Sample-Optimal Tomography of Quantum States

By Jeongwan Haah, Aram Wettroth Harrow, Zhengfeng Ji, Xiaodi Wu, Nengkun Yu. IEEE Transactions on Information Theory, 2015

470 citations recorded by the source corpus.

It is a fundamental problem to decide how many copies of an unknown mixed quantum state are necessary and sufficient to determine the state. Previously, it was known only that estimating states to error <inline-formula> <tex-math notation="LaTeX">$\epsilon $ </tex-math></inline-formula> in trace distance required <inline-formula> <tex-math notation="LaTeX">$O(dr^{2}/\epsilon ^{2})$ </tex-math></inline-formula> copies for a <inline-formula> <tex-math notation="LaTeX">$d$ </tex-math></inline-formula>-dimensional density matrix of rank <inline-formula> <tex-math notation="LaTeX">$r$ </tex-math></inline-formula>. Here, we give a theoretical measurement scheme (POVM) that requires <inline-formula> <tex-math notation="LaTeX">$O (dr/ \delta) \ln ~(d/\delta) $ </tex-math></inline-formula> copies to estimate <inline-formula> <tex-math notation="LaTeX">$\rho $ </tex-math></inline-formula> to error <inline-formula> <tex-math notation="LaTeX">$\delta $ </tex-math></inline-formula> in infidelity, and a matching lower bound up to logarithmic factors. This implies <inline-formula> <tex-math notation="LaTeX">$O((dr / \epsilon ^{2}) \ln ~(d/\epsilon))$ </tex-math></inline-formula> copies suffice to achieve error <inline-formula> <tex-math notation="LaTeX">$\epsilon $ </tex-math></inline-formula> in trace distance. We also prove that for independent (product) measurements, <inline-formula> <tex-math notation="LaTeX">$\Omega (dr^{2}/\delta ^{2}) / \ln (1/\delta)$ </tex-math></inline-formula> copies are necessary in order to achieve error <inline-formula> <tex-math notation="LaTeX">$\delta $ </tex-math></inline-formula> in infidelity. For fixed <inline-formula> <tex-math notation="LaTeX">$d$ </tex-math></inline-formula>, our measurement can be implemented on a quantum computer in time polynomial in <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula>.

Subfields: Artificial Intelligence, Atomic and Molecular Physics, and Optics

View Paper: https://www.semanticscholar.org/paper/de247019a9a3cb9362105068f90f30738066f8f7

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In Artificial Intelligence

  1. Hsin-Yuan Huang
  2. John Preskill
  3. Richard Kueng
  4. Scott Aaronson
  5. Ivan H. Deutsch
  6. Ashley Montanaro
  7. Aram Wettroth Harrow
  8. Barbara M. Terhal

In Atomic and Molecular Physics, and Optics

  1. Hsin-Yuan Huang
  2. Richard Kueng
  3. John Preskill
  4. Scott Aaronson
  5. L. Susskind
  6. D. Stanford
  7. Ivan H. Deutsch
  8. D. Harlow

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