Self-Consistent Quantum Process Tomography

TL;DR

This paper introduces a self-consistent quantum process tomography method that accurately reconstructs quantum gates without assuming perfect state preparation and measurement, addressing systematic errors in practical quantum computing.

Contribution

It proposes a novel self-consistent tomography approach that does not rely on idealized assumptions, improving accuracy in realistic quantum gate characterization.

Findings

Standard process tomography is inaccurate with systematic errors.

The new method reconstructs an entire library of gates.

Linearization makes the optimization computationally feasible.

Abstract

Quantum process tomography is a necessary tool for verifying quantum gates and diagnosing faults in architectures and gate design. We show that the standard approach of process tomography is grossly inaccurate in the case where the states and measurement operators used to interrogate the system are generated by gates that have some systematic error, a situation all but unavoidable in any practical setting. These errors in tomography can not be fully corrected through oversampling or by performing a larger set of experiments. We present an alternative method for tomography to reconstruct an entire library of gates in a self-consistent manner. The essential ingredient is to define a likelihood function that assumes nothing about the gates used for preparation and measurement. In order to make the resulting optimization tractable we linearize about the target, a reasonable approximation when benchmarking a quantum computer as opposed to probing a black-box function.