Mean-squared-error-based adaptive estimation of pure quantum states and unitary transformations

TL;DR

This paper introduces a high-accuracy quantum state and unitary transformation estimation method based on minimizing squared error, utilizing stochastic optimization and maximum likelihood, with near-optimal precision demonstrated through numerical experiments.

Contribution

It presents a novel estimation technique combining stochastic and statistical methods that achieves near-theoretical limits for pure quantum states and unitary transformations.

Findings

Estimation accuracy is state independent.

Method approaches twice the Gill-Massar lower bound.

Extension to unitary transformations improves over tomographic methods.

Abstract

In this article we propose a method to estimate with high accuracy pure quantum states of a single qudit. Our method is based on the minimization of the squared error between the complex probability amplitudes of the unknown state and its estimate. We show by means of numerical experiments that the estimation accuracy of the present method, which is given by the expectation of the squared error on the sample space of estimates, is state independent. Furthermore, the estimation accuracy delivered by our method is close to twice the Gill-Massar lower bound, which represents the best achievable accuracy, for all inspected dimensions. The minimization problem is solved via the concatenation of the Complex simultaneous perturbation approximation, an iterative stochastic optimization method that works within the field of the complex numbers, and Maximum likelihood estimation, a well-known statistical inference method. This can be carried out with the help of a multi-arm interferometric array. In the case of a single qubit, a Mach-Zehnder interferometer suffices. We also show that our estimation procedure can be easily extended to estimate unknown unitary transformations acting on a single qudit. Thereby, the estimation of unitary transformations achieves a higher accuracy than that achieved by processes based on tomographic methods for mixed states.