Framework for learning agents in quantum environments
This work addresses the problem of optimizing learning in quantum systems for researchers in quantum machine learning, but it appears incremental as it builds on existing concepts like quantum oracles.
The paper tackles the problem of understanding when learning agents can achieve quantum enhancements in quantum environments, showing that improvements in learning times are possible in luck-favoring settings, with near-generic improvements for model-based agents.
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.