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  • 21 - Quantum tomography

  • from Part II - Quantum algorithmic primitives
  • Alexander M. Dalzell, AWS Center for Quantum Computing, Sam McArdle, AWS Center for Quantum Computing, Mario Berta, RWTH Aachen University, Przemyslaw Bienias, AWS Center for Quantum Computing, Chi-Fang Chen, University of California, Berkeley, András Gilyén, HUN-REN Alfréd Rényi Institute of Mathematics, Connor T. Hann, AWS Center for Quantum Computing, Michael J. Kastoryano, University of Copenhagen, Emil T. Khabiboulline, National Institute of Standards and Technology, Aleksander Kubica, Yale University, Grant Salton, Amazon Quantum Solutions Lab, Samson Wang, Caltech, Fernando G. S. L. Brandão, AWS Center for Quantum Computing
  • Book:
    Quantum Algorithms
    Published online:
    03 May 2025
    Print publication:
    24 April 2025, pp 286-290
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  • Summary

    This chapter covers quantum tomography, a quantum algorithmic primitive that enables a quantum algorithm to learn a full classical description of a quantum state. Generally, the goal of a quantum tomography procedure is to obtain this description using as few copies of the state as possible. The optimal number of copies may depend on what kind of measurements are allowed and what error metric is being used, and in most cases, quantum tomography procedures have been developed with provably optimal complexity.