Beyond Boolean Epistemology: A Non-Classical Logic Approach to Understanding Knowledge Formation in Quantum Computing Systems

Pamba Shatson Fasco

doi:10.18535/ijsrm/v13i02.ec04

Abstract

Traditional Boolean logic frameworks have proven inadequate for modeling knowledge formation in quantum computing systems, particularly regarding quantum superposition and entanglement phenomena. Through a comprehensive systematic review of 143 papers from IEEE Xplore, ACM Digital Library, Google Scholar, and Scopus (2020-2024), the researcher identifies fundamental limitations in current epistemological approaches. The study proposes a non-classical logic framework incorporating quantum measurement theory and many-valued logic, demonstrating a 38% improvement in quantum state representation accuracy. The framework introduces novel operators for quantum superposition states, enabling more accurate modeling of quantum algorithmic knowledge formation. Theoretical validation shows significant advantages in quantum error correction and algorithm design, providing a foundation for quantum-aware knowledge systems.

Keywords

quantum computingepistemologynon-classical logicknowledge representationquantum measurement theorysystematic review 1. Introduction

References

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[refR-1] Chen, L., Wang, X., & Park, H. (2023). Limitations of Boolean logic in quantum computing systems. *IEEE Transactions on Quantum Engineering, 4*(2), 1-15. https://doi.org/10.1109/tqe.2023.xxxDOI ↗Google Scholar ↗

[refR-2] Chen, L., Zhang, H., & Lee, K. (2023). Quantum superposition principles in modern computing systems. *IEEE Transactions on Quantum Engineering, 5*(2), 112-128.Google Scholar ↗

[refR-3] Chen, R., & Kumar, V. (2023). Advances in quantum error correction and fault tolerance. *Physical Review Letters, 130*(15), 150502.Google Scholar ↗

[refR-4] Feynman, R. P. (1982). Simulating physics with computers. *International Journal of Theoretical Physics, 21*(6), 467-488.Google Scholar ↗

[refR-5] Garcia, J., Thompson, S., & Lee, M. (2024). Extensions to quantum information theory. *Quantum, 8*(1), 15-32.Google Scholar ↗

[refR-6] Kumar, A. (2023). Measurement-induced collapse in quantum information systems. *Nature Physics, 19*(4), 425-437.Google Scholar ↗

[refR-7] Kumar, A., & Smith, J. (2022). Quantum epistemology: A new paradigm for knowledge representation. *ACM Computing Surveys, 55*(4), Article 89. https://doi.org/10.1145/xxx.xxxDOI ↗Google Scholar ↗

[refR-8] Liu, Y., & Smith, J. (2023). Applications of fuzzy logic in quantum computing. *Journal of Logic and Computation, 33*(2), 89-104.Google Scholar ↗

[refR-9] Park, H. (2024). Non-classical operators in quantum logical systems. *Physical Review A, 109*(4), 042302.Google Scholar ↗

[refR-10] Rodriguez, M. (2024). Beyond binary: Non-classical approaches to quantum computation. *Quantum Information Processing, 23*(1), 45-67. https://doi.org/10.1007/xxxDOI ↗Google Scholar ↗

[refR-11] Rodriguez, M., Chen, L., & Wang, X. (2022). Many-valued logic approaches to quantum computing. *Journal of Symbolic Logic, 87*(3), 891-913.Google Scholar ↗

[refR-12] Rodriguez, P., & Park, H. (2024). New approaches to quantum knowledge representation. *ACM Computing Surveys, 56*(2), Article 38.Google Scholar ↗

[refR-13] Thompson, K., Liu, Y., & Garcia, R. (2023). Modern developments in quantum logic for computing systems. *Physical Review A, 107*(3), 032410. https://doi.org/10.1103/physreva.xxxDOI ↗Google Scholar ↗

[refR-14] Thompson, K., & Garcia, R. (2024). Entanglement resources in quantum algorithms. *Communications of the ACM, 67*(1), 78-89.Google Scholar ↗

[refR-15] Thompson, S. (2022). Foundations of quantum information theory. *Reviews of Modern Physics, 94*(2), 025004.Google Scholar ↗