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@bdoolittle
bdoolittle committed Jan 9, 2023
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2 changes: 1 addition & 1 deletion CITATION.bib
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Expand Up @@ -4,7 +4,7 @@ @misc{doolittle_2022_supp_codebase
month = may,
year = 2022,
publisher = {Zenodo},
version = {v0.1.0},
version = {v0.1.1},
doi = {10.5281/zenodo.6519147},
url = {https://github.com/ChitambarLab/vqo-nonlocality-noisy-quantum-networks},
howpublished = {\url{https://github.com/ChitambarLab/vqo-nonlocality-noisy-quantum-networks}},
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367 changes: 184 additions & 183 deletions script/plots/vqo_ibm_hardware.py
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Expand Up @@ -128,194 +128,195 @@
"""
Plotting Data
"""
# fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(16, 8))

# fig.suptitle(r"VQO of Non-$n$-Locality on IBM Quantum Computers", size=24, fontweight="bold")

# axes = [ax1, ax2, ax3, ax4]
# titles = [
# "CHSH Network",
# "Bilocal Network",
# "\nTrilocal Chain Network",
# "\nTrilocal Star Network",
# ]
# ylabels = [
# r"Bell Score ($S_{\mathrm{CHSH}}$)",
# r"Bell Score ($S_{\mathrm{Bilocal}}$)",
# r"Bell Score ($S_{3\mathrm{-Chain}}$)",
# r"Bell Score ($S_{3\mathrm{-Star}}$)",
# ]
# num_steps_list = [num_chsh_steps, num_bilocal_steps, num_chain_steps, num_star_steps]
# quantum_bounds = [2 * np.sqrt(2), np.sqrt(2), np.sqrt(2), np.sqrt(2)]
# classical_bounds = [2, 1, 1, 1]
# ideal_data_sets = [
# chsh_ideal_scores,
# bilocal_ideal_scores,
# chain_ideal_scores,
# star_ideal_scores,
# ]
# data_sets = [chsh_data, bilocal_data, chain_data, star_data]
# for i in range(4):
# ax = axes[i]
# num_steps = num_steps_list[i]

# ax.plot(
# range(num_steps),
# [quantum_bounds[i]] * num_steps,
# "-",
# linewidth=3,
# label="Quantum\nBound",
# color="C0",
# )
# ax.plot(
# range(num_steps),
# [classical_bounds[i]] * num_steps,
# "--",
# linewidth=3,
# label="Classical\nBound",
# color="C1",
# )
# ax.plot(
# range(num_steps),
# [data_sets[i]["max_theoretical_score"]] * num_steps,
# linestyle=(0, (3, 2, 1, 2, 1, 2)),
# linewidth=3,
# label="Theoretical\nMax Score",
# color="C6",
# )
# ax.plot(
# range(num_steps),
# [data_sets[i]["mean_theoretical_score"]] * num_steps,
# "-.",
# label="Theoretical\nMean Score",
# linewidth=3,
# color="C5",
# )
# ax.plot(
# range(num_steps),
# ideal_data_sets[i],
# ":d",
# markersize=8,
# linewidth=3,
# label="Noiseless\nOptimized\nMax Score",
# color="C2",
# )
# ax.plot(
# range(num_steps),
# data_sets[i]["max_scores"],
# ":o",
# markersize=7,
# linewidth=3,
# label="Optimized\nMax Score",
# color="C3",
# )
# ax.errorbar(
# range(num_steps),
# data_sets[i]["mean_scores"],
# data_sets[i]["stderr_scores"],
# linestyle=":",
# linewidth=3,
# label="Optimized\nMean Score",
# color="C4",
# )

# ax.set_title(titles[i], size=20)
# if i >= 2:
# ax.set_xlabel("Gradient Descent Step", size=18)

# ax.set_ylabel(ylabels[i], size=18)

# if i == 0:
# plt.figlegend(
# ncol=1,
# loc="center right",
# fontsize=18,
# bbox_to_anchor=(0, 0, 1, 1),
# labelspacing=1.1,
# ) # , bbox_to_anchor=(0, 0,1,1,))

