Davide Paparo, GiuseppeDunjko, VedranMakmal, AdiMartín-Delgado Alcántara, Miguel ÁngelBriegel, Hans J.2023-06-192023-06-192014-06-082160-330810.1103/PhysRevX.4.031002https://hdl.handle.net/20.500.14352/35595©2014 American Physical Society. M. A. M.-D. acknowledges support by the Spanish MICINN Grants No. FIS2009-10061 and No. FIS2012- 33152, the CAM Research Consortium QUITEMAD S2009-ESP-1594, the European Commission PICC: FP7 2007-2013, Grant No. 249958, and the UCM-BS Grant No. GICC-910758. H. J. B. acknowledges support by the Austrian Science Fund (FWF) through the SFB FoQuS F 4012, and the Templeton World Charity Fund grant TWCF0078/AB46. G. D. P. and V. D. have contributed equally to this work.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 reallife 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.engjournal articlehttp://dx.doi.org/10.1103/PhysRevX.4.031002https://journals.aps.orgopen access53ComputationAlgorithmsNetworkGoogle.Física-Modelos matemáticos