Quantum Federated Lasso With Anonymous Consensus Built Upon Quantum Blockchain

Objective With the rapid advancement of quantum computing technologies, Quantum Federated Learning (QFL) has emerged as a promising framework for privacy‐preserving quantum machine learning (QML). QFL provides a promising approach for privacy‐preserving quantum machine learning by enabling collaborative model training without sharing raw data. However, malicious or unreliable quantum clients may submit poisoned or low‐quality models, threatening the reliability of global aggregation. This study aims to develop a secure and robust QFL framework against such adversarial behaviors. Methods We propose a novel Quantum Federated Lasso (QFLasso) framework built on a quantum blockchain infrastructure. Local quantum models are trained with Lasso regularization to achieve sparse representations, and model updates are recorded on the blockchain. Quantum validators verify the quality and integrity of submitted models before aggregation. An anonymous quantum consensus mechanism is further introduced to ensure reliable agreement while preserving validator anonymity. Result The proposed framework integrates quantum Lasso optimization, federated learning, blockchain‐based verification, and anonymous quantum consensus to enhance the security, robustness, and privacy preservation of distributed quantum learning systems. The validator verification mechanism effectively filters unreliable model updates, while the anonymous consensus protocol improves resistance against dishonest or colluding validators and prevents targeted attacks on individual participants. Conclusion QFLasso provides a secure and scalable architecture for privacy‐preserving quantum intelligence, offering potential applications in secure quantum finance, distributed quantum sensing, and collaborative quantum medical diagnosis.