量子环境中的学习代理框架
Framework for learning agents in quantum environments
摘要：
在本文中，我们提供了一个广泛的框架来描述一般量子环境中的学习代理。我们分析了经典指定的环境，允许量子增强学习的类型，通过对比环境量子预言。我们表明，量子改进是否完全可能取决于量子环境的内部结构。如果构建的环境和内部结构是适当的选择，或者如果代理有有限的能力来影响环境的内部状态，我们表明，学习时间的改善是可能的，在广泛的场景。我们把这种情况称为幸运偏好设置。构造环境的情况下，特别是相关的类基于模型的学习代理，我们的研究结果意味着一个接近通用的改进。
英文摘要：
In this paper we provide a broad framework for describing learning agents in general quantum environments. We analyze the types of classically specified environments which allow for quantum enhancements in learning, by contrasting environments to quantum oracles. We show that whether or not quantum improvements are at all possible depends on the internal structure of the quantum environment. If the environments are constructed and the internal structure is appropriately chosen, or if the agent has limited capacities to influence the internal states of the environment, we show that improvements in learning times are possible in a broad range of scenarios. Such scenarios we call luck-favoring settings. The case of constructed environments is particularly relevant for the class of model-based learning agents, where our results imply a near-generic improvement.
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