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Formate notebook as black
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notebooks/hd_tomography.ipynb

Lines changed: 76 additions & 48 deletions
Original file line numberDiff line numberDiff line change
@@ -36902,7 +36902,7 @@
3690236902
"\n",
3690336903
"\n",
3690436904
"def maps2str(maps):\n",
36905-
" return \"||\".join(\"|\".join(str(i) for i in reg) for reg in maps) "
36905+
" return \"||\".join(\"|\".join(str(i) for i in reg) for reg in maps)"
3690636906
]
3690736907
},
3690836908
{
@@ -36922,24 +36922,24 @@
3692236922
" result_path_base = rf\"result-{circuit.name}-{backend.name}-{shots}\"\n",
3692336923
" maps_str = maps2str(maps) if maps is not None else None\n",
3692436924
" if use_cached is not False:\n",
36925-
" cached_results = [] \n",
36925+
" cached_results = []\n",
3692636926
" path_pattern = re.compile(re.escape(result_path_base) + \"-([\\d|]+)-?([a-z\\d]*).json\")\n",
3692736927
" for result_path in assets_path.iterdir():\n",
3692836928
" m = path_pattern.match(result_path.name)\n",
3692936929
" if m is None:\n",
3693036930
" continue\n",
3693136931
" if maps is None or m.group(1) == maps_str:\n",
36932-
" result = qiskit.result.Result.from_dict(json.loads(result_path.read_text())) \n",
36932+
" result = qiskit.result.Result.from_dict(json.loads(result_path.read_text()))\n",
3693336933
" print(f\"Load result from '{os.path.relpath(result_path, Path.cwd())}'!\")\n",
3693436934
" if use_cached is True or use_cached == \"any\":\n",
36935-
" return result \n",
36935+
" return result\n",
3693636936
" elif use_cached == \"all\":\n",
3693736937
" cached_results.append(result)\n",
3693836938
" else:\n",
3693936939
" raise ValueError(f\"wrong type of cache rule: {use_cached}\")\n",
3694036940
" if len(cached_results) > 0:\n",
3694136941
" return cached_results\n",
36942-
" \n",
36942+
"\n",
3694336943
" circuit_transpiled = transpile(circuit, backend, maps=maps)\n",
3694436944
" if maps is None:\n",
3694536945
" maps = maps_from_layout(circuit_transpiled.layout)\n",
@@ -39402,23 +39402,31 @@
3940239402
" errs = [fit_limit(x, y, hdist, neighbour=n, a_tol=1e-3) for n in range(1, n_data_qubits // 2 + 1)]\n",
3940339403
" return errs\n",
3940439404
"\n",
39405+
"\n",
3940539406
"def argminmeans(lst):\n",
3940639407
" return np.argmin([np.mean([e for _, e in item]) for item in lst])\n",
39407-
" \n",
39408+
"\n",
39409+
"\n",
3940839410
"def neighbors_errors(backend, circuit, n_data_qubits, shots, maps=None, use_cached=True, choose=None):\n",
3940939411
" result = run_on_backend(backend, circuit, shots, maps=maps, use_cached=use_cached)\n",
3941039412
" if not isinstance(result, (tuple, list)):\n",
3941139413
" return neighbors_errors_from_result(result, n_data_qubits)\n",
39412-
" \n",
39414+
"\n",
3941339415
" choose = argminmeans if choose is None else choose\n",
3941439416
" errss = [neighbors_errors_from_result(r, n_data_qubits) for r in result]\n",
3941539417
" print(errss)\n",
3941639418
" errs_indx = choose(errss)\n",
3941739419
" print(f\"Choose {errs_indx} result.\")\n",
3941839420
" return errss[errs_indx]\n",
3941939421
"\n",
39420-
