Learning optimal quantum models is NP-hard
Author(s)
Stark, Cyril
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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.
Date issued
2018-02Department
Massachusetts Institute of Technology. Center for Theoretical PhysicsJournal
Physical Review A
Publisher
American Physical Society
Citation
Stark, Cyril J. et al. "Learning optimal quantum models is NP-hard. " Physical Review A 97, 2 (February 2018): 020103(R) © 2018 American Physical Society
Version: Final published version
ISSN
2469-9926
2469-9934