Luan, T.; Li, Z.; Zheng, C.; Kuang, X.; Yu, X.; Zhang, Z. Quantum Tomography: From Markovianity to Non-Markovianity. Symmetry2024, 16, 180.
Luan, T.; Li, Z.; Zheng, C.; Kuang, X.; Yu, X.; Zhang, Z. Quantum Tomography: From Markovianity to Non-Markovianity. Symmetry 2024, 16, 180.
Luan, T.; Li, Z.; Zheng, C.; Kuang, X.; Yu, X.; Zhang, Z. Quantum Tomography: From Markovianity to Non-Markovianity. Symmetry2024, 16, 180.
Luan, T.; Li, Z.; Zheng, C.; Kuang, X.; Yu, X.; Zhang, Z. Quantum Tomography: From Markovianity to Non-Markovianity. Symmetry 2024, 16, 180.
Abstract
Engineering of quantum computers requires reliable characterization of qubits, quantum operations, and even the entire hardware. Quantum tomography is an indispensable framework in
quantum characterization, verification, and validation (QCVV), which has been widely accepted
by researchers. According to the tomographic target, quantum tomography can be categorized
into quantum state tomography (QST), quantum process tomography (QPT), gate set tomography
(GST), process tensor tomography (PTT), and instrument set tomography (IST). Standard quantum tomography toolkits generally consist of basic linear inverse methods and statistical maximum
likelihood estimation (MLE) based methods. Furthermore, the performance of standard methods,
including effectiveness and efficiency, has been further developed by exploiting Bayesian estimation,
neural networks, matrix completion techniques, etc. In this review, we introduce the fundamental
quantum tomography techniques, including QST, QPT, GST, PTT, and IST. We first introduce
the details of basic linear inverse methods. Then, the framework of MLE methods with constraints
is summarized. Finally, we briefly introduce recent further research in developing the performance
of tomography. This review provides a primary getting-start in developing quantum tomography,
which promotes quantum computer development.
Keywords
Quantum computer; Quantum tomography
Subject
Physical Sciences, Quantum Science and Technology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
