We present an algorithm for projecting superoperators onto the set of completely positive, trace-preserving maps. When combined with gradient descent of a cost function, the procedure results in an algorithm for quantum process tomography: finding the quantum process that best fits a set of sufficient observations. We compare the performance of our algorithm to the diluted iterative algorithm as well as second-order solvers interfaced with the popular CVX package for MATLAB, and find it to be significantly faster and more accurate while guaranteeing a physical estimate.
@article{arxiv.1803.10062,
title = {Quantum process tomography via completely positive and trace-preserving projection},
author = {George C. Knee and Eliot Bolduc and Jonathan Leach and Erik M. Gauger},
journal= {arXiv preprint arXiv:1803.10062},
year = {2019}
}