The Nieto-Silleras and Moroder hierarchies in Ncpol2sdpa

Posted on 27 November 2014

Some alternatives to the NPA hierarchy (Pironio et al., 2010) were published recently. One of the new approaches takes all joint probabilities into consideration when looking for a maximum guessing probability, and not just the ones included in a particular Bell inequality (Nieto-Silleras et al., 2014, Bancal et al., 2014). We refer to this type as the Nieto-Silleras hierarchy.

The other extension consider a tensor product structure between the parties in a two-party Bell experiment (Moroder et al., 2013). This structure allows imposing further constraints that are not possible in the NPA hierarchy, such as the positivity of the partial transpose of the moment matrix. We refer to this as the Moroder hierarchy.

Ncpol2sdpa was recently extended to generate these two types of hierarchies, and the subsequent sections give an example to each. We assume that Ncpol2sdpa version 1.5 and its dependencies are installed, QuTiP is available, and the default solver, SDPA, is also operational.

Nieto-Silleras hierarchy

To deal with the joint probabilities necessary for setting constraints, we also rely on QuTiP (Johansson et al., 2013). First we need to import some functions:

from math import sqrt
from qutip import tensor, basis, sigmax, sigmay, expect, qeye
from ncpol2sdpa import SdpRelaxation, flatten, solve_sdp,   

We will work in a CHSH scenario where we are trying to find the maximum guessing probability of the first projector of Alice's first measurement. We generate the joint probability distribution on the maximally entangled state with the measurements that give the maximum quantum violation of the CHSH inequality:

def joint_probabilities():
    psi = (tensor(basis(2,0),basis(2,0)) + 
    A_0 = sigmax()
    A_1 = sigmay()
    B_0 = (-sigmay()+sigmax())/sqrt(2)
    B_1 = (sigmay()+sigmax())/sqrt(2)

    A_00 = (qeye(2) + A_0)/2
    A_10 = (qeye(2) + A_1)/2
    B_00 = (qeye(2) + B_0)/2
    B_10 = (qeye(2) + B_1)/2

    p.append(expect(tensor(A_00, qeye(2)), psi))
    p.append(expect(tensor(A_10, qeye(2)), psi))
    p.append(expect(tensor(qeye(2), B_00), psi))
    p.append(expect(tensor(qeye(2), B_10), psi))

    p.append(expect(tensor(A_00, B_00), psi))
    p.append(expect(tensor(A_00, B_10), psi))
    p.append(expect(tensor(A_10, B_00), psi))
    p.append(expect(tensor(A_10, B_10), psi))
    return p

Next we need the basic configuration of the projectors. We also set the level of the SDP relaxation and the objective.

level = 1
A_configuration = [2, 2]
B_configuration = [2, 2]
P_A = generate_measurements(A_configuration, 'P_A')
P_B = generate_measurements(B_configuration, 'P_B')
monomial_substitutions = projective_measurement_constraints(
    P_A, P_B)
objective = -P_A[0][0]

We must define further constraints, namely that the joint probabilities must match:

probabilities = joint_probabilities()
equalities = []
# We impose the constraints of the Nieto-Silleras formulation 
# using the joint probabilities. Marginals go first.
for i in range(len(A_configuration)):
    k += 1
for i in range(len(B_configuration)):
    k += 1
for i in range(len(A_configuration)):
    for j in range(len(B_configuration)):
        k += 1

From here, obtaining the SDP relaxation and solving it is trivial:

sdpRelaxation = SdpRelaxation([flatten([P_A, P_B])], verbose=2,
sdpRelaxation.get_relaxation(objective, [], equalities,
                             monomial_substitutions, level)


Number of SDP variables: 15
Generating moment matrix...
Reduced number of SDP variables: 11
Processing 16 inequalities...
(-0.5000000504870096, -0.5000001112451216)

Moroder hierarchy

This type of hierarchy allows for a wider range of constraints of the optimization problems, including ones that are not of polynomial form. These constraints are hard to impose using SymPy and the sparse structures in Ncpol2Sdpa. For this reason, we separate two steps: generating the SDP and post-processing the SDP to impose extra constraints. This second step can be done in MATLAB, for instance.

