Published: September 22, 2016
Jacob M. Taylor, Hans Briegel, Vedran Dunjko
In this paper we provide a broad framework for describing learning agents in general quantum environments. We analyze the types of 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.
Citation: Physical Review Letters
Pub Type: Journals
Quantum Computation, Machine Learning, Quantum Information
Created September 22, 2016, Updated November 10, 2018