• Corpus ID: 244477889

Sublinear quantum algorithms for estimating von Neumann entropy

@article{Gur2021SublinearQA,
  title={Sublinear quantum algorithms for estimating von Neumann entropy},
  author={Tom Gur and Min-Hsiu Hsieh and Sathyawageeswar Subramanian},
  journal={Electron. Colloquium Comput. Complex.},
  year={2021},
  volume={28},
  pages={174}
}
Entropy is a fundamental property of both classical and quantum systems, spanning myriad theoretical and practical applications in physics and computer science. We study the problem of obtaining estimates to within a multiplicative factor γ > 1 of the Shannon entropy of probability distributions and the von Neumann entropy of mixed quantum states. Our main results are: In both cases, the input is assumed to have entropy bounded away from zero by a quantity determined by the parameter η > 0… 
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