Abstract: Apparatuses, systems, and methods described herein include receiving by a quantum hybrid branch simulation engine, a request for a next task by a branch personnel or worker. The branch simulation engine may receive data representing a simulated branch environment and apply a deep reinforcement learning (DRL) staffing operations model to predict a next action or task. The model may weigh impacts of a reward to substantially maximize the reward in light of a policy to determine the next action and update a database to store/represent a next state of the simulated or real-life branch environment after the next action is performed.