Tomography

Tian Luan, Zetong Li, Congcong Zheng +3 more

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.

Norbert Dragon

Shuixin Xiao, Yuanlong Wang, Jun Zhang +2 more

Quantum state tomography and quantum detector tomography are two main problems in quantum system identification. In this paper, we study state tomography and detector tomography with prior knowledge about the true rank of the unknown state/detector. In this case, we propose a sufficient condition for static (non-adaptive) tomography algorithms to achieve the optimal infidelity scaling O(1/N) on the number of state copies N. Then we present two examples of such static quantum state tomography and quantum detector tomography algorithms. Numerical examples demonstrate the effectiveness of our algorithms.

Devon Bowen

Jürgen Jost

Arturo Rojas-Santana, Gerard J. Machado, Maria V. Chekhova +2 more

Optical coherence tomography (OCT) is a 3D imaging technique that was introduced in 1991 [Science 254, 1178 (1991); Applied Optics 31, 919 (1992)]. Since 2018 there has been growing interest in a new type of OCT scheme based on the use of so-called nonlinear interferometers, interferometers that contain optical parametric amplifiers. Some of these OCT schemes are based on the idea of induced coherence [Physical Review A 97, 023824 (2018)], while others make use of an SU(1,1) interferometer [Quantum Science and Technology 3 025008 (2018)]. What are the differences and similarities between the output signals measured in standard OCT and in these new OCT schemes? Are there any differences between OCT schemes based on induced coherence and on an SU(1,1) interferometer? Differences can unveil potential advantages of OCT based on nonlinear interferometers when compared with conventional OCT schemes. Similarities might benefit the schemes based on nonlinear interferometers from the wealth of research and technology related to conventional OCT schemes. In all cases we will consider the scheme where the optical sectioning of the sample is obtained by measuring the output signal spectrum (spectral, or Fourier-domain OCT), since it shows better performance in terms of speed and sensitivity than its counterpart time-domain OCT.