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‎CITATION.bib‎

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@@ -4,7 +4,7 @@ @misc{doolittle_2022_supp_codebase
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month = may,
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year = 2022,
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publisher = {Zenodo},
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version = {v0.1.0},
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version = {v0.1.1},
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doi = {10.5281/zenodo.6519147},
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url = {https://github.com/ChitambarLab/vqo-nonlocality-noisy-quantum-networks},
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howpublished = {\url{https://github.com/ChitambarLab/vqo-nonlocality-noisy-quantum-networks}},

‎script/plots/vqo_ibm_hardware.py‎

Lines changed: 184 additions & 183 deletions
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@@ -128,194 +128,195 @@
128128
"""
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Plotting Data
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"""
131-
# fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(16, 8))
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133-
# fig.suptitle(r"VQO of Non-$n$-Locality on IBM Quantum Computers", size=24, fontweight="bold")
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# axes = [ax1, ax2, ax3, ax4]
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# titles = [
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# "CHSH Network",
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# "Bilocal Network",
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# "\nTrilocal Chain Network",
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# "\nTrilocal Star Network",
141-
# ]
142-
# ylabels = [
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# r"Bell Score ($S_{\mathrm{CHSH}}$)",
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# r"Bell Score ($S_{\mathrm{Bilocal}}$)",
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# r"Bell Score ($S_{3\mathrm{-Chain}}$)",
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# r"Bell Score ($S_{3\mathrm{-Star}}$)",
147-
# ]
148-
# num_steps_list = [num_chsh_steps, num_bilocal_steps, num_chain_steps, num_star_steps]
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# quantum_bounds = [2 * np.sqrt(2), np.sqrt(2), np.sqrt(2), np.sqrt(2)]
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# classical_bounds = [2, 1, 1, 1]
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# ideal_data_sets = [
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# chsh_ideal_scores,
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# bilocal_ideal_scores,
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# chain_ideal_scores,
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# star_ideal_scores,
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# ]
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# data_sets = [chsh_data, bilocal_data, chain_data, star_data]
158-
# for i in range(4):
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# ax = axes[i]
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# num_steps = num_steps_list[i]
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# ax.plot(
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# range(num_steps),
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# [quantum_bounds[i]] * num_steps,
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# "-",
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# linewidth=3,
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# label="Quantum\nBound",
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# color="C0",
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# )
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# ax.plot(
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# range(num_steps),
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# [classical_bounds[i]] * num_steps,
173-
# "--",
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# linewidth=3,
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# label="Classical\nBound",
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# color="C1",
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# )
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# ax.plot(
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# range(num_steps),
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# [data_sets[i]["max_theoretical_score"]] * num_steps,
181-
# linestyle=(0, (3, 2, 1, 2, 1, 2)),
182-
# linewidth=3,
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# label="Theoretical\nMax Score",
184-
# color="C6",
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# )
186-
# ax.plot(
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# range(num_steps),
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# [data_sets[i]["mean_theoretical_score"]] * num_steps,
189-
# "-.",
190-
# label="Theoretical\nMean Score",
191-
# linewidth=3,
192-
# color="C5",
193-
# )
194-
# ax.plot(
195-
# range(num_steps),
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# ideal_data_sets[i],
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# ":d",
198-
# markersize=8,
199-
# linewidth=3,
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# label="Noiseless\nOptimized\nMax Score",
201-
# color="C2",
202-
# )
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# ax.plot(
204-
# range(num_steps),
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# data_sets[i]["max_scores"],
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# ":o",
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# markersize=7,
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# linewidth=3,
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# label="Optimized\nMax Score",
210-
# color="C3",
211-
# )
212-
# ax.errorbar(
213-
# range(num_steps),
214-
# data_sets[i]["mean_scores"],
215-
# data_sets[i]["stderr_scores"],
216-
# linestyle=":",
217-
# linewidth=3,
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# label="Optimized\nMean Score",
219-
# color="C4",
220-
# )
221-
222-
# ax.set_title(titles[i], size=20)
223-
# if i >= 2:
224-
# ax.set_xlabel("Gradient Descent Step", size=18)
225-
226-
# ax.set_ylabel(ylabels[i], size=18)
227-
228-
# if i == 0:
229-
# plt.figlegend(
230-
# ncol=1,
231-
# loc="center right",
232-
# fontsize=18,
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# bbox_to_anchor=(0, 0, 1, 1),
234-
# labelspacing=1.1,
235-
# ) # , bbox_to_anchor=(0, 0,1,1,))
236-
237-
# fig.subplots_adjust(left=0.05, right=0.82)
131+
fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(16, 8))
238132

