A simple minimax estimator for quantum states

TL;DR

This paper introduces a simple minimax estimator for quantum states that outperforms maximum-likelihood methods in small-sample quantum tomography, ensuring full-rank estimates with a natural dependence on data size.

Contribution

The paper develops a new minimax-based estimator for quantum states that is simple, full-rank, and better suited for small data sets compared to traditional ML methods.

Findings

Outperforms ML estimator for small sample sizes in qubit tomography

Always produces full-rank quantum state estimates

Has a natural dependence on the number of measurements

Abstract

Quantum tomography requires repeated measurements of many copies of the physical system, all prepared by a source in the unknown state. In the limit of very many copies measured, the often-used maximum-likelihood (ML) method for converting the gathered data into an estimate of the state works very well. For smaller data sets, however, it often suffers from problems of rank deficiency in the estimated state. For many systems of relevance for quantum information processing, the preparation of a very large number of copies of the same quantum state is still a technological challenge, which motivates us to look for estimation strategies that perform well even when there is not much data. In this article, we review the concept of minimax state estimation, and use minimax ideas to construct a simple estimator for quantum states. We demonstrate that, for the case of tomography of a single qubit, our estimator significantly outperforms the ML estimator for small number of copies of the state measured. Our estimator is always full-rank, and furthermore, has a natural dependence on the number of copies measured, which is missing in the ML estimator.