Quantum Assemblage Tomography

TL;DR

This paper introduces a new method for quantum assemblage tomography that improves accuracy in reconstructing quantum states in asymmetric nonlocality protocols by combining conical optimization with maximum likelihood estimation.

Contribution

It presents a generalized loss model using conical optimization and Akaike's Information Criterion for more accurate quantum state assemblage reconstruction.

Findings

Outperforms standard methods in accuracy on experimental data

Effectively accounts for model complexity in quantum state reconstruction

Provides a robust framework for quantum steering protocols

Abstract

A central requirement in asymmetric quantum nonlocality protocols, such as quantum steering, is the precise reconstruction of state assemblages -- statistical ensembles of quantum states correlated with remote classical signals. Here we introduce a generalized loss model for assemblage tomography that uses conical optimization techniques combined with maximum likelihood estimation. Using an evidence-based framework based on Akaike's Information Criterion, we demonstrate that our approach excels in the accuracy of reconstructions while accounting for model complexity. In comparison, standard tomographic methods fall short when applied to experimentally relevant data.