Iterative optimization in quantum metrology and entanglement theory using semidefinite programming

TL;DR

This paper introduces an efficient iterative semidefinite programming method to optimize quantum Fisher information for local Hamiltonians, enhancing quantum metrology performance and analyzing entanglement properties.

Contribution

It presents a novel iterative see-saw approach using semidefinite programming for optimizing quantum Fisher information in bipartite systems, improving over existing methods.

Findings

The method achieves fast, robust convergence.

It effectively optimizes local Hamiltonians for quantum metrology.

Applicable to other quantum information problems like entanglement detection.

Abstract

We discuss efficient methods to optimize the metrological performance over local Hamiltonians in a bipartite quantum system. For a given quantum state, our methods find the best local Hamiltonian for which the state outperforms separable states the most from the point of view of quantum metrology. We show that this problem can be reduced to maximizing the quantum Fisher information over a certain set of Hamiltonians. We present the quantum Fisher information in a bilinear form and maximize it by an iterative see-saw (ISS) method, in which each step is based on semidefinite programming. We also solve the problem with the method of moments that works very well for smaller systems. Our approach is one of the efficient methods that can be applied for an optimization of the unitary dynamics in quantum metrology, the other methods being, for example, machine learning, variational quantum circuits, or neural networks. The advantage of our method is the fast and robust convergence due to the simple mathematical structure of the approach. We also consider a number of other problems in quantum information theory that can be solved in a similar manner. For instance, we determine the bound entangled quantum states that maximally violate the Computable Cross Norm-Realignment (CCNR) criterion.