Nav-Q: Quantum Deep Reinforcement Learning for Collision-Free Navigation of Self-Driving Cars.

The challenge of collision-free navigation (CFN) for self-driving cars is an NP-hard problem addressed through Deep Reinforcement Learning (DRL). Despite the effectiveness of DRL methods, their application demands significant computing resources and prolonged training periods to establish a resilient agent. On the other hand, quantum reinforcement learning algorithms have recently demonstrated faster convergence and improved stability in simple, non-real-world environments. However, their application in the real-world CFN domain has not been explored, and their direct adaptation would require a quantum computing device onboard the vehicle for testing. In this work, we propose Nav-Q, the first quantum-supported DRL algorithm for CFN of self-driving cars, that leverages quantum computation for improving the training performance without the requirement for onboard quantum hardware. Nav-Q is based on the actor-critic approach, where the critic is implemented using a hybrid quantum-classical algorithm suitable for near-term quantum devices. We assess the performance of Nav-Q using the CARLA driving simulator, a de facto standard benchmark for evaluating state-of-the-art DRL methods. Our empirical evaluations showcase that Nav-Q surpasses its classical counterpart not only in terms of training stability but also, in certain instances, with respect to the convergence rate when analyzing the Reward vs. Episode curve. This enhancement is accomplished without negatively impacting the learned policy by the agent. Furthermore, we assess Nav-Q in relation to effective dimension, unveiling that the incorporation of a quantum component results in a model possessing greater descriptive power compared to classical baselines. Finally, we evaluate the performance of Nav-Q using noisy quantum simulation, observing that the quantum noise enhances the exploratory tendencies of the agent during training.