239-
# datetime_ext = datetime.utcnow().strftime("%Y-%m-%dT%H-%M-%SZ")
240-
# filename = "simple_ansatzes_" + datetime_ext
241-
# plot_dir = "data/plots/vqo_ibm_hardware/"
242-
243-
fig, (ax2) = plt.subplots(1, 1, figsize=(25, 18))
244-
245-
fig.suptitle("VQO of Star Network Nonlocality on\nNoisy IBM Quantum Computers", size=80, fontweight="bold")
246-
247-
num_steps = num_star_steps
248-
quantum_bound = np.sqrt(2)
249-
classical_bound = 1
250-
ideal_data_set = star_ideal_scores
251-
data_set = star_data
252-
253-
ax2.semilogy(
254-
range(num_steps),
255-
np.sqrt(2) - data_set["mean_scores"],
256-
linestyle=":",
257-
marker="s",
258-
markersize=32,
259-
linewidth=8,
260-
label="VQO Mean",
261-
color="C4",
262-
)
263-
ax2.semilogy(
264-
range(num_steps),
265-
np.sqrt(2) - data_set["max_scores"],
266-
":o",
267-
markersize=32,
268-
linewidth=10,
269-
label="VQO Max",
270-
color="C3",
271-
)
272-
ax2.semilogy(
273-
range(num_steps),
274-
np.sqrt(2) - ideal_data_set,
275-
":d",
276-
markersize=32,
277-
linewidth=10,
278-
label="Noiseless VQO Max",
279-
color="C2",
280-
)
281-
282-
ax2.semilogy(
283-
range(num_steps),
284-
np.sqrt(2) - [classical_bound] * num_steps,
285-
"--",
286-
linewidth=10,
287-
label="Classical Bound",
288-
color="C0",
289-
)
290-
ax2.semilogy(
291-
range(num_steps),
292-
np.sqrt(2) - [data_set["mean_theoretical_score"]] * num_steps,
293-
"-.",
294-
label="Noisy Quantum Bound",
295-
linewidth=10,
296-
color="C1",
297-
)
298-
ax2.legend(
299-
ncol=1,
300-
fontsize=56,
301-
loc="lower left",
302-
)
133+
fig.suptitle(r"VQO of Non-$n$-Locality on IBM Quantum Computers", size=24, fontweight="bold")
303134

304-
# ax1.tick_params(axis='both', which='major', labelsize=52)
305-
ax2.tick_params(axis='both', which='major', labelsize=56)
135+
axes = [ax1, ax2, ax3, ax4]
136+
titles = [
137+
"CHSH Network",
138+
"Bilocal Network",
139+
"\nTrilocal Chain Network",
140+
"\nTrilocal Star Network",
141+
]
142+
ylabels = [
143+
r"Bell Score ($S_{\mathrm{CHSH}}$)",
144+
r"Bell Score ($S_{\mathrm{Bilocal}}$)",
145+
r"Bell Score ($S_{3\mathrm{-Chain}}$)",
146+
r"Bell Score ($S_{3\mathrm{-Star}}$)",
147+
]
148+
num_steps_list = [num_chsh_steps, num_bilocal_steps, num_chain_steps, num_star_steps]
149+
quantum_bounds = [2 * np.sqrt(2), np.sqrt(2), np.sqrt(2), np.sqrt(2)]
150+
classical_bounds = [2, 1, 1, 1]
151+
ideal_data_sets = [
152+
chsh_ideal_scores,
153+
bilocal_ideal_scores,
154+
chain_ideal_scores,
155+
star_ideal_scores,
156+
]
157+
data_sets = [chsh_data, bilocal_data, chain_data, star_data]
158+
for i in range(4):
159+
ax = axes[i]
160+
num_steps = num_steps_list[i]
161+
162+
ax.plot(
163+
range(num_steps),
164+
[quantum_bounds[i]] * num_steps,
165+
"-",
166+
linewidth=3,
167+
label="Quantum\nBound",
168+
color="C0",
169+
)
170+
ax.plot(
171+
range(num_steps),
172+
[classical_bounds[i]] * num_steps,
173+
"--",
174+
linewidth=3,
175+
label="Classical\nBound",
176+
color="C1",
177+
)
178+
ax.plot(
179+
range(num_steps),
180+
[data_sets[i]["max_theoretical_score"]] * num_steps,
181+
linestyle=(0, (3, 2, 1, 2, 1, 2)),
182+
linewidth=3,
183+
label="Theoretical\nMax Score",
184+
color="C6",
185+
)
186+
ax.plot(
187+
range(num_steps),
188+
[data_sets[i]["mean_theoretical_score"]] * num_steps,
189+
"-.",
190+
label="Theoretical\nMean Score",
191+
linewidth=3,
192+
color="C5",
193+
)
194+
ax.plot(
195+
range(num_steps),
196+
ideal_data_sets[i],
197+
":d",
198+
markersize=8,
199+
linewidth=3,
200+
label="Noiseless\nOptimized\nMax Score",
201+
color="C2",
202+
)
203+
ax.plot(
204+
range(num_steps),
205+
data_sets[i]["max_scores"],
206+
":o",
207+
markersize=7,
208+
linewidth=3,
209+
label="Optimized\nMax Score",
210+
color="C3",
211+
)
212+
ax.errorbar(
213+
range(num_steps),
214+
data_sets[i]["mean_scores"],
215+
data_sets[i]["stderr_scores"],
216+
linestyle=":",
217+
linewidth=3,
218+
label="Optimized\nMean Score",
219+
color="C4",
220+
)
221+
222+
ax.set_title(titles[i], size=20)
223+
if i >= 2:
224+
ax.set_xlabel("Gradient Descent Step", size=18)
225+
226+
ax.set_ylabel(ylabels[i], size=18)
227+
228+
if i == 0:
229+
plt.figlegend(
230+
ncol=1,
231+
loc="center right",
232+
fontsize=18,
233+
bbox_to_anchor=(0, 0, 1, 1),
234+
labelspacing=1.1,
235+
) # , bbox_to_anchor=(0, 0,1,1,))
236+
237+
fig.subplots_adjust(left=0.05, right=0.82)
306238

