Quantum Learning for Autonomous Aircraft Control: A Reinforcement Learning Perspective

초록

Attitude control is vital for ensuring flight stability in aircraft operating under dynamic disturbances such as turbulence and vortices. While reinforcement learning (RL)-based controllers offer adaptivity to uncertain dynamics, their deployment on airborne platforms is often constrained by the large number of training parameters and associated computational overhead. To address this limitation, this article implements a quantum reinforcement learning (QRL)-based attitude controller in which a quantum neural network (QNN) replaces the classical neural network (NN) within an actor-critic framework. Exploiting quantum principles such as superposition and entanglement, the QRL controller reduces training parameters, thereby enhancing computational efficiency and enabling resilient, lightweight flight control for next-generation aircraft systems. The proposed controller is evaluated in a simulated flight-control environment that reflects the aerodynamic specifications of a Boeing B777-300X airframe and is subjected to stochastic vortical disturbances. The simulation results demonstrate that the QNN-based controller can reduce the number of training parameters by approximately 760x compared with a controller based on the classical NN. These results indicate that, within the realistic experimental setting, QRL can provide a parameter-efficient and computationally lightweight alternative for aircraft attitude control under uncertain aerodynamic conditions.

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