APPLICATION OF BAYESIAN AND SOFTMAX BASED REPUTATION UPDATE MECHANISMS IN QUANTUM BLOCKCHAIN CONSENSUS (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.
Keywords: Quantum blockchain, consensus mechanism, reputation model, Bayesian updating, Softmax function, measurement error mitigation, quantum simulation.