adding colored noise and biased detector updates

script/plots/detector_biased_noise_robustness.py

@@ -384,7 +384,7 @@
         ],
         plot_dir="./data/plots/detector_biased_noise_robustness/",
         legend_labels=["Arbitrary VQO", "Max Entangled VQO"],
-        ncol_legend=2,
-        bottom_padding=0.45,
-        fig_height=7,
+        ncol_legend=4,
+        bottom_padding=0.3,
+        fig_height=5,
     )

script/plots/source_colored_noise_robustness.py

@@ -430,52 +430,60 @@ def verify_data(theoretical_score, vqo_score, atol=1e-8):
         return theoretical_score >= vqo_score or np.isclose(theoretical_score, vqo_score, atol=atol)
 
     for u in range(21):
-        assert verify_data(phi_plus_theoretical_chsh, phi_plus_max_chsh_colored)
-        assert verify_data(phi_plus_theoretical_single_bilocal, phi_plus_max_bilocal_single_colored)
+        assert verify_data(phi_plus_theoretical_chsh[5 * u], phi_plus_max_chsh_colored[u])
         assert verify_data(
-            phi_plus_theoretical_single_n3_chain, phi_plus_max_bilocal_single_colored
+            phi_plus_theoretical_single_bilocal[5 * u], phi_plus_max_bilocal_single_colored[u]
         )
         assert verify_data(
-            phi_plus_theoretical_single_n4_chain, phi_plus_max_n4_chain_single_colored
+            phi_plus_theoretical_single_n3_chain[5 * u], phi_plus_max_n3_chain_single_colored[u]
+        )
+        assert verify_data(
+            phi_plus_theoretical_single_n4_chain[5 * u], phi_plus_max_n4_chain_single_colored[u]
+        )
+        assert verify_data(
+            phi_plus_theoretical_single_n3_star[5 * u], phi_plus_max_n3_star_single_colored[u]
         )
-        assert verify_data(phi_plus_theoretical_single_n3_star, phi_plus_max_n3_star_single_colored)
 
-        assert verify_data(psi_plus_theoretical_chsh, psi_plus_max_chsh_colored)
-        assert verify_data(psi_plus_theoretical_single_bilocal, psi_plus_max_bilocal_single_colored)
+        assert verify_data(psi_plus_theoretical_chsh[5 * u], psi_plus_max_chsh_colored[u])
+        assert verify_data(
+            psi_plus_theoretical_single_bilocal[5 * u], psi_plus_max_bilocal_single_colored[u]
+        )
+        assert verify_data(
+            psi_plus_theoretical_single_n3_chain[5 * u], psi_plus_max_n3_chain_single_colored[u]
+        )
         assert verify_data(
-            psi_plus_theoretical_single_n3_chain, psi_plus_max_bilocal_single_colored
+            psi_plus_theoretical_single_n4_chain[5 * u], psi_plus_max_n4_chain_single_colored[u]
         )
         assert verify_data(
-            psi_plus_theoretical_single_n4_chain, psi_plus_max_n4_chain_single_colored
+            psi_plus_theoretical_single_n3_star[5 * u], psi_plus_max_n3_star_single_colored[u]
         )
-        assert verify_data(psi_plus_theoretical_single_n3_star, psi_plus_max_n3_star_single_colored)
 
-        assert verify_data(phi_plus_theoretical_chsh, phi_plus_max_chsh_colored)
+        assert verify_data(phi_plus_theoretical_chsh[5 * u], phi_plus_max_chsh_colored[u])
         assert verify_data(
-            phi_plus_theoretical_uniform_bilocal, phi_plus_max_bilocal_uniform_colored
+            phi_plus_theoretical_uniform_bilocal[5 * u], phi_plus_max_bilocal_uniform_colored[u]
         )
         assert verify_data(
-            phi_plus_theoretical_uniform_n3_chain, phi_plus_max_bilocal_uniform_colored
+            phi_plus_theoretical_uniform_n3_chain[5 * u], phi_plus_max_n3_chain_uniform_colored[u]
         )
         assert verify_data(
-            phi_plus_theoretical_uniform_n4_chain, phi_plus_max_n4_chain_uniform_colored
+            phi_plus_theoretical_uniform_n4_chain[5 * u], phi_plus_max_n4_chain_uniform_colored[u]
         )
         assert verify_data(
-            phi_plus_theoretical_uniform_n3_star, phi_plus_max_n3_star_uniform_colored
+            phi_plus_theoretical_uniform_n3_star[5 * u], phi_plus_max_n3_star_uniform_colored[u]
         )
 