307-
# ax1.set_xlabel("Gradient Descent Step", fontsize=52)
308-
ax2.set_xlabel("Gradient Descent Step", fontsize=72)
239+
datetime_ext = datetime.utcnow().strftime("%Y-%m-%dT%H-%M-%SZ")
240+
filename = "simple_ansatzes_" + datetime_ext
241+
plot_dir = "data/plots/vqo_ibm_hardware/"
309242

310-
# ax1.set_ylabel("Nonlocal Star Score", fontsize=52)
311-
ax2.set_ylabel("Distance to Max Bell Violation", fontsize=72)
312243

313-
plt.grid(linewidth=4)
314-
fig.tight_layout(pad=2)
244+
# """
245+
# semilog plot of data
246+
# """
247+
248+
# fig, (ax2) = plt.subplots(1, 1, figsize=(25, 18))
249+
250+
# fig.suptitle("VQO of Star Network Nonlocality on\nNoisy IBM Quantum Computers", size=80, fontweight="bold")
251+
252+
# num_steps = num_star_steps
253+
# quantum_bound = np.sqrt(2)
254+
# classical_bound = 1
255+
# ideal_data_set = star_ideal_scores
256+
# data_set = star_data
257+
258+
# ax2.semilogy(
259+
# range(num_steps),
260+
# np.sqrt(2) - data_set["mean_scores"],
261+
# linestyle=":",
262+
# marker="s",
263+
# markersize=32,
264+
# linewidth=8,
265+
# label="VQO Mean",
266+
# color="C4",
267+
# )
268+
# ax2.semilogy(
269+
# range(num_steps),
270+
# np.sqrt(2) - data_set["max_scores"],
271+
# ":o",
272+
# markersize=32,
273+
# linewidth=10,
274+
# label="VQO Max",
275+
# color="C3",
276+
# )
277+
# ax2.semilogy(
278+
# range(num_steps),
279+
# np.sqrt(2) - ideal_data_set,
280+
# ":d",
281+
# markersize=32,
282+
# linewidth=10,
283+
# label="Noiseless VQO Max",
284+
# color="C2",
285+
# )
286+
287+
# ax2.semilogy(
288+
# range(num_steps),
289+
# np.sqrt(2) - [classical_bound] * num_steps,
290+
# "--",
291+
# linewidth=10,
292+
# label="Classical Bound",
293+
# color="C0",
294+
# )
295+
# ax2.semilogy(
296+
# range(num_steps),
297+
# np.sqrt(2) - [data_set["mean_theoretical_score"]] * num_steps,
298+
# "-.",
299+
# label="Noisy Quantum Bound",
300+
# linewidth=10,
301+
# color="C1",
302+
# )
303+
# ax2.legend(
304+
# ncol=1,
305+
# fontsize=56,
306+
# loc="lower left",
307+
# )
308+
# ax2.tick_params(axis='both', which='major', labelsize=56)
309+
310+
# ax2.set_xlabel("Gradient Descent Step", fontsize=72)
311+
312+
# ax2.set_ylabel("Distance to Max Bell Violation", fontsize=72)
313+
314+
# plt.grid(linewidth=4)
315+
# fig.tight_layout(pad=2)
315316

316317

317-
datetime_ext = datetime.utcnow().strftime("%Y-%m-%dT%H-%M-%SZ")
318-
filename = "simple_ansatzes_" + datetime_ext
319-
plot_dir = "data/plots/vqo_ibm_hardware/"
318+
# datetime_ext = datetime.utcnow().strftime("%Y-%m-%dT%H-%M-%SZ")
319+
# filename = "simple_ansatzes_" + datetime_ext
320+
# plot_dir = "data/plots/vqo_ibm_hardware/"
320321

321-
plt.savefig(plot_dir + filename)
322+
# plt.savefig(plot_dir + filename)

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