steeringRobustnessState.m

function [SRrho,optMax] = steeringRobustnessState(rho,Max,varargin)
%STEERINGWEIGHTSTATE estimate the steering robustness of a quantum state
% This function has two required arguments:
%  rho: a 2-D array containing a dA*dB x dA*dB bipartite quantum state rho.
%  Max: a 4-D array containing an initial set of POVMs to start the
%  algorithm with. The first two dimensions contain the dA x dA POVM
%  elements. The last two dimensions label (a,x). 
%
% This function has one optional argument:
%  improv_tol: (default: 1E-6) a scalar which sets the threshold
%  improvement in one round of the see-saw iteration before the algorithm
%  terminates.
%
% SRrho = steeringRobustnessState(rho,Max) returns a lower bound on the
% Steering Robustness of the state rho, when measured with ma measurements
% of oa outcomes each, taking as an initial seed for the measurements Max.
% It employs a see-saw algorithm to try and find the optimal set of
% measurements for the state rho.
%
% [SRrho,optMax] = steeringRobustnessState(rho,Max) also returns the final
% set of measurements in optMax, that achieve the Steering Robustness
% SRrho. optMax is a 4-D array. The first two dimenions contain the dA x dA
% POVM elements. The last two dimensions contain (a,x).
%
% [SRrho,optMax] = steeringRobustnessState(rho,Max,improv_tol) uses the
% tolerance improv_tol in its stopping criteria for the algorithm: only
% if the improvement in one round of the see-saw algorithm drops below
% improv_tol will the algorithm terminate.
%
% requires: CVX (http://cvxr.com/cvx/), QETLAB (http://www.qetlab.com)
% authors: Paul Skrzypczyk, Daniel Cavalcanti
% last updated: March 17, 2016

[improv_tol] = opt_args({1E-6},varargin{:});
% improv_tol: tolerance in see-saw. default is 1e-6.

[SRi, F] = steeringRobustness(genAssemblage(rho,Max));
% find the initial steering Robustness SRi, and the initial steering
% inequality F which certifies SWi

improv = 1;
% initalise improv > improv_tol.

while improv >= improv_tol % as long as the improvement is big enough
    [~, Max] = bestSteeringMeasurements(rho,F);
    % find the best set of POVMs given the state rho, and functional F
    [SRf, F] = steeringRobustness(genAssemblage(rho,Max));
    % find the new steering weight and function F given rho and Max
    improv = SRf - SRi;
    % calculate the improvement in the SW
    SRi = SRf;
    % set initial = final, and iterate.
end

SRrho = SRf;
optMax = Max;

end