-        assert verify_data(psi_plus_theoretical_chsh, psi_plus_max_chsh_colored)
+        assert verify_data(psi_plus_theoretical_chsh[5 * u], psi_plus_max_chsh_colored[u])
         assert verify_data(
-            psi_plus_theoretical_uniform_bilocal, psi_plus_max_bilocal_uniform_colored
+            psi_plus_theoretical_uniform_bilocal[5 * u], psi_plus_max_bilocal_uniform_colored[u]
         )
         assert verify_data(
-            psi_plus_theoretical_uniform_n3_chain, psi_plus_max_bilocal_uniform_colored
+            psi_plus_theoretical_uniform_n3_chain[5 * u], psi_plus_max_n3_chain_uniform_colored[u]
         )
         assert verify_data(
-            psi_plus_theoretical_uniform_n4_chain, psi_plus_max_n4_chain_uniform_colored
+            psi_plus_theoretical_uniform_n4_chain[5 * u], psi_plus_max_n4_chain_uniform_colored[u]
         )
         assert verify_data(
-            psi_plus_theoretical_uniform_n3_star, psi_plus_max_n3_star_uniform_colored
+            psi_plus_theoretical_uniform_n3_star[5 * u], psi_plus_max_n3_star_uniform_colored[u]
         )
 
     """

script/plots/source_depolarizing_noise_robustness.py

@@ -24,6 +24,8 @@ def bell_state_noise(gamma):
 if __name__ == "__main__":
     num_samples = 21
 
+    theory_noise_params = np.arange(0, 1.0001, 0.001)
+
     bell_state_noise_states = [bell_state_noise(gamma) for gamma in np.arange(0, 1.01, 0.05)]
 
     bell_state = np.array([[1, 0, 0, 1], [0, 0, 0, 0], [0, 0, 0, 0], [1, 0, 0, 1]]) / 2
@@ -55,7 +57,7 @@ def bell_state_noise(gamma):
     # ]
 
     theoretical_max_chsh = [
-        np.sqrt(2) * np.abs((1 - gamma * 16 / 15)) for gamma in np.arange(0, 1.01, 0.05)
+        np.sqrt(2) * np.abs((1 - gamma * 16 / 15)) for gamma in theory_noise_params
     ]
 
     """
@@ -103,12 +105,11 @@ def bell_state_noise(gamma):
     # ]
 
     theoretical_max_uniform_bilocal = [
-        np.sqrt(2) * np.sqrt(np.abs((1 - gamma * 16 / 15) ** 2))
-        for gamma in np.arange(0, 1.01, 0.05)
+        np.sqrt(2) * np.sqrt(np.abs((1 - gamma * 16 / 15) ** 2)) for gamma in theory_noise_params
     ]
 
     theoretical_max_single_bilocal = [
-        np.sqrt(2) * np.sqrt(np.abs((1 - gamma * 16 / 15))) for gamma in np.arange(0, 1.01, 0.05)
+        np.sqrt(2) * np.sqrt(np.abs((1 - gamma * 16 / 15))) for gamma in theory_noise_params
     ]
 
     """
@@ -152,12 +153,11 @@ def bell_state_noise(gamma):
     # ]
 
     theoretical_max_uniform_n3_chain = [
-        np.sqrt(2) * np.sqrt(np.abs((1 - gamma * 16 / 15) ** 3))
-        for gamma in np.arange(0, 1.01, 0.05)
+        np.sqrt(2) * np.sqrt(np.abs((1 - gamma * 16 / 15) ** 3)) for gamma in theory_noise_params
     ]
 
     theoretical_max_single_n3_chain = [
-        np.sqrt(2) * np.sqrt(np.abs((1 - gamma * 16 / 15))) for gamma in np.arange(0, 1.01, 0.05)
+        np.sqrt(2) * np.sqrt(np.abs((1 - gamma * 16 / 15))) for gamma in theory_noise_params
     ]
 
