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The task of collision-free navigation (CFN) of self-driving cars is anNP-hard problem usually tackled using Deep Reinforcement Learning (DRL). WhileDRL methods have proven to be effective, their implementation requiressubstantial computing resources and extended training periods to develop arobust agent. On the other hand, quantum reinforcement learning has recentlydemonstrated faster convergence and improved stability in simple,non-real-world environments.  In this work, we propose Nav-Q, the first quantum-supported DRL algorithm forCFN of self-driving cars, that leverages quantum computation for improving thetraining performance without the requirement for onboard quantum hardware.Nav-Q is based on the actor-critic approach, where the critic is implementedusing a hybrid quantum-classical algorithm suitable for near-term quantumdevices. 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. Ourempirical evaluations showcase that Nav-Q surpasses its classical counterpartin terms of training stability and, in certain instances, with respect to theconvergence rate. Furthermore, we assess Nav-Q in relation to effectivedimension, unveiling that the incorporation of a quantum component results in amodel with greater descriptive power compared to classical baselines. Finally,we evaluate the performance of Nav-Q using noisy quantum simulation, observingthat the quantum noise deteriorates the training performances but enhances theexploratory tendencies of the agent during training.

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