(untitled) (Eureka Journal of Artificial Intelligence and Data Innovation, 2026)
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.