Quantum-Assisted Agentic Learning: QAOA-Based Trust-Aware Coordination for Reliable Distributed Intelligence

Abstract

Distributed intelligent systems rely on many autonomous agents that train a shared model on their own data, and a single faulty or malicious agent can corrupt that model. This paper proposes Quantum-Assisted Agentic Learning (QAAL), in which a coordinator agent perceives the agreement between agents' updates, keeps a trust memory of each agent, and plans which agents to aggregate by solving a quadratic unconstrained binary optimisation (QUBO) problem with a constraint-preserving quantum approximate optimisation algorithm (QAOA). QAAL was evaluated in simulation with 12 agents, non-IID data, random dropouts and three Byzantine attacks on two classification tasks, against FedAvg, coordinate-wise median, trimmed mean and Multi-Krum. With 4 of 12 agents faulty, QAAL reached 83.4% mean accuracy versus 55.2% for FedAvg and 72.5% for trimmed mean, and matched Multi-Krum (84.0%). The QAOA planner found the optimal agent subset in 98.7% of 1260 training rounds, and its trust memory separated honest from faulty agents with AUC up to 0.996. At the 12-agent scale tested, classical solvers are equally accurate and far faster, so no quantum speed-up is claimed.