Learning optimal quantum models is NP-hard
Physical modeling translates measured data into a physical model. Physical modeling is a major objective in physics and is generally regarded as a creative process. How good are computers at solving this task? Here, we show that in the absence of physical heuristics, the inference of optimal quantum...
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| Formato: | Artículo |
| Lenguaje: | English |
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American Physical Society
2018
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| Acceso en línea: | http://hdl.handle.net/1721.1/114464 https://orcid.org/0000-0002-7588-6796 |
| Sumario: | Physical modeling translates measured data into a physical model. Physical modeling is a major objective in physics and is generally regarded as a creative process. How good are computers at solving this task? Here, we show that in the absence of physical heuristics, the inference of optimal quantum models cannot be computed efficiently (unless P=NP). This result illuminates rigorous limits to the extent to which computers can be used to further our understanding of nature. |
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