The Vector representation of the Standard Quantum Process Tomography

TL;DR

This paper develops a vector-based framework for standard quantum process tomography, proving the uniqueness of solutions and optimizing the process using symmetric informationally complete POVMs for improved accuracy.

Contribution

It introduces a self-consistent vector scheme for solving the linear equations in SQPT and demonstrates the optimality of symmetric IC-POVMs for input preparation and measurement.

Findings

Proves the uniqueness of the solution to the linear equations in SQPT.

Develops a vector-based representation for quantum channels.

Shows that symmetric IC-POVMs optimize the process tomography accuracy.

Abstract

The characterization of the evolution of a quantum system is one of the main tasks to accomplish to achieve quantum information processing. The standard quantum process tomography (SQPT) has the unique property that it can be applied without introducing any additional quantum resources. In present work, we shall focus on the following two topics about the SQPT. At first, in the SQPT protocol for a $d$-dimensional system, one should encounter a problem in solving of a set of $d^4$ linear equations in order to get the matrix containing the complete information about the unknown quantum channel. Until now, the general form of the solution is unknown. And a long existed conviction is that the solutions are not unique. Here, we shall develop a self-consistent scheme, in which bounded linear operators are presented by vectors, to construct the set of linear equations. With the famous Cramer's rule for the set of linear equations, we are able to give the general form of the solution and prove that it is unique. In second, the central idea of the SQPT is to prepare a set of linearly independent inputs and measuring the outputs via the quantum state tomography (QST). Letting the inputs and the measurements be prepared by two sets of the rank-one positive-operator-valued measures [POVMs], where each POVM is supposed to be linearly independent and informationally complete (IC), we observe that SQPT now is equivalent to deciding a unknown state with a set of product IC-POVM in the $d^2$-dimensional Hilbert space. Following the general linear state tomography theory, we show that the product symmetric IC-POVM should minimize the mean-square Hilbert-Schmidt distance between the estimator and the true states. So, an optimal SQPT can be realized by preparing both the inputs and the measurements as the symmetric IC-POVM.