In this work, we utilize Quantum Deep Reinforcement Learning as method to learn navigation tasks for a simple, wheeled robot in three simulated environments of increasing complexity. We show similar performance of a parameterized quantum circuit trained with well established deep reinforcement learning techniques in a hybrid quantum-classical setup compared to a classical baseline. To our knowledge this is the first demonstration of quantum machine learning (QML) for robotic behaviors. Thus, we establish robotics as a viable field of study for QML algorithms and henceforth quantum computing and quantum machine learning as potential techniques for future advancements in autonomous robotics. Beyond that, we discuss current limitations of the presented approach as well as future research directions in the field of quantum machine learning for autonomous robots.

翻译：在这项工作中,我们用量子深层强化学习作为方法,在三个日益复杂的模拟环境中,为一个简单、轮式机器人学习导航任务。我们展示了一种参数化量子电路的类似性能,在混合量子古典结构中,与古典基线相比,该参数化量子电路经过成熟的深度强化学习技术培训。据我们所知,这是首次为机器人行为演示量子机器学习(QML)。因此,我们将机器人作为QML算法的可行研究领域,并因此将量子计算和量子机器学习作为自主机器人未来进步的潜在技术。此外,我们讨论了目前采用的方法的局限性以及自主机器人量子机器学习领域今后的研究方向。