhushan N.B.,

  • Mohammed Almakki,

  • Mohammed El Khider,

  1. Research Student, Department of Chemistry, Regional Institute of Education (NCERT), Karnataka, India
  2. Assistant Professor, School of Engineering, Architecture and Interior Design, Amity University Dubai, Dubai, United Arab Emirates
  3. Assistant Professor, Department of General Undergraduate Curriculum Requirements, University of Dubai, Dubai, United Arab Emirates

Abstract

A tensor-operator framework is developed for fuzzy conjunction, fuzzy disjunction, and symmetry-preserving state discrimination in multi-qubit quantum systems. In this formulation, fuzzy membership and non-membership degrees are represented through expectations of effect operators acting on density matrices, providing a natural bridge between fuzzy logic and quantum measurement theory. Conjunction and disjunction operations are extended to the quantum domain via tensorised channels, constructed using projective measurements and unitary transformations, enabling logical aggregation directly on composite quantum states. To incorporate structural constraints, permutation-group symmetry is enforced through an operator-valued projector acting on the joint Hilbert space. This leads to the definition of a symmetry-defect functional, which quantifies deviations from exact invariance under qubit permutations. The framework thus separates symmetric components of the state from symmetry-breaking residuals, allowing controlled manipulation of both. An uncertainty-band family of perturbed quantum channels is introduced to model variability and noise in practical systems. Within this setting, fixed-point conditions are derived in operator form, characterizing states that remain invariant under repeated channel application. Furthermore, local bifurcation analysis reveals how qualitative changes in system behavior arise when channel parameters vary. In particular, the resulting branch equations show that nontrivial discrimination states emerge when a dominant eigenvalue of the channel crosses unity, signaling a transition from trivial to informative solutions. Illustrative two- and three-qubit examples demonstrate the practical implications of the theory. These cases show that small, controlled symmetry-breaking perturbations can enhance state discrimination performance while maintaining bounded symmetry loss. This highlights a key trade-off between symmetry preservation and functional effectiveness. Overall, the proposed framework provides a mathematically rigorous contribution in the RTM style, integrating tensor algebra, quantum channels, fuzzy logic, and uncertainty-band analysis. It offers a unified perspective for designing and analyzing symmetry-aware quantum information processes with controlled uncertainty.

Keywords: Quantum fuzzy logic; tensor operators; bifurcation; symmetry projector; multi-qubit gates; state discrimination

[This article belongs to Emerging Trends in Symmetry ]

How to cite this article:
Tejas Bhushan N.B., Mohammed Almakki, Mohammed El Khider. Quantum-Fuzzy Tensor Operators and Uncertainty-Band Bifurcation for Symmetry-Preserving State Discrimination. Emerging Trends in Symmetry. 2026; 02(01):08-15.
How to cite this URL:
Tejas Bhushan N.B., Mohammed Almakki, Mohammed El Khider. Quantum-Fuzzy Tensor Operators and Uncertainty-Band Bifurcation for Symmetry-Preserving State Discrimination. Emerging Trends in Symmetry. 2026; 02(01):08-15. Available from: https://journals.stmjournals.com/etsy/article=2026/view=247454