We need to import a slightly different set of functions:

from ncpol2sdpa import SdpRelaxation, flatten, write_to_sdpa,   

Then we set up the problem with specifically with the CHSH inequality in the probability picture as the objective function:

level = 1
A_configuration = [2, 2]
B_configuration = [2, 2]
I = [[ 0,   -1,    0 ],
     [-1,    1,    1 ], 
     [ 0,    1,   -1 ]]
A = generate_measurements(A_configuration, 'A')
B = generate_measurements(B_configuration, 'B')
monomial_substitutions = projective_measurement_constraints(A, B)
objective = define_objective_with_I(I, A, B)

We obtain the relaxation and write it to a sparse SDPA file:

sdpRelaxation = SdpRelaxation([flatten(A), flatten(B)], verbose=2,
sdpRelaxation.get_relaxation(objective, [], [],
                             monomial_substitutions, level)
write_to_sdpa(sdpRelaxation, "chsh-moroder.dat-s")

Number of SDP variables: 44
Generating moment matrix...
Reduced number of SDP variables: 18
Processing 0 inequalities...

From here, processing switches to MATLAB. We can read the SDPA file with SeDuMi or Yalmip, for instance. Yalmip gives more flexibility, so we work with that. The default function for loading is called loadsdpafile. We modify this to return the moment matrix itself:

function [F,h,momentmatrix] = loadsdpafilehacked(varargin)

filename = varargin{1};
% Does the file exist
if ~exist(filename)
    filename = [filename '.dat-s'];
    if ~exist(filename)
        error(['No such file.']);

% Load using SeDuMi
    [At,b,c,K] = fromsdpa(filename);
    error('LOADSDPAFILE currently requires SeDuMi to be installed');

nvars = length(b);
x = sdpvar(nvars,1);

F = ([]);
top = 1;
if isfield(K,'l')
    if K.l(1)>0
        X = c(top:top+K.l-1)-At(top:top+K.l-1,:)*x;
        F = F + (X(:)>=0);
        top = top + K.l;

if isfield(K,'s')
    if K.s(1)>0
        for i = 1:length(K.s)
            [ix,iy,iv] = find([c(top:top+K.s(i)^2-1) At(top:top+K.s(i)^2-1,:)]);
            off = (ix-1)/(K.s(i)+1);
            if all(off == round(off))
                X = c(top:top+K.s(i)^2-1)-At(top:top+K.s(i)^2-1,:)*x;
                F = F + (diag(reshape(X,K.s(i),K.s(i))) >= 0);
                if first==0
                top = top + K.s(i)^2;
                X = c(top:top+K.s(i)^2-1)-At(top:top+K.s(i)^2-1,:)*x;
                whatever = c(top:top+K.s(i)^2-1)
                F = F + (reshape(X,K.s(i),K.s(i)) >= 0);
                if first==0
                top = top + K.s(i)^2;
h = -b'*x;

Using this modified function, we can perform arbitrary postprocessing on the moment matrix, for instance, imposing the PPT condition:

F = F + (PartialTranspose(momentmatrix>=0)


Bancal, J.-D.; Sheridan, L. & Scarani, V. More randomness from the same data. New Journal of Physics, 2014, 16, pp. 033011.

Johansson, J.R.; Nation, P.D. & Nori, F. QuTiP 2: A Python framework for the dynamics of open quantum systems. Computer Physics Communications, 2013, 184, pp. 1234--1240.

Moroder, T.; Bancal, J.-D.; Liang, Y.-C.; Hofmann, M. & Gühne, O. Device-independent entanglement quantification and related applications. Physics Review Letters, American Physical Society, 2013, 111, pp. 030501.

Nieto-Silleras, O.; Pironio, S. & Silman, J. Using complete measurement statistics for optimal device-independent randomness evaluation. New Journal of Physics, 2014, 16, pp. 013035.

Pironio, S.; Navascués, M. & Acín, A. Convergent relaxations of polynomial optimization problems with noncommuting variables. SIAM Journal on Optimization, 2010, 20, pp. 2157-2180.

Tags: Noncommutative polynomials, Python, Quantum information theory, Semidefinite programming, SymPy

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