"def neighbors_errors_over_shots(backend, circuit, n_data_qubits, shots_arr, maps=None, use_cached=True, choose=None) -> list:\n",
39421-
" errs = [neighbors_errors(backend, circuit, n_data_qubits, s, maps=maps, choose=choose, use_cached=use_cached) for s in shots_arr]\n",
39422+
"\n",
39423+
"def neighbors_errors_over_shots(\n",
39424+
" backend, circuit, n_data_qubits, shots_arr, maps=None, use_cached=True, choose=None\n",
39425+
") -> list:\n",
39426+
" errs = [\n",
39427+
" neighbors_errors(backend, circuit, n_data_qubits, s, maps=maps, choose=choose, use_cached=use_cached)\n",
39428+
" for s in shots_arr\n",
39429+
" ]\n",
3942239430
" errs = [list(zip(*limits_and_errors)) for limits_and_errors in list(zip(*errs))]\n",
3942339431
" return errs"
3942439432
]
@@ -39436,9 +39444,10 @@
3943639444
"\n",
3943739445
"def line(x, a, b):\n",
3943839446
" return a * x + b\n",
39439-
" \n",
39440-
"def fit_exp(shots_arr, errs_arr): \n",
39441-
" errs_arr_argmin = np.argmin(np.hstack((errs_arr, [0]))) # can not fit at zero \n",
39447+
"\n",
39448+
"\n",
39449+
"def fit_exp(shots_arr, errs_arr):\n",
39450+
" errs_arr_argmin = np.argmin(np.hstack((errs_arr, [0]))) # can not fit at zero\n",
3944239451
" errs_arr_up_to_min, shots_arr_ = errs_arr[:errs_arr_argmin], shots_arr[:errs_arr_argmin]\n",
3944339452
" params, params_covariance = curve_fit(line, shots_arr_, np.log(errs_arr_up_to_min), p0=(1e-3, 1))\n",
3944439453
" r2 = r2_score(errs_arr_up_to_min, np.exp(line(shots_arr_, *params)))\n",
@@ -39473,7 +39482,7 @@
3947339482
},
3947439483
"outputs": [],
3947539484
"source": [
39476-
"shots_arr = np.linspace(500, 1.7e4, 34).astype(int)# ; shots_arr"
39485+
"shots_arr = np.linspace(500, 1.7e4, 34).astype(int) # ; shots_arr"
3947739486
]
3947839487
},
3947939488
{
@@ -39578,8 +39587,13 @@
3957839587
"import re\n",
3957939588
"\n",
3958039589
"pattern = rf\"result-{circuit.name}-{ibm_backend.name}-(\\d+)-.*.json\"\n",
39581-
"ibm_shots_arr = sorted({int(m.group(1)) for item in (Path.cwd().parent / \"assets\").iterdir() if (m := re.match(pattern, str(item.name)))}\n",
39582-
" )# ibm_shots_arr = np.linspace(2e3, 1.7e4, 16).astype(int)\n",
39590+
"ibm_shots_arr = sorted(\n",
39591+
" {\n",
39592+
" int(m.group(1))\n",
39593+
" for item in (Path.cwd().parent / \"assets\").iterdir()\n",
39594+
" if (m := re.match(pattern, str(item.name)))\n",
39595+
" }\n",
39596+
") # ibm_shots_arr = np.linspace(2e3, 1.7e4, 16).astype(int)\n",
3958339597
"ibm_shots_arr"
3958439598
]
3958539599
},
@@ -39672,7 +39686,9 @@
3967239686
}
3967339687
],
3967439688
"source": [
39675-
"ibm_limits_and_errors = neighbors_errors_over_shots(ibm_backend, circuit, n_data_qubits, ibm_shots_arr, use_cached=\"all\")"
39689+
"ibm_limits_and_errors = neighbors_errors_over_shots(\n",
39690+
" ibm_backend, circuit, n_data_qubits, ibm_shots_arr, use_cached=\"all\"\n",
39691+
")"
3967639692
]
3967739693
},
3967839694
{
@@ -42014,12 +42030,14 @@
4201442030
}
4201542031
],
4201642032
"source": [
42017-
"fig, (axy, axx) = plt.subplots(2, 1, figsize=(6, 7), gridspec_kw={'height_ratios': [10, 1]})\n",
42033+
"fig, (axy, axx) = plt.subplots(2, 1, figsize=(6, 7), gridspec_kw={\"height_ratios\": [10, 1]})\n",
4201842034
