Optimal networks for Quantum Metrology: semidefinite programs and product rules

TL;DR

This paper develops a semidefinite programming approach to optimize quantum process estimation, proving a product rule for independent processes and demonstrating how entanglement can enhance estimation precision.

Contribution

It introduces a semidefinite program formulation for quantum network optimization and establishes a general product rule for joint estimation of independent processes.

Findings

Optimal quantum network estimation can be formulated as a semidefinite program.

The product rule allows independent estimation of processes when the figure of merit is multiplicative.

Entanglement can significantly improve the precision of estimating sums of independent phase shifts.

Abstract

We investigate the optimal estimation of a quantum process that can possibly consist of multiple time steps. The estimation is implemented by a quantum network that interacts with the process by sending an input and processing the output at each time step. We formulate the search of the optimal network as a semidefinite program and use duality theory to give an alternative expression for the maximum payoff achieved by estimation. Combining this formulation with a technique devised by Mittal and Szegedy we prove a general product rule for the joint estimation of independent processes, stating that the optimal joint estimation can achieved by estimating each process independently, whenever the figure of merit is of a product form. We illustrate the result in several examples and exhibit counterexamples showing that the optimal joint network may not be the product of the optimal individual networks if the processes are not independent or if the figure of merit is not of the product form. In particular, we show that entanglement can reduce by a factor K the variance in the estimation of the sum of K independent phase shifts.