Quantum speedup for active learning agents
Abstract
Can quantum mechanics help us in building intelligent robots and agents? One of the defining characteristics of intelligent behavior is the capacity to learn from experience. However, a major bottleneck for agents to learn in any real-life situation is the size and complexity of the corresponding task environment. Owing to, e.g., a large space of possible strategies, learning is typically slow. Even for a moderate task environment, it may simply take too long to rationally respond to a given situation. If the environment is impatient, allowing only a certain time for a response, an agent may then be unable to cope with the situation and to learn at all. Here we show that quantum physics can help and provide a significant speed-up for active learning as a genuine problem of artificial intelligence. We introduce a large class of quantum learning agents for which we show a quadratic boost in their active learning efficiency over their classical analogues. This result will be particularly relevant for applications involving complex task environments.
Cite
@article{arxiv.1401.4997,
title = {Quantum speedup for active learning agents},
author = {Giuseppe Davide Paparo and Vedran Dunjko and Adi Makmal and Miguel Angel Martin-Delgado and Hans J. Briegel},
journal= {arXiv preprint arXiv:1401.4997},
year = {2014}
}
Comments
Minor updates, 14 pages, 3 figures