QMetro++ -- Python optimization package for large scale quantum metrology with customized strategy structures

TL;DR

QMetro++ is a Python package that optimizes quantum estimation protocols by maximizing quantum Fisher information using tensor networks and iterative algorithms, suitable for large-scale quantum metrology with customizable strategies.

Contribution

The paper introduces QMetro++, a comprehensive Python toolkit that efficiently optimizes quantum metrology strategies for large systems using advanced computational techniques.

Findings

Efficient optimization of quantum estimation protocols for N≈100 channel uses.

Implementation of methods to compute fundamental upper bounds on QFI.

User-friendly interface for defining and analyzing quantum strategies.

Abstract

QMetro++ is a Python package that provides a set of tools for identifying optimal estimation protocols that maximize quantum Fisher information (QFI). Optimization can be performed for arbitrary configurations of input states, parameter-encoding channels, noise correlations, control operations, and measurements. The use of tensor networks and an iterative see-saw algorithm allows for an efficient optimization even in the regime of a large number of channel uses ($N\approx100$). Additionally, the package includes implementations of the recently developed methods for computing fundamental upper bounds on QFI, which serve as benchmarks for assessing the optimality of numerical optimization results. All functionalities are wrapped up in a user-friendly interface which enables the definition of strategies at various levels of detail.