Simplified Quantum Process Tomography

TL;DR

This paper introduces an efficient quantum process tomography method that leverages prior knowledge and convex optimization to overcome the scalability issues of traditional approaches, enabling accurate characterization of quantum dynamics.

Contribution

It presents a simplified, scalable quantum process tomography technique using prior knowledge and convex optimization, improving accuracy and efficiency in specific physical scenarios.

Findings

Reduces quantum process tomography complexity

Enables incorporation of mixed environment states

Provides globally optimal Kraus operator estimates

Abstract

We propose and evaluate experimentally an approach to quantum process tomography that completely removes the scaling problem plaguing the standard approach. The key to this simplification is the incorporation of prior knowledge of the class of physical interactions involved in generating the dynamics, which reduces the problem to one of parameter estimation. This allows part of the problem to be tackled using efficient convex methods, which, when coupled with a constraint on some parameters allows globally optimal estimates for the Kraus operators to be determined from experimental data. Parameterising the maps provides further advantages: it allows the incorporation of mixed states of the environment as well as some initial correlation between the system and environment, both of which are common physical situations following excitation of the system away from thermal equilibrium. Although the approach is not universal, in cases where it is valid it returns a complete set of positive maps for the dynamical evolution of a quantum system at all times.