Release for 1.0.9

Author: vprusso

docs/conf.py

@@ -18,11 +18,11 @@
 # -- Project information -----------------------------------------------------
 
 project = "toqito"
-copyright = "2020-2024, Toqito Contributors"
-author = "Contributors to Toqito"
+copyright = "2020-2024, toqito Contributors"
+author = "Contributors to toqito"
 
 # The full version, including alpha/beta/rc tags
-release = "1.0.8"
+release = "1.0.9"
 
 
 # -- General configuration ---------------------------------------------------

main.py

@@ -0,0 +1,140 @@
+import numpy as np
+
+from toqito.matrices import standard_basis
+from toqito.nonlocal_games import ExtendedNonlocalGame
+from toqito.rand import random_povm
+from toqito.states import trine
+
+import numpy as np
+from toqito.matrices import standard_basis
+from toqito.state_props import concurrence
+e_0, e_1 = standard_basis(2)
+e_00, e_11 = np.kron(e_0, e_0), np.kron(e_1, e_1)
+u_vec = 1 / np.sqrt(2) * (e_00 + e_11)
+rho = u_vec @ u_vec.conj().T
+print(np.around(concurrence(rho), decimals=2))
+
+eps = 0.5
+print(np.around(1/3 * (2 + np.sqrt(1 - eps**2)), decimals=3))
+
+
+exit()
+
+e_0, e_1 = standard_basis(2)
+ep = (e_0 + e_1) / np.sqrt(2)
+em = (e_0 - e_1) / np.sqrt(2)
+
+dim = 2
+num_alice_out, num_bob_out = 2, 2
+num_alice_in, num_bob_in = 3, 3
+
+povms = random_povm(dim=dim, num_inputs=num_alice_in, num_outputs=num_alice_out)
+
+print(povms[:,:,0,0] + povms[:,:,0,1])
+print(ep @ ep.conj().T + em @ em.conj().T)
+exit()
+
+pred_mat = np.zeros([dim, dim, num_alice_out, num_bob_out, num_alice_in, num_bob_in])
+pred_mat[:, :, 0, 0, 0, 0] = povms[:, :, 0, 0]
+pred_mat[:, :, 0, 0, 1, 1] = povms[:, :, 1, 0]
+pred_mat[:, :, 0, 0, 2, 2] = povms[:, :, 2, 0]
+
+pred_mat[:, :, 1, 1, 0, 0] = povms[:, :, 0, 1]
+pred_mat[:, :, 1, 1, 1, 1] = povms[:, :, 1, 1]
+pred_mat[:, :, 1, 1, 2, 2] = povms[:, :, 2, 1]
+
+prob_mat = 1 / 3 * np.identity(3)
+
+game = ExtendedNonlocalGame(prob_mat, pred_mat, reps=1)
+unent = game.unentangled_value()
+print(f"{unent=}")
+lb = game.quantum_value_lower_bound()
+print(f"{lb=}")
+ns = game.nonsignaling_value()
+print(f"{ns=}")
+
+exit()
+
+e_0, e_1 = standard_basis(2)
+e_p = (e_0 + e_1) / np.sqrt(2)
+e_m = (e_0 - e_1) / np.sqrt(2)
+
+dim = 2
+num_alice_out, num_bob_out = 2, 2
+num_alice_in, num_bob_in = 2, 2
+
+pred_mat = np.zeros([dim, dim, num_alice_out, num_bob_out, num_alice_in, num_bob_in])
+
+#pred_mat[:, :, 0, 1, 0, 0] = e_0 @ e_0.conj().T
+#pred_mat[:, :, 1, 0, 0, 0] = e_0 @ e_0.conj().T
+pred_mat[:, :, 1, 1, 0, 0] = e_0 @ e_0.conj().T
+
+pred_mat[:, :, 0, 0, 0, 1] = e_1 @ e_1.conj().T
+#pred_mat[:, :, 1, 0, 0, 1] = e_1 @ e_1.conj().T
+pred_mat[:, :, 1, 1, 0, 1] = e_1 @ e_1.conj().T
+
+pred_mat[:, :, 0, 0, 1, 0] = e_p @ e_p.conj().T
+#pred_mat[:, :, 0, 1, 1, 0] = e_p @ e_p.conj().T
+pred_mat[:, :, 1, 1, 1, 0] = e_p @ e_p.conj().T
+
+pred_mat[:, :, 0, 0, 1, 1] = e_m @ e_m.conj().T
+#pred_mat[:, :, 0, 1, 1, 1] = e_m @ e_m.conj().T
+#pred_mat[:, :, 1, 0, 1, 1] = e_m @ e_m.conj().T
+
+prob_mat = 1 / 2 * np.identity(2)
+#prob_mat = np.array([[1/4, 1/4], [1/4, 1/4]])
+
+game = ExtendedNonlocalGame(prob_mat, pred_mat, reps=2)
+res = game.unentangled_value()
+#res = game.nonsignaling_value()
+#res = game.quantum_value_lower_bound()
+print(res)
+
+#res = game.nonsignaling_value()
+#res = game.commuting_measurement_value_upper_bound()
+#res = game.quantum_value_lower_bound()
+
+exit()
+dim = 2
+num_in = 3
+num_out = 2
+pred_mat = np.zeros([dim, dim, num_out, num_out, num_in, num_in], dtype=complex)
+
+e0, e1 = standard_basis(2)
+
+theta_1 = 1/4 * np.arccos((121 + 52 * np.sqrt(13))/(477))
+theta_2 = 1/4 * np.arccos((-431 + 4 * np.sqrt(13))/(477))
+alpha_0 = -theta_1
+alpha_1 = theta_2
+beta_0 = (np.pi/2) - theta_2
+beta_1 = theta_1
+def M(theta):
+    return np.array([
+        [np.cos(theta)**2, np.cos(theta) * np.sin(theta)],
+        [np.sin(theta) * np.cos(theta), np.sin(theta)**2]
+    ])
+
+# V(0,0|x,y):
+pred_mat[:, :, 0, 0, 1, 1] = M(alpha_0)
+pred_mat[:, :, 0, 0, 2, 2] = np.identity(dim) - M(alpha_0)
+
+# V(0,1|x,y):
+pred_mat[:, :, 0, 1, 0, 0] = M(alpha_1)
+pred_mat[:, :, 0, 1, 1, 1] = np.identity(dim) - M(alpha_1)
+
+# V(1,0|x,y):
+pred_mat[:, :, 1, 0, 0, 0] = M(beta_0)
+pred_mat[:, :, 1, 0, 1, 1] = np.identity(dim) - M(beta_0)
+
+# V(1,1|x,y):
+pred_mat[:, :, 1, 1, 0, 0] = M(beta_1)
+pred_mat[:, :, 1, 1, 2, 2] = np.identity(dim) - M(beta_1)
+
+prob_mat = 1 / 3 * np.identity(3)
+
+game = ExtendedNonlocalGame(prob_mat, pred_mat)
+res = game.unentangled_value()
+#res = game.nonsignaling_value()
+#res = game.commuting_measurement_value_upper_bound()
+#res = game.quantum_value_lower_bound()
+print(res)