Abstract
In this article the randomized algorithm for estimating the von Neumann entropy of large-scale density matrices is considered. By capturing the dominant eigenspace via a k-rank approximation of the density matrix we estimate the entropy. We analyze the error bound with the eigenvalues of density matrix. Numerical experiments show that the proposed method is extensively efficient for large-scale density matrices.
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