# fig.subplots_adjust(left=0.05, right=0.82)
fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(16, 8))

# datetime_ext = datetime.utcnow().strftime("%Y-%m-%dT%H-%M-%SZ")
# filename = "simple_ansatzes_" + datetime_ext
# plot_dir = "data/plots/vqo_ibm_hardware/"

fig, (ax2) = plt.subplots(1, 1, figsize=(25, 18))

fig.suptitle("VQO of Star Network Nonlocality on\nNoisy IBM Quantum Computers", size=80, fontweight="bold")

num_steps = num_star_steps
quantum_bound = np.sqrt(2)
classical_bound = 1
ideal_data_set = star_ideal_scores
data_set = star_data

ax2.semilogy(
range(num_steps),
np.sqrt(2) - data_set["mean_scores"],
linestyle=":",
marker="s",
markersize=32,
linewidth=8,
label="VQO Mean",
color="C4",
)
ax2.semilogy(
range(num_steps),
np.sqrt(2) - data_set["max_scores"],
":o",
markersize=32,
linewidth=10,
label="VQO Max",
color="C3",
)
ax2.semilogy(
range(num_steps),
np.sqrt(2) - ideal_data_set,
":d",
markersize=32,
linewidth=10,
label="Noiseless VQO Max",
color="C2",
)

ax2.semilogy(
range(num_steps),
np.sqrt(2) - [classical_bound] * num_steps,
"--",
linewidth=10,
label="Classical Bound",
color="C0",
)
ax2.semilogy(
range(num_steps),
np.sqrt(2) - [data_set["mean_theoretical_score"]] * num_steps,
"-.",
label="Noisy Quantum Bound",
linewidth=10,
color="C1",
)
ax2.legend(
ncol=1,
fontsize=56,
loc="lower left",
)
fig.suptitle(r"VQO of Non-$n$-Locality on IBM Quantum Computers", size=24, fontweight="bold")

# ax1.tick_params(axis='both', which='major', labelsize=52)
ax2.tick_params(axis='both', which='major', labelsize=56)
axes = [ax1, ax2, ax3, ax4]
titles = [
"CHSH Network",
"Bilocal Network",
"\nTrilocal Chain Network",
"\nTrilocal Star Network",
]
ylabels = [
r"Bell Score ($S_{\mathrm{CHSH}}$)",
r"Bell Score ($S_{\mathrm{Bilocal}}$)",
r"Bell Score ($S_{3\mathrm{-Chain}}$)",
r"Bell Score ($S_{3\mathrm{-Star}}$)",
]
num_steps_list = [num_chsh_steps, num_bilocal_steps, num_chain_steps, num_star_steps]
quantum_bounds = [2 * np.sqrt(2), np.sqrt(2), np.sqrt(2), np.sqrt(2)]
classical_bounds = [2, 1, 1, 1]
ideal_data_sets = [
chsh_ideal_scores,
bilocal_ideal_scores,
chain_ideal_scores,
star_ideal_scores,
]
data_sets = [chsh_data, bilocal_data, chain_data, star_data]
for i in range(4):
ax = axes[i]
num_steps = num_steps_list[i]

ax.plot(
range(num_steps),
[quantum_bounds[i]] * num_steps,
"-",
linewidth=3,
label="Quantum\nBound",
color="C0",
)
ax.plot(
range(num_steps),
[classical_bounds[i]] * num_steps,
"--",
linewidth=3,
label="Classical\nBound",
color="C1",
)
ax.plot(
range(num_steps),
[data_sets[i]["max_theoretical_score"]] * num_steps,
linestyle=(0, (3, 2, 1, 2, 1, 2)),
linewidth=3,
label="Theoretical\nMax Score",
color="C6",
)
ax.plot(
range(num_steps),
[data_sets[i]["mean_theoretical_score"]] * num_steps,
"-.",
label="Theoretical\nMean Score",
linewidth=3,
color="C5",
)
ax.plot(
range(num_steps),
ideal_data_sets[i],
":d",
markersize=8,
linewidth=3,
label="Noiseless\nOptimized\nMax Score",
color="C2",
)
ax.plot(
range(num_steps),
data_sets[i]["max_scores"],
":o",
markersize=7,
linewidth=3,
label="Optimized\nMax Score",
color="C3",
)
ax.errorbar(
range(num_steps),
data_sets[i]["mean_scores"],
data_sets[i]["stderr_scores"],
linestyle=":",
linewidth=3,
label="Optimized\nMean Score",
color="C4",
)

