Estimation of pure quantum states in high dimension at the limit of quantum accuracy through complex optimization and statistical inference

TL;DR

This paper introduces an adaptive quantum tomography method that combines complex optimization and statistical inference, achieving near-optimal accuracy for high-dimensional pure quantum states, surpassing existing mixed-state methods.

Contribution

It presents a novel adaptive tomographic approach that asymptotically reaches the fundamental accuracy limit for high-dimensional pure states, using complex stochastic optimization and statistical inference.

Findings

Approaches the Gill-Massar lower bound for estimation accuracy.

Outperforms existing mixed-state tomography methods.

Feasible with current experimental technology.

Abstract

Quantum tomography has become a key tool for the assessment of quantum states, processes, and devices. This drives the search for tomographic methods that achieve greater accuracy. In the case of mixed states of a single 2-dimensional quantum system adaptive methods have been recently introduced that achieve the theoretical accuracy limit deduced by Hayashi and Gill and Massar. However, accurate estimation of higher-dimensional quantum states remains poorly understood. This is mainly due to the existence of incompatible observables, which makes multiparameter estimation difficult. Here we present an adaptive tomographic method and show through numerical simulations that, after a few iterations, it is asymptotically approaching the fundamental Gill-Massar lower bound for the estimation accuracy of pure quantum states in high dimension. The method is based on a combination of stochastic optimization on the field of the complex numbers and statistical inference, exceeds the accuracy of any mixed-state tomographic method, and can be demonstrated with current experimental capabilities. The proposed method may lead to new developments in quantum metrology.