References

  1. Zadeh LA. Fuzzy sets. Inf Control. 1965;8(3):338-353. doi:10.1016/S0019-9958(65)90241-X.
  2. Atanassov KT. Intuitionistic fuzzy sets. Fuzzy Sets Syst. 1986;20(1):87-96. doi:10.1016/S0165-0114(86)80034-3.
  3. Feynman RP. Simulating physics with computers. Int J Theor Phys. 1982;21(6-7):467–488. doi:10.1007/BF02650179.
  4. Acampora G. Quantum machine intelligence. Quantum Mach Intell. 2019;1(1-2):1–3. doi:10.1007/s42484-019-00006-5.
  5. Ladd TD, Jelezko F, Laflamme R, Nakamura Y, Monroe C, OBrien JL. Quantum computers. Nature. 2010;464(7285):45-53. doi:10.1038/nature08812.
  6. Aaronson S. Read the fine print. Nat Phys. 2015;11(4):291-293. doi:10.1038/nphys3272.
  7. Preskill J. Quantum computing in the NISQ era and beyond. Quantum. 2018;2:79. doi:10.22331/q-2018-08-06-79.
  8. Yogeesh N, Girija DK, Rashmi M, Divyashree J. Quantum implementation of fuzzy logic conjunction and disjunction using multi-qubit gates. Eur Chem Bull. 2023;12(7):137. doi:10.48047/ecb/2023.12.7.137.
  9. Yogeesh N. Intuitionistic fuzzy hypergraphs and their operations. In: Applied Computer Vision and Soft Computing with Interpretable AI. Boca Raton: Chapman and Hall/CRC; 2023. doi:10.1201/9781003359456-10.
  10. Yogeesh N, Raja N, Hema K, et al. Intuitionistic fuzzy scoring for fluency in telepractice sessions. Int J Appl Math. 2024;38(3S):Article 206. doi:10.12732/ijam.v38i3s.206.
  11. Yogeesh N, Raja N. A mathematical fuzzy model for syntax-pragmatics interface. Forum Linguist Stud. 2025;7(6):9618. doi:10.30564/fls.v7i6.9618.
  12. Yogeesh N, Raja N, Rashmi M, Girija DK. Improving speech privacy with fuzzy logic-based encryption. In: Proceedings of the International Conference on Intelligent Data Engineering and Analytics. 2023. doi:10.1109/ICIDeA59866.2023.10295183.
  13. Yogeesh N, Raja N, Rashmi M, Girija DK. Robust speech processing with fuzzy logic-driven anti-spoofing techniques. In: Proceedings of the International Conference on Smart Systems and Advanced Computing. 2023. doi:10.1109/ICSSAS57918.2023.10331804.
  14. Yogeesh N, Raja N. Fuzzy logic-based beat tracking in music signals. GI Forum. 2023;11(1):Article 343. doi:10.15463/gfbm-mib-2023–343.
  15. Yogeesh N, Karthik M, Vasudevan A, et al. Global bifurcation for nonlinear operators with uncertainty bands. Global Stoch Anal. 2025;12(6):59–76. doi:10.64837/GSA.12.6.5.
  16. Yogeesh N, Karthik M, Vasudevan A, et al. Invariant manifolds for nonlinear flows with uncertainty-aware cone conditions. Global Stoch Anal. 2026;13(1):1–18. doi:10.64837/GSA.13.1.1.
  17. Yogeesh N. Applying fuzzy data science in generative AI for healthcare. In: Advanced Intelligent Healthcare Systems. Hoboken: Wiley; 2025. doi:10.1002/9781394302932.ch10.
  18. Aburub FAF, Yogeesh N, Mohammad SIS, Raja N, Lingaraju L, William P, et al. A comprehensive algebraic framework for fuzzy graphs and their operators. J Posthumanism. 2024;4(3):929–963. doi:10.63332/joph.v4i3.430.
  19. Yogeesh N, Mohammad SI, Raja N, Chetana R, William P, Vasudevan A, et al. From crisp to fuzzy: A comparative review of statistical and fuzzy approaches to problem solving. Appl Math Inf Sci. 2025;19(3):647-658. doi:10.18576/amis/190313.
  20. Mohammad SI, Yogeesh N, Raja N, Chetana R, William P, Vasudevan A, et al. The synergy of simplicity and vagueness: Exploring simple statistics in fuzzy mathematical frameworks. Appl Math Inf Sci. 2025;19(2):457–465. doi:10.18576/amis/190219.
  21. Yogeesh N. Fuzzy logic in metaverse technology roadmap: Adaptive future systems. In: Fuzzy Logic in the Metaverse. Hoboken: Wiley; 2026. doi:10.1002/9781394272228.ch14.
  22. Saleh IA, Srivastava A. Exploring the intersection of fuzzy logic and quantum logic: A new frontier in nonclassical logics. SciWaveBulletin. 2023;1(2):27–34. doi:10.61925/SWB.2023.1204.
  23. Samaila B, Sekar C. Quantum power flow: Revolutionizing power systems analysis. SciWaveBulletin. 2023;1(2):19. doi:10.61925/SWB.2023.1201.
  24. Giruba M, Al Ansari MS. Quantum field theory: Advanced calculations in nonperturbative regimes. SciWaveBulletin. 2023;1(4):1–8. doi:10.61925/SWB.2023.1401.
  25. Bellman RE, Zadeh LA. Decision-making in a fuzzy environment. Manage Sci. 1970;17(4):B141–B164. doi:10.1287/mnsc.17.4.B141.

Regular Issue Subscription Review Article
Volume 02
Issue 01
Received 14/03/2026
Accepted 22/04/2026
Published 30/04/2026
Publication Time 47 Days


Login


My IP

PlumX Metrics