"ibm_errs_arrs = list(zip(*ibm_limits_and_errors))[1]\n",
4201942035
"for i, errs_arr in enumerate(list(zip(*limits_and_errors))[1], 1):\n",
4202042036
" color = (\"r\", \"b\")[i - 1]\n",
4202142037
" marker = (\"o\", \".\")[i - 1]\n",
42022-
" axy.scatter(shots_arr, errs_arr, marker=marker, c=color, label=rf\"Noise simulator ($d^\\mathrm{{max}} = {i}$)\")\n",
42038+
" axy.scatter(\n",
42039+
" shots_arr, errs_arr, marker=marker, c=color, label=rf\"Noise simulator ($d^\\mathrm{{max}} = {i}$)\"\n",
42040+
" )\n",
4202342041
" axx.scatter(shots_arr, errs_arr, marker=marker, c=color)\n",
4202442042
"\n",
4202542043
" params, r2 = fit_exp(shots_arr, errs_arr)\n",
@@ -42030,15 +42048,20 @@
4203042048
" f\"{linestyle}{color}\",\n",
4203142049
" label=rf\"Noise simulator $R^2={r2:.2}$ ($d^\\mathrm{{max}} = {i}$)\",\n",
4203242050
" )\n",
42033-
" \n",
42051+
"\n",
4203442052
" ibm_marker = (\"^\", \"v\")[i - 1]\n",
4203542053
" ibm_color = (\"m\", \"c\")[i - 1]\n",
4203642054
" ibm_shots_arr_, ibm_errs_arr_ = np.array(ibm_shots_arr), np.array(ibm_errs_arrs[i - 1])\n",
4203742055
" ibm_shots_arr_, ibm_errs_arr_ = ibm_shots_arr_, ibm_errs_arr_\n",
42038-
" axy.scatter(ibm_shots_arr_, ibm_errs_arr_, marker=ibm_marker, c=ibm_color, label=rf\"IBM ($d^\\mathrm{{max}} = {i}$)\")\n",
42056+
" axy.scatter(\n",
42057+
" ibm_shots_arr_,\n",
42058+
" ibm_errs_arr_,\n",
42059+
" marker=ibm_marker,\n",
42060+
" c=ibm_color,\n",
42061+
" label=rf\"IBM ($d^\\mathrm{{max}} = {i}$)\",\n",
42062+
" )\n",
4203942063
" axx.scatter(ibm_shots_arr_, ibm_errs_arr_, marker=ibm_marker, c=ibm_color)\n",
42040-
" \n",
42041-
" \n",
42064+
"\n",
4204242065
"\n",
4204342066
"axy.set_ylabel(r\"$N_\\mathrm{e}$, number of wrong neighbours\")\n",
4204442067
"axy.set_yscale(\"log\", base=2)\n",
@@ -42049,13 +42072,13 @@
4204942072
"axx.set_yticklabels([0])\n",
4205042073
"axx.set_ylim(-0.5, 0.5)\n",
4205142074
"\n",
42052-
"axy.spines['bottom'].set_visible(False)\n",
42075+
"axy.spines[\"bottom\"].set_visible(False)\n",
4205342076
"axy.set_xticks(np.linspace(0, 20e3, 21))\n",
4205442077
"axy.set_xticklabels([])\n",
4205542078
"axy.tick_params(axis=\"x\", width=0)\n",
4205642079
"axy.set_xlim(0, 2**14 + 1e3)\n",
4205742080
"\n",
42058-
"axx.spines['top'].set_visible(False)\n",
42081+
"axx.spines[\"top\"].set_visible(False)\n",
4205942082
"axx.set_xlabel(\"$N_\\mathrm{s}$, number of shots\")\n",
4206042083
"axx.set_xticks(np.linspace(0, 20e3, 21))\n",
4206142084
"axx.set_xticklabels([0] + [f\"{i}k\" for i in np.linspace(1, 20, 20).astype(int)])\n",
@@ -42070,8 +42093,6 @@
4207042093
"axx.set_axisbelow(True)\n",
4207142094
"\n",
4207242095
"\n",
42073-
"\n",
42074-
"\n",
4207542096
"fig.tight_layout()\n",
4207642097
"fig.savefig(f\"./ne-over-shots-n{n_data_qubits}.pdf\")\n",
4207742098
"fig.show()"
@@ -42087,7 +42108,7 @@
4208742108
"ERRORS_OVER_SHOTS = {\n",
4208842109
" \"IBM\": {\n",
4208942110
" \"4\": (ibm_shots_arr, ibm_limits_and_errors),\n",
42090-
" }, \n",
42111+
" },\n",
4209142112
" \"NOISE\": {\n",
4209242113
" \"4\": (shots_arr, limits_and_errors),\n",
4209342114
" },\n",
@@ -42227,7 +42248,13 @@
4222742248
"import re\n",
4222842249
"\n",
4222942250
"pattern = rf\"result-{circuit.name}-{ibm_backend.name}-(\\d+)-.*.json\"\n",
