steeringreview/steeringLHSRobustness.m at master · luigibrancati/steeringreview · GitHub

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function [SLR, F] = steeringLHSRobustness(sigma,varargin)
%STEERINLHSGROBUSTNESS calculates the steering LHS-robustness of an
%assemblage
% This function has one required argument:
%  sigma: a 4-D array, containing the members of the assemblage. The first
%  two dimensions contain the (unnormalised) quantum states, while the
%  remaining two dimensions are (a,x), such that sigma(:,:,a,x) =
%  \sigma_a|x.
%
% SLR = steeringLHSRobustness(sigma) returns the steering LHS-robustness
% SLR of the assemblage sigma.
%
% [SLR, F] = steeringLHSRobustness(sigma) also returns the steering
% functional F that certifies that the steering LHS-robustness is SLR. F is
% a 4-D array, with the first two dimensions containing the members of the
% steering functional, and the last two labelling (a,x).
%
% This function has one optional argument:
%   consistent: (default 0)
%
% [SLR, F] = steeringLHSRobustness(sigma,consistent) calculates the
% standard steering LHS-robustness when consistent = 0, and the consistent
% steering LHS-robustness when consistent = 1.
%
%   requires: CVX (http://cvxr.com/cvx/), QETLAB (http://www.qetlab.com)
%   authors: Paul Skrzypczyk, Daniel Cavalcanti last updated: March 17,
%   2016

[consistent] = opt_args({0},varargin{:});
% if unspecified, it is assumed the standard steering weight is required.

[dB,~,oa,ma] = size(sigma);
% dB = dim. of Bob, oa = # outcomes for Alice, ma = # inputs for Alice

Ndet = oa^ma;
% number of determinstic probability distributions for Alice

SingleParty = genSinglePartyArray(oa,ma);
% generate array containing the single party distributions

% check that the assemblage is valid
if  NSAssemblage(sigma) == 0
    error('assemblage is not valid')
end

sigR = sum(sigma(:,:,:,1),3);
% sigR = reduced state of Bob, necessary for consistent steering robust.

% NOTE: Here we use the dual formulation of the steering LHS-robustness.

cvx_begin sdp quiet

    variable F(dB,dB,oa,ma) hermitian
    % members of the steering functional

    if consistent == 1
        variable X(dB,dB) hermitian
        % if consistent steering robustness is required, we need an
        % additional variable.
    else
        X = zeros(dB,dB);
        % if we want the standard steering robustness, we can set this
        % variable equal to the zero matrix.
    end

    maximise -1 + real(sum(reshape(F.*conj(sigma),1,[])))
    % max sum_ax trace(F_ax*sigma_a|x)

    subject to

    for i = 1:Ndet
        eye(dB) - sum(sum(permute(repmat(SingleParty(:,:,i),[1,1,dB,dB]),[3,4,1,2]).*F,3),4)...
             + X - trace(X*sigR)*eye(dB) == hermitian_semidefinite(dB);
        % standard SR: eye(dB) - sum_ax F_ax D(a|x,lam) >= 0, forall lam
        % consist. SR: eye(dB) - sum_ax F_ax D(a|x,lam) - X +
        % tr(X*sigR)eye(dB) >= 0, forall lam
        sum(sum(permute(repmat(SingleParty(:,:,i),[1,1,dB,dB]),[3,4,1,2]).*F,3),4)...
            == hermitian_semidefinite(dB)
        % - sum_ax F_ax D(a|x,lam) >= 0, forall lam
    end

cvx_end

SLR = cvx_optval;

end