BellPolytopes.bell_frank_wolfeMethod

Calls the lazy pairwise blended conditional gradient algorithm from Frank-Wolfe package.

Arguments:

  • p: a correlation tensor of order N.

Returns:

  • x: a correlation tensor of order N, the output of the Frank-Wolfe algorithm,
  • ds: a deterministic strategy, the atom returned by the last LMO,
  • primal: ½|x-v₀*p|²
  • dual_gap: ⟨x-v₀*p, x-ds⟩
  • traj_data: trajectory of the algorithm,
  • active_set: all deterministic strategies used for the decomposition of the last iterate x, contains fields weights, atoms, and x,
  • M: a Bell inequality, meaningful only if the dual gap is small enough
  • β: the local bound of the inequality parametrised by M, reliable only if the last LMO is exact.

Optional arguments:

  • marg: a boolean, indicates if p contains marginals
  • v0: the visibility used to make a nonlocal p closer to the local polytope,
  • epsilon: the tolerance, used as a stopping criterion (when the primal value or the dual gap go below its value), by default 1e-7,
  • verbose: an integer, indicates the level of verbosity from 0 to 3,
  • shr2: the potential underlying shrinking factor, used to display the lower bound in the callback,
  • TD: type of the computations, by default the one of ̀p,
  • mode: an integer, 0 is for the heuristic LMO, 1 for the enumeration LMO,
  • nb: an integer, number of random tries in the LMO, if heuristic, by default 10^2,
  • TL: type of the last call of the LMO,
  • mode_last: an integer, mode of the last call of the LMO, -1 for no last call
  • nb_last: an integer, number of random tries in the last LMO, if heuristic, by default 10^5,
  • sym: a boolean, indicates if the symmetry of the input should be used, by default automatic choice,
  • use_array: a boolean, indicates to store the full deterministic strategies to trade memory for speed in multipartite scenarios,
  • callback_interval: an integer, print interval if verbose = 3,
  • seed: an integer, the initial random seed.
BellPolytopes.local_boundMethod

Compute the local bound of a Bell inequality parametrised by M. No symmetry detection is implemented yet, used mostly for pedagogy and tests.

BellPolytopes.nonlocality_thresholdMethod

Compute the nonlocality threshold of the qubit measurements encoded by the Bloch vectors vec in a Bell scenario with N parties.

Arguments:

  • vec: an m × 3 matrix with Bloch vectors coordinates,
  • N: the number of parties.

Returns:

  • lower_bound_infinite: a lower bound on the nonlocality threshold under all projective measurements (in the subspace spanned by vec in the Bloch sphere),
  • lower_bound: a lower bound on the nonlocality threshold under the measurements provided in input,
  • upper_bound: a (heuristic) upper bound on the nonlocality threshold under the measurements provided in input, also valid for all projective measurements
  • local_model: a decomposition of the correlation tensor obtained by applying the measurements encoded by the Bloch vectors vec on all N subsystems of the shared state rho with visibility lower_bound,
  • bell_inequality: a (heuristic) Bell inequality corresponding to upper_bound.

Optional arguments:

  • rho: the shared state, by default the singlet state in the bipartite case and the GHZ state otherwise,
  • v0: the initial visibility, which should be an upper bound on the nonlocality threshold, 1.0 by default,
  • precision: number of digits of lower_bound, 4 by default,
  • for the other optional arguments, see bell_frank_wolfe.