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21 - Quantum tomography

from Part II - Quantum algorithmic primitives

Published online by Cambridge University Press:  03 May 2025

Alexander M. Dalzell ,
Sam McArdle ,
Mario Berta ,
Przemyslaw Bienias ,
Chi-Fang Chen ,
András Gilyén ,
Connor T. Hann ,
Michael J. Kastoryano ,
Emil T. Khabiboulline  and
Aleksander Kubica
...Show all authors
Alexander M. Dalzell
Affiliation:
AWS Center for Quantum Computing
Sam McArdle
Affiliation:
AWS Center for Quantum Computing
Mario Berta
Affiliation:
RWTH Aachen University
Przemyslaw Bienias
Affiliation:
AWS Center for Quantum Computing
Chi-Fang Chen
Affiliation:
University of California, Berkeley
András Gilyén
Affiliation:
HUN-REN Alfréd Rényi Institute of Mathematics
Connor T. Hann
Affiliation:
AWS Center for Quantum Computing
Michael J. Kastoryano
Affiliation:
University of Copenhagen
Emil T. Khabiboulline
Affiliation:
National Institute of Standards and Technology
Aleksander Kubica
Affiliation:
Yale University
Grant Salton
Affiliation:
Amazon Quantum Solutions Lab
Samson Wang
Affiliation:
Caltech
Fernando G. S. L. Brandão
Affiliation:
AWS Center for Quantum Computing
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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.

Keywords

tomographylearning quantum statesquantum state estimationentangled measurementsdevice characterizationshadow tomographyclassical shadowsquantum algorithmquantum algorithmic primitive
Type
Chapter
Information
Quantum Algorithms
A Survey of Applications and End-to-end Complexities
 , pp. 286 - 290
Publisher: Cambridge University Press
Print publication year: 2025
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This content is Open Access and distributed under the terms of the Creative Commons Attribution licence CC-BY-NC-ND 4.0 https://creativecommons.org/cclicenses/

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