Rank-based model selection for multiple ions quantum tomography

TL;DR

This paper introduces a model selection approach using AIC and BIC to identify the simplest quantum states fitting measurement data from multi-ion experiments, improving quantum tomography efficiency.

Contribution

It applies and compares AIC and BIC for quantum state model selection in multi-ion experiments, demonstrating their effectiveness in identifying low-rank states.

Findings

AIC and BIC effectively select appropriate model ranks for quantum states.

Optimal model ranks for data range between 6 and 9.

Maximum likelihood estimator performance is close to the theoretical optimum.

Abstract

The statistical analysis of measurement data has become a key component of many quantum engineering experiments. As standard full state tomography becomes unfeasible for large dimensional quantum systems, one needs to exploit prior information and the "sparsity" properties of the experimental state in order to reduce the dimensionality of the estimation problem. In this paper we propose model selection as a general principle for finding the simplest, or most parsimonious explanation of the data, by fitting different models and choosing the estimator with the best trade-off between likelihood fit and model complexity. We apply two well established model selection methods -- the Akaike information criterion (AIC) and the Bayesian information criterion (BIC) -- to models consising of states of fixed rank and datasets such as are currently produced in multiple ions experiments. We test the performance of AIC and BIC on randomly chosen low rank states of 4 ions, and study the dependence of the selected rank with the number of measurement repetitions for one ion states. We then apply the methods to real data from a 4 ions experiment aimed at creating a Smolin state of rank 4. The two methods indicate that the optimal model for describing the data lies between ranks 6 and 9, and the Pearson $\chi^{2}$ test is applied to validate this conclusion. Additionally we find that the mean square error of the maximum likelihood estimator for pure states is close to that of the optimal over all possible measurements.