BellPolytopes.jl

This package addresses the membership problem for local polytopes: it constructs Bell inequalities and local models in multipartite Bell scenarios with binary outcomes.

The original article for which it was written can be found here:

Improved local models and new Bell inequalities via Frank-Wolfe algorithms.

Installation

The most recent release is available via the julia package manager, e.g., with

using Pkg
Pkg.add("BellPolytopes")

or the main branch:

Pkg.add(url="https://github.com/ZIB-IOL/BellPolytopes.jl", rev="main")

Getting started

Let's say we want to characterise the nonlocality threshold obtained with the two-qubit maximally entangled state and measurements whose Bloch vectors form an icosahedron. Using BellPolytopes.jl, here is what the code looks like.

julia> using BellPolytopes, LinearAlgebra

julia> N = 2; # bipartite scenario

julia> rho = rho_GHZ(N) # two-qubit maximally entangled state
4×4 Matrix{Float64}:
 0.5  0.0  0.0  0.5
 0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0
 0.5  0.0  0.0  0.5

julia> measurements_vec = icosahedron_vec() # Bloch vectors forming an icosahedron
6×3 Matrix{Float64}:
 0.0        0.525731   0.850651
 0.0        0.525731  -0.850651
 0.525731   0.850651   0.0
 0.525731  -0.850651   0.0
 0.850651   0.0        0.525731
 0.850651   0.0       -0.525731

julia> _, lower_bound, upper_bound, local_model, bell_inequality, _ =
       nonlocality_threshold(measurements_vec, N; rho = rho);

julia> println([lower_bound, upper_bound])
[0.7784, 0.7784]

julia> p = correlation_tensor(measurements_vec, N; rho = rho)
6×6 Matrix{Float64}:
  0.447214  -1.0       -0.447214   0.447214   0.447214  -0.447214
 -1.0        0.447214  -0.447214   0.447214  -0.447214   0.447214
 -0.447214  -0.447214  -0.447214   1.0        0.447214   0.447214
  0.447214   0.447214   1.0       -0.447214   0.447214   0.447214
  0.447214  -0.447214   0.447214   0.447214   1.0        0.447214
 -0.447214   0.447214   0.447214   0.447214   0.447214   1.0

julia> final_iterate = sum(local_model.weights[i] * local_model.atoms[i] for i in 1:length(local_model));

julia> norm(final_iterate - lower_bound * p) < 1e-3 # checking local model
true

julia> local_bound(bell_inequality)[1] / dot(bell_inequality, p) # checking the Bell inequality
0.7783914488195466

Under the hood

The computation is based on an efficient variant of the Frank-Wolfe algorithm to iteratively find the local point closest to the input correlation tensor. See this recent review for an introduction to the method and the package FrankWolfe.jl for the implementation on which this package relies.

In a nutshell, each step gets closer to the objective point:

• either by moving towards a good vertex of the local polytope,
• or by astutely combining the vertices (or atoms) already found and stored in the active set.

julia> res = bell_frank_wolfe(p; v0=0.8, verbose=3, callback_interval=10^2, mode_last=-1);

Visibility: 0.8
 Symmetric: true
   #Inputs: 6
 Dimension: 21

Intervals
    Print: 100
   Renorm: 100
   Reduce: 10000
    Upper: 1000
Increment: 1000

   Iteration        Primal      Dual gap    Time (sec)       #It/sec    #Atoms       #LMO
         100    8.7570e-03    6.0089e-02    6.9701e-04    1.4347e+05        11         26
         200    5.9241e-03    5.4948e-02    9.4910e-04    2.1073e+05        16         33
         300    3.5594e-03    3.4747e-02    1.1942e-03    2.5122e+05        18         40
         400    1.9068e-03    3.4747e-02    1.3469e-03    2.9697e+05        16         42
         500    1.8093e-03    5.7632e-06    1.5409e-03    3.2448e+05        14         48

    Primal: 1.81e-03
  Dual gap: 2.60e-08
      Time: 1.63e-03
    It/sec: 3.28e+05
    #Atoms: 14

v_c ≤ 0.778392

Going further

More examples can be found in the corresponding folder of the package. They include the construction of a Bell inequality with a higher tolerance to noise as CHSH as well as multipartite instances.