Operational, gauge-free quantum tomography

TL;DR

This paper introduces an efficient gauge-free quantum tomography method that uses experimental observables as model parameters, solving the gauge ambiguity problem and enabling Bayesian inference with uncertainty quantification.

Contribution

It presents a novel operational tomography approach that addresses gauge issues and allows Bayesian analysis, applicable to various quantum characterization scenarios.

Findings

Successfully applied to process tomography, Ramsey interferometry, randomized benchmarking, and gate set tomography.

Enables incorporation of prior information and uncertainty quantification in quantum device characterization.

Provides a computationally efficient framework for robust quantum diagnostics.

Abstract

As increasingly impressive quantum information processors are realized in laboratories around the world, robust and reliable characterization of these devices is now more urgent than ever. These diagnostics can take many forms, but one of the most popular categories is tomography, where an underlying parameterized model is proposed for a device and inferred by experiments. Here, we introduce and implement efficient operational tomography, which uses experimental observables as these model parameters. This addresses a problem of ambiguity in representation that arises in current tomographic approaches (the gauge problem). Solving the gauge problem enables us to efficiently implement operational tomography in a Bayesian framework computationally, and hence gives us a natural way to include prior information and discuss uncertainty in fit parameters. We demonstrate this new tomography in a variety of different experimentally-relevant scenarios, including standard process tomography, Ramsey interferometry, randomized benchmarking, and gate set tomography.