Renormalized quantum tomography

Giacomo Mauro D’Ariano, Matteo G. A. Paris, Massimiliano Federico Sacchi

Jong Yeon Lee, Olivier Landon-Cardinal

We improve upon a recently introduced efficient quantum state reconstruction procedure targeted to states well approximated by the multiscale entanglement renormalization ansatz (MERA), e.g., ground states of critical models. We show how to numerically select a subset of experimentally accessible measurements which maximize information extraction about renormalized particles, thus dramatically reducing the required number of physical measurements. We numerically estimate the number of measurements required to characterize the ground state of the critical one-dimensional Ising (resp. XX) model and find that MERA tomography on 16-qubit (resp. 24-qubit) systems requires the same experimental effort as brute-force tomography on 8 qubits. We derive a bound computable from experimental data which certifies the distance between the experimental and reconstructed states.

I. A. Luchnikov, Mikhail Krechetov, Sergey N. Filippov

Optimization methods on Riemannian manifolds are a powerful class of optimization methods that allow performing constrained optimization. We apply first-order Riemannian optimization methods to various problems of quantum physics including entanglement renormalization of local many-body Hamiltonians, quantum control, and quantum tomography. We show that the Riemannian optimization forms a new powerful numerical tool for solving different problems of quantum technologies. Besides, we provide a package written on top of TensorFlow for the Riemannian optimization in quantum physics

Giacomo Mauro D’Ariano, Matteo G. A. Paris, Massimiliano Federico Sacchi

Alexander M. Dalzell, Sam McArdle, Mario Berta +10 more

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.