论文标题
量子增强学习的调查
A Survey on Quantum Reinforcement Learning
论文作者
论文摘要
量子增强学习是量子计算与机器学习交集的新兴领域。尽管我们打算对量子增强学习的文献进行广泛的概述 - 我们对本术语的解释将在下面阐明 - 我们特别强调了最近的发展。侧重于已经可用的嘈杂的中间量子量子设备，其中包括在原本经典的增强学习设置中充当函数近似器的变异量子电路。此外，我们根据未来容易耐受的硬件进行了量子增强学习算法的调查，其中一些具有可证明的量子优势。我们既可以提供该领域的鸟眼观看，也提供了文献选定部分的摘要和评论。
Quantum reinforcement learning is an emerging field at the intersection of quantum computing and machine learning. While we intend to provide a broad overview of the literature on quantum reinforcement learning - our interpretation of this term will be clarified below - we put particular emphasis on recent developments. With a focus on already available noisy intermediate-scale quantum devices, these include variational quantum circuits acting as function approximators in an otherwise classical reinforcement learning setting. In addition, we survey quantum reinforcement learning algorithms based on future fault-tolerant hardware, some of which come with a provable quantum advantage. We provide both a birds-eye-view of the field, as well as summaries and reviews for selected parts of the literature.
