Maximum likelihood quantum state tomography is inadmissible

08/03/2018
by   Christopher Ferrie, et al.
0

Maximum likelihood estimation (MLE) is the most common approach to quantum state tomography. In this letter, we investigate whether it is also optimal in any sense. We show that MLE is an inadmissible estimator for most of the commonly used metrics of accuracy, i.e., some other estimator is more accurate for every true state. MLE is inadmissible for fidelity, mean squared error (squared Hilbert-Schmidt distance), and relative entropy. We prove that almost any estimator that can report both pure states and mixed states is inadmissible. This includes MLE, compressed sensing (nuclear-norm regularized) estimators, and constrained least squares. We provide simple examples to illustrate why reporting pure states is suboptimal even when the true state is itself pure, and why "hedging" away from pure states generically improves performance.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
10/02/2021

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

We propose an iterative algorithm that computes the maximum-likelihood e...
research
03/26/2021

Testing identity of collections of quantum states: sample complexity analysis

We study the problem of testing identity of a collection of unknown quan...
research
06/10/2019

Stretching the Effectiveness of MLE from Accuracy to Bias for Pairwise Comparisons

A number of applications (e.g., AI bot tournaments, sports, peer grading...
research
11/23/2022

Faster Stochastic First-Order Method for Maximum-Likelihood Quantum State Tomography

In maximum-likelihood quantum state tomography, both the sample size and...
research
01/23/2019

A comparative study of estimation methods in quantum tomography

As quantum tomography is becoming a key component of the quantum enginee...
research
07/18/2022

Quantum tomography using state-preparation unitaries

We describe algorithms to obtain an approximate classical description of...
research
07/29/2022

Fermionic tomography and learning

Shadow tomography via classical shadows is a state-of-the-art approach f...

Please sign up or login with your details

Forgot password? Click here to reset