APPLICATION OF BAYESIAN AND SOFTMAX BASED REPUTATION UPDATE MECHANISMS IN QUANTUM BLOCKCHAIN CONSENSUS

Authors

  • Muhamediyeva D. T.

    Tashkent Institute of Irrigation and Agricultural Mechanization Engineers National Research University

    Author

  • Tagayev F. A.

    Tashkent Institute of Irrigation and Agricultural Mechanization Engineers National Research University

    Author

Keywords:
Quantum blockchain, consensus mechanism, reputation model, Bayesian updating, Softmax function, measurement error mitigation, quantum simulation.
Abstract

This paper proposes and analyzes two probabilistic models for dynamically updating validator reputation within a quantum-based blockchain consensus mechanism: Bayesian updating and Softmax-based weighting methods. In the proposed framework, the voting process is organized using a commit–reveal scheme, while the collective decision mechanism is modeled using a GHZ-type entangled quantum state. Quantum measurement results are mitigated using per-qubit calibration matrices, after which the quantum consensus probability is calculated. The final decision is determined through a combination of the quantum result and a classical weighted fallback mechanism. Validator reputations are updated according to their agreement with the final decision using either Bayesian inference or a temperature-controlled Softmax function. Simulation results demonstrate that the reputation mechanism enables the gradual identification of Byzantine behavior and improves consensus stability. The proposed approach provides a conceptual framework for strengthening blockchain security by integrating quantum and classical probabilistic models.

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Published
2026-03-15
Issue
Vol. 2 No. 3 (2026)
Section
Articles
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How to Cite

APPLICATION OF BAYESIAN AND SOFTMAX BASED REPUTATION UPDATE MECHANISMS IN QUANTUM BLOCKCHAIN CONSENSUS. (2026). Eureka Journal of Artificial Intelligence and Data Innovation, 2(3), 24-36. https://eurekaoa.com/index.php/11/article/view/599

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