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Framework for learning agents in quantum environments

In this paper we provide a broad framework for describing learning agents in general quantum environments. We analyze the types of classically speci ed 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 in uence 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.


The capacity to learn, and to adapt to unknown environments is one of the quintessential characteristics of intelligent agents. The development of learning models has, consequently, been one of the central areas of research in the broad eld of arti cial intelligence (AI) from its very beginnings.
Nonetheless, the central goal of AI, the design of autonomous, intelligent learning agents is still far from realized. In contrast, more computation-orientated aspects of so-called applied AI, for instance machine learning, classi cation, pattern recognition, even game playing (e.g. computer chess), have continuously progressed over the years, despite occasional set-backs.
The eld of quantum information processing (QIP), which has had a profound impact on computer and communication sciences, has also recently started directly in uencing such applied aspects of AI. For example, the theory and algorithms for...

Read more, download the paper below (PDF 39 pages)
1507_08482v1.pdf 1507.08482v1.pdf  (727.91 Ko)

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