Permute state_ghz arguments

Author: sebastiendesignolle

README.md

@@ -42,7 +42,7 @@ julia> N = 2; # bipartite scenario
 
 julia> p = 0.2; # white noise strength
 
-julia> ρ = Ket.state_ghz(d, N; v = 1 - p) # two-qubit maximally entangled state with white noise
+julia> ρ = Ket.state_ghz(N, d; v = 1 - p) # two-qubit maximally entangled state with white noise
 4×4 Hermitian{ComplexF64, Matrix{ComplexF64}}:
  0.45+0.0im   0.0+0.0im   0.0+0.0im   0.4+0.0im
   0.0-0.0im  0.05+0.0im   0.0+0.0im   0.0+0.0im
@@ -115,7 +115,7 @@ true
 Now consider a noisier version of the same state:
 
 ```julia
-julia> d = 2; N = 2; p = 0.8; ρ = Ket.state_ghz(d, N; v = 1 - p) # with more white noise
+julia> d = 2; N = 2; p = 0.8; ρ = Ket.state_ghz(N, d; v = 1 - p) # with more white noise
 4×4 Hermitian{ComplexF64, Matrix{ComplexF64}}:
  0.3+0.0im  0.0+0.0im  0.0+0.0im  0.1+0.0im
  0.0-0.0im  0.2+0.0im  0.0+0.0im  0.0+0.0im

examples/10qubits.jl

@@ -22,7 +22,7 @@ lmo = EntanglementDetection.AlternatingSeparableLMO(T, dims; nb = 10, threshold
 Random.seed!(0)
 println("Initializing the state ...")
 v = 0.3
-ρ = Ket.state_ghz(Complex{T}, 2, N; v = v)
+ρ = Ket.state_ghz(Complex{T}, N; v)
 println("Converting ...")
 C = correlation_tensor(ρ, dims)
 
@@ -67,4 +67,4 @@ time = [traj[i][5] for i in indices]
 
 plot_data = (itr, primal, dual_gap, time)
 plot_dir =  string(output_dir , "/ghz_" ,N , "_plot.dat")
-serialize(plot_dir, plot_data)
+serialize(plot_dir, plot_data)

examples/gme.jl

@@ -10,19 +10,17 @@ import MathOptInterface as MOI
 using Serialization
 
 max_iteration = 10^7    # control time
-callback_iter = 10^3    
+callback_iter = 10^3
 max_active = 4 * 10^4   # control the size of memory
 shortcut_scale = 10
 noise_atol = 1e-3
 
-
 output_dir = "EntanglementDetection/examples/results/gme"
 
 T = Float64
 N = 4
 dims = Tuple(fill(2, N))
-ρ = Ket.state_ghz(Complex{T}, 2, N)
-
+ρ = Ket.state_ghz(Complex{T}, N)
 
 k = 2
 C = correlation_tensor(ρ, dims)

test/interface.jl

@@ -6,7 +6,7 @@ import LinearAlgebra as LA
 p = 0.888 # 1 - 1/9
 T = Float64
 dims = [2, 2, 2, 2]
-target_rho = LA.Hermitian((1 - p) * Ket.state_ghz(2, 4) + p * Complex.(Matrix{T}(LA.I, prod(dims), prod(dims)) / prod(dims)))
+target_rho = LA.Hermitian((1 - p) * Ket.state_ghz(Complex{T}, 4) + p * Complex.(Matrix{T}(LA.I, prod(dims), prod(dims)) / prod(dims)))
 
 # Some oracle give us a guess