A Scalable Maximum Likelihood Method for Quantum State Tomography

TL;DR

This paper introduces a scalable maximum likelihood approach for quantum state tomography that combines established algorithms with quantum many-body techniques to handle large systems efficiently.

Contribution

It proposes a novel strategy that merges maximum likelihood reconstruction with quantum many-body methods to improve scalability in quantum state tomography.

Findings

Enhanced scalability for large quantum systems

Reduced experiment time and postprocessing resources

Effective reconstruction of many-body quantum states

Abstract

The principle of maximum likelihood reconstruction has proven to yield satisfactory results in the context of quantum state tomography for many-body systems of moderate system sizes. Until recently, however, quantum state tomography has been considered to be infeasible for systems consisting of a large number of subsystems due to the exponential growth of the Hilbert space dimension with the number of constituents. Several reconstruction schemes have been proposed since then to overcome the two main obstacles in quantum many-body tomography: experiment time and postprocessing resources. Here we discuss one strategy to address these limitations for the maximum likelihood principle by adopting a particular state representation to merge a well established reconstruction algorithm maximizing the likelihood with techniques known from quantum many-body theory.