Jincheng Zhang
This paper proposes a novel distributed consensus algorithm inspired by quantum mechanics, termed the Quantum-Inspired Distributed Consensus Algorithm with Measurement-Based Feedback (QIDCA-MBF). The core idea is to utilize the principles of quantum superposition to accelerate the convergence of distributed consensus in challenging network environments, particularly those prone to node failures. Unlike traditional consensus algorithms, QIDCA-MBF employs probabilistic representations of proposed values within each node, mimicking the concept of quantum superposition. A key innovation is the incorporation of measurement-based feedback, modeled after quantum measurement, to collapse these superpositions and guide the nodes towards a shared consensus value. This feedback mechanism dynamically adapts to the network topology and detects node failures, significantly enhancing the algorithm's robustness and convergence speed. The algorithm is formulated based on a modified averaging process, incorporating probabilistic weights derived from the superposition states. Simulation results demonstrate the effectiveness of QIDCA-MBF in achieving consensus rapidly and reliably, outperforming conventional distributed consensus protocols under various failure scenarios. The algorithm's adaptability and resilience make it a promising candidate for applications in decentralized systems, sensor networks, and blockchain technologies.