     """
@@ -201,13 +201,12 @@ def bell_state_noise(gamma):
     # ]
 
     theoretical_max_single_n3_star = [
-        np.sqrt(2) * np.power(np.abs((1 - gamma * 16 / 15)), 1 / 3)
-        for gamma in np.arange(0, 1.01, 0.05)
+        np.sqrt(2) * np.power(np.abs((1 - gamma * 16 / 15)), 1 / 3) for gamma in theory_noise_params
     ]
 
     theoretical_max_uniform_n3_star = [
         np.sqrt(2) * np.power(np.abs((1 - gamma * 16 / 15) ** 3), 1 / 3)
-        for gamma in np.arange(0, 1.01, 0.05)
+        for gamma in theory_noise_params
     ]
 
     """
@@ -218,15 +217,15 @@ def verify_data(theoretical_score, vqo_score):
         return theoretical_score >= vqo_score or np.isclose(theoretical_score, vqo_score)
 
     for u in range(21):
-        assert verify_data(theoretical_max_chsh[u], max_chsh_dep[u])
-        assert verify_data(theoretical_max_uniform_bilocal[u], max_bilocal_uniform_dep[u])
-        assert verify_data(theoretical_max_uniform_n3_chain[u], max_n3_chain_uniform_dep[u])
-        assert verify_data(theoretical_max_uniform_n3_star[u], max_n3_star_uniform_dep[u])
+        assert verify_data(theoretical_max_chsh[50 * u], max_chsh_dep[u])
+        assert verify_data(theoretical_max_uniform_bilocal[50 * u], max_bilocal_uniform_dep[u])
+        assert verify_data(theoretical_max_uniform_n3_chain[50 * u], max_n3_chain_uniform_dep[u])
+        assert verify_data(theoretical_max_uniform_n3_star[50 * u], max_n3_star_uniform_dep[u])
 
-        assert verify_data(theoretical_max_chsh[u], max_chsh_dep[u])
-        assert verify_data(theoretical_max_single_bilocal[u], max_bilocal_single_dep[u])
-        assert verify_data(theoretical_max_single_n3_chain[u], max_n3_chain_single_dep[u])
-        assert verify_data(theoretical_max_single_n3_star[u], max_n3_star_single_dep[u])
+        assert verify_data(theoretical_max_chsh[50 * u], max_chsh_dep[u])
+        assert verify_data(theoretical_max_single_bilocal[50 * u], max_bilocal_single_dep[u])
+        assert verify_data(theoretical_max_single_n3_chain[50 * u], max_n3_chain_single_dep[u])
+        assert verify_data(theoretical_max_single_n3_star[50 * u], max_n3_star_single_dep[u])
 
     """
     Plotting Data
@@ -268,4 +267,5 @@ def verify_data(theoretical_score, vqo_score):
         plot_dir="./data/plots/source_depolarizing_noise_robustness/",
         # bottom_padding=0.4,
         ncol_legend=3,
+        theory_params=theory_noise_params,
     )

src/utilities.py

@@ -602,6 +602,7 @@ def plot_unital_single_and_uniform_max_scores_data(
     bottom_padding=0.3,
     fig_height=5,
     ncol_legend=4,
+    theory_params=[],
 ):
     """Plots the noise robustness for single and uniform qubit/source/measurement
     noise models. This method is intended for plotting the max score across many
@@ -637,6 +638,9 @@ def plot_unital_single_and_uniform_max_scores_data(
 
     :param plot_dir: The directory to which the data will be plotted.
     :type plt_dir: String
+
+    :param theory_params: Noise params for theoretical data.
+    :type theory_params: List
     """
     fig, axes = plt.subplots(1, 2, figsize=(10, fig_height))
     fig.suptitle(fig_title, fontsize=24, fontweight="bold")
@@ -655,6 +659,9 @@ def plot_unital_single_and_uniform_max_scores_data(
     # line_widths = [5.5,5,4.5,4,3.5,3,2.5,2]
     dashes_list = [2, 3, 4, 5, 6, 7, 8, 9]
 
+    if len(theory_params) == 0:
+        theory_params = noise_params
+
     ax_data_sets = [single_max_scores, uniform_max_scores]
     ax_theory_sets = [single_theoretical_scores, uniform_theoretical_scores]
     ax_match_sets = [single_match_scores, uniform_match_scores]
@@ -692,7 +699,7 @@ def plot_unital_single_and_uniform_max_scores_data(
 
             if len(theory_set) != 0:
                 (theory_plt,) = ax.plot(
-                    noise_params,
+                    theory_params,
                     theory_set,
                     color=line_colors[j],
                     linestyle=":",