This paper presents a distributed area autonomy load frequency control (DAA-LFC) method capable of balancing the interests of different grid operators and achieving fast frequency recovery. The method treats each area controller in a multiarea microgrid as an agent. During offline training, the agents enter into gameplay with each other to obtain a global optimization policy. The agents involved in this method are capable of independent decision-making and need not communicate with each other during online operation. In addition, this paper presents a distributed quantum multiagent deep meta-deterministic policy gradient (DQMA-DMDPG) algorithm, which employs both large-scale learning and meta-learning to achieve collaborative multitask learning by setting reasonable exploration parameters under different tasks. A quantum method is used to set the exploration action noise as a set of superposition states to obtain richer samples. These innovations deliver better performance in terms of frequency deviation and total generation cost, thus satisfying the requirements of different grid operators. A simulation based on a four-area microgrid of the China Southern Grid (CSG) demonstrates that the proposed method can simultaneously reduce the frequency deviation and power generation costs and balance the interests of multiple operators.