Quantum computing should be applied to robots, automatons, and other agents that use AI to make them more creative and to learn and respond faster than conventional robots, researchers from the Complutense University of Madrid (UCM) and the University of Innsbruck (Austria) recommend.

In a study in the journal ‘Physical Review X’ modeling the use of quantum physics in future robots (and other agents), they demonstrate that quantum machines can quickly adapt to situations compared to slower conventional robots, which are limited by the size and complexity of the task environment.

“In the case of very demanding … environments, the quantum robot can adapt itself and survive, while the classic robot is destined to collapse,” says G. Davide Paparo and Miguel A. Martín-Delgado, two researchers from UCM who have participated in the study. Their theoretical work has focused on using quantum computing for machine learning.

“Building a model is actually a creative act, but conventional computers are no good at it,” says Martín-Delgado. “The advances [quantum computing] brings are not only quantitative in terms of greater speed, but also qualitative: adapting better to environments where the classic agent does not survive. This means that quantum robots are [in models] more creative.”

UPDATE Oct. 8, 2014 — Clarified that their researchers’ claims are only supported by models.

Abstract of Quantum Speedup for Active Learning Agents

Can quantum mechanics help us build intelligent learning agents? A defining signature of intelligent behavior is the capacity to learn from experience. However, a major bottleneck for agents to learn in real-life situations is the size and complexity of the corresponding task environment. Even in a moderately realistic 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 quadratic speedup for active learning as a genuine problem of artificial intelligence. This result will be particularly relevant for applications involving complex task environments.