Optimal estimation of quantum processes using incomplete information: variational quantum process tomography

TL;DR

This paper introduces a variational quantum process tomography method that reconstructs quantum processes from noisy, incomplete data, unifying existing schemes and enhancing robustness in quantum process estimation.

Contribution

It develops a new variational approach to quantum process tomography that handles incomplete and noisy data, integrating various existing methods into a unified framework.

Findings

Successfully reconstructs quantum processes with incomplete data

Handles noisy measurements effectively

Unifies multiple quantum process tomography schemes

Abstract

We develop a quantum process tomography method, which variationally reconstruct the map of a process, using noisy and incomplete information about the dynamics. The new method encompasses the most common quantum process tomography schemes. It is based on the variational quantum tomography method (VQT) proposed by Maciel et al. in arXiv:1001.1793[quant-ph].