Learning optimal quantum models is NP-hard
Name
PhysRevA.97.020103.pdf
Size
186.54 KB
Format
Adobe PDF
Checksum (MD5)
ea90872caed92ae9850f06948a10b206
Author(s)
Stark, Cyril
Date Issued
February 2018
Journal
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
Abstract
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.
MIT Department
Massachusetts Institute of Technology. Center for Theoretical Physics
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
https://doi.org/10.1103/PhysRevA.97.020103
