Maximum-Likelihood Quantum State Tomography by Cover's Method with Non-Asymptotic Analysis

TL;DR

This paper introduces an iterative maximum-likelihood quantum state tomography algorithm with proven convergence rates and computational complexity, improving accuracy without requiring parameter tuning.

Contribution

It presents a novel iterative algorithm for quantum state tomography with non-asymptotic error bounds and computational efficiency, advancing existing methods.

Findings

Convergence rate of $O((1/k) \, ext{log} D)$ for the optimization error.

Per-iteration complexity of $O(D^3 + N D^2)$.

Parameter-free correction of the RρR method.

Abstract

We propose an iterative algorithm that computes the maximum-likelihood estimate in quantum state tomography. The optimization error of the algorithm converges to zero at an $O ( ( 1 / k ) \log D )$ rate, where $k$ denotes the number of iterations and $D$ denotes the dimension of the quantum state. The per-iteration computational complexity of the algorithm is $O ( D ^ 3 + N D ^2 )$, where $N$ denotes the number of measurement outcomes. The algorithm can be considered as a parameter-free correction of the $R \rho R$ method [A. I. Lvovsky. Iterative maximum-likelihood reconstruction in quantum homodyne tomography. \textit{J. Opt. B: Quantum Semiclass. Opt.} 2004] [G. Molina-Terriza et al. Triggered qutrits for quantum communication protocols. \textit{Phys. Rev. Lett.} 2004.].