| Autor: |
Giuseppe Davide Paparo, Vedran Dunjko, Adi Makmal, Miguel Angel Martin-Delgado, Hans J. Briegel |
| Jazyk: |
angličtina |
| Rok vydání: |
2014 |
| Předmět: |
|
| Zdroj: |
Physical Review X, Vol 4, Iss 3, p 031002 (2014) |
| Druh dokumentu: |
article |
| ISSN: |
2160-3308 |
| DOI: |
10.1103/PhysRevX.4.031002 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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