Reconstruction of quantum states by applying an analytical optimization model

TL;DR

This paper introduces an analytical optimization model for quantum state reconstruction that improves accuracy with limited measurements, addressing issues of non-physical results and computational efficiency in quantum tomography.

Contribution

The study presents a novel analytical optimization approach that enhances quantum state reconstruction accuracy under measurement constraints, outperforming traditional methods.

Findings

Improved reconstruction accuracy with limited measurement samples.

Identification of multiple solutions depending on the state and measurement model.

Motivation for developing optimal algorithms tailored to specific experimental setups.

Abstract

When working with quantum states, analysis of the final quantum state generated through probabilistic measurements is essential. This analysis is typically conducted by constructing the density matrix from either partial or full tomography measurements of the quantum state. While full tomography measurement offers the most accurate reconstruction of the density matrix, limited measurements pose challenges for reconstruction algorithms, often resulting in non-physical density matrices with negative eigenvalues. This is often remedied using maximum likelihood estimators, which have a high computing time or by other estimation methods that decrease the reconstructed fidelity. In this study, we show that when restricting the measurement sample size, improvement over existing algorithms can be achieved. Our findings underline the multiplicity of solutions in the reconstruction problem, depending upon the generated state and measurement model utilized, thus motivating further research towards identifying optimal algorithms tailored to specific experimental contexts.