ax.set_title(titles[i], size=20)
if i >= 2:
ax.set_xlabel("Gradient Descent Step", size=18)

ax.set_ylabel(ylabels[i], size=18)

if i == 0:
plt.figlegend(
ncol=1,
loc="center right",
fontsize=18,
bbox_to_anchor=(0, 0, 1, 1),
labelspacing=1.1,
) # , bbox_to_anchor=(0, 0,1,1,))

fig.subplots_adjust(left=0.05, right=0.82)

# ax1.set_xlabel("Gradient Descent Step", fontsize=52)
ax2.set_xlabel("Gradient Descent Step", fontsize=72)
datetime_ext = datetime.utcnow().strftime("%Y-%m-%dT%H-%M-%SZ")
filename = "simple_ansatzes_" + datetime_ext
plot_dir = "data/plots/vqo_ibm_hardware/"

# ax1.set_ylabel("Nonlocal Star Score", fontsize=52)
ax2.set_ylabel("Distance to Max Bell Violation", fontsize=72)

plt.grid(linewidth=4)
fig.tight_layout(pad=2)
# """
# semilog plot of data
# """

# fig, (ax2) = plt.subplots(1, 1, figsize=(25, 18))

# fig.suptitle("VQO of Star Network Nonlocality on\nNoisy IBM Quantum Computers", size=80, fontweight="bold")

# num_steps = num_star_steps
# quantum_bound = np.sqrt(2)
# classical_bound = 1
# ideal_data_set = star_ideal_scores
# data_set = star_data

# ax2.semilogy(
# range(num_steps),
# np.sqrt(2) - data_set["mean_scores"],
# linestyle=":",
# marker="s",
# markersize=32,
# linewidth=8,
# label="VQO Mean",
# color="C4",
# )
# ax2.semilogy(
# range(num_steps),
# np.sqrt(2) - data_set["max_scores"],
# ":o",
# markersize=32,
# linewidth=10,
# label="VQO Max",
# color="C3",
# )
# ax2.semilogy(
# range(num_steps),
# np.sqrt(2) - ideal_data_set,
# ":d",
# markersize=32,
# linewidth=10,
# label="Noiseless VQO Max",
# color="C2",
# )

# ax2.semilogy(
# range(num_steps),
# np.sqrt(2) - [classical_bound] * num_steps,
# "--",
# linewidth=10,
# label="Classical Bound",
# color="C0",
# )
# ax2.semilogy(
# range(num_steps),
# np.sqrt(2) - [data_set["mean_theoretical_score"]] * num_steps,
# "-.",
# label="Noisy Quantum Bound",
# linewidth=10,
# color="C1",
# )
# ax2.legend(
# ncol=1,
# fontsize=56,
# loc="lower left",
# )
# ax2.tick_params(axis='both', which='major', labelsize=56)

# ax2.set_xlabel("Gradient Descent Step", fontsize=72)

# ax2.set_ylabel("Distance to Max Bell Violation", fontsize=72)

# plt.grid(linewidth=4)
# fig.tight_layout(pad=2)


datetime_ext = datetime.utcnow().strftime("%Y-%m-%dT%H-%M-%SZ")
filename = "simple_ansatzes_" + datetime_ext
plot_dir = "data/plots/vqo_ibm_hardware/"
# datetime_ext = datetime.utcnow().strftime("%Y-%m-%dT%H-%M-%SZ")
# filename = "simple_ansatzes_" + datetime_ext
# plot_dir = "data/plots/vqo_ibm_hardware/"

plt.savefig(plot_dir + filename)
# plt.savefig(plot_dir + filename)

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