42230-
"ibm_shots_arr = sorted({int(m.group(1)) for item in (Path.cwd().parent / \"assets\").iterdir() if (m := re.match(pattern, str(item.name)))})\n",
42251+
"ibm_shots_arr = sorted(\n",
42252+
" {\n",
42253+
" int(m.group(1))\n",
42254+
" for item in (Path.cwd().parent / \"assets\").iterdir()\n",
42255+
" if (m := re.match(pattern, str(item.name)))\n",
42256+
" }\n",
42257+
")\n",
4223142258
"# ibm_shots_arr = np.linspace(2e3, 1.7e4, 16).astype(int)\n",
4223242259
"print(ibm_shots_arr)"
4223342260
]
@@ -44559,7 +44586,7 @@
4455944586
}
4456044587
],
4456144588
"source": [
44562-
"fig, (axy, axx) = plt.subplots(2, 1, figsize=(6, 5), gridspec_kw={'height_ratios': [10, 1]})\n",
44589+
"fig, (axy, axx) = plt.subplots(2, 1, figsize=(6, 5), gridspec_kw={\"height_ratios\": [10, 1]})\n",
4456344590
"ibm_errs_arrs = list(zip(*ibm_limits_and_errors))[1]\n",
4456444591
"for i, errs_arr in enumerate(list(zip(*limits_and_errors))[1], 1):\n",
4456544592
" color = (\"r\", \"b\")[i - 1]\n",
@@ -44569,17 +44596,19 @@
4456944596
"\n",
4457044597
" ibm_marker = (\"^\", \"v\")[i - 1]\n",
4457144598
" print(len(ibm_shots_arr), len(ibm_errs_arrs[i - 1]))\n",
44572-
" axy.scatter(ibm_shots_arr, ibm_errs_arrs[i - 1], marker=ibm_marker, c=color, label=f\"(IBM) Up to {i} neighbors\")\n",
44599+
" axy.scatter(\n",
44600+
" ibm_shots_arr, ibm_errs_arrs[i - 1], marker=ibm_marker, c=color, label=f\"(IBM) Up to {i} neighbors\"\n",
44601+
" )\n",
4457344602
" axx.scatter(ibm_shots_arr, ibm_errs_arrs[i - 1], marker=ibm_marker, c=color)\n",
44574-
" \n",
44603+
"\n",
4457544604
" params, r2 = fit_exp(shots_arr, errs_arr)\n",
4457644605
" axy.plot(\n",
4457744606
" shots_arr,\n",
4457844607
" np.exp(line(shots_arr, *params)),\n",
4457944608
" f\"{color}\",\n",
4458044609
" label=f\"Up to {i} neighbors R2={r2:.2}\",\n",
4458144610
" )\n",
44582-
" \n",
44611+
"\n",
4458344612
"\n",
4458444613
"axy.set_ylabel(r\"$N_\\mathrm{e}$, number of wrong neighbours\")\n",
4458544614
"axy.set_yscale(\"log\", base=2)\n",
@@ -44590,13 +44619,13 @@
4459044619
"axx.set_yticklabels([0])\n",
4459144620
"axx.set_ylim(-0.5, 0.5)\n",
4459244621
"\n",
44593-
"axy.spines['bottom'].set_visible(False)\n",
44622+
"axy.spines[\"bottom\"].set_visible(False)\n",
4459444623
"axy.set_xticks(np.linspace(1e4, 13e4, 13))\n",
4459544624
"axy.set_xticklabels([])\n",
4459644625
"axy.tick_params(axis=\"x\", width=0)\n",
4459744626
"axy.set_xlim(2**12, 13e4 + 5e3)\n",
4459844627
"\n",
44599-
"axx.spines['top'].set_visible(False)\n",
44628+
"axx.spines[\"top\"].set_visible(False)\n",
4460044629
"axx.set_xlabel(\"$N_\\mathrm{s}$, number of shots\")\n",
4460144630
"axx.set_xticks(np.linspace(1e4, 13e4, 13))\n",
4460244631
"axx.set_xticklabels([f\"{i}0k\" for i in np.linspace(1, 13, 13).astype(int)])\n",
@@ -44608,7 +44637,6 @@
4460844637
"axx.grid()\n",
4460944638
"\n",
4461044639
"\n",
44611-
"\n",
4461244640
"fig.tight_layout()\n",
4461344641
"fig.savefig(f\"./ne-over-shots-n{n_data_qubits}.pdf\")\n",
4461444642
"fig.show()"
@@ -47207,7 +47235,7 @@
4720747235
"(shots_arr, limits_and_errors) = ERRORS_OVER_SHOTS[\"NOISE\"][\"4\"]\n",
4720847236
"(ibm_shots_arr, ibm_limits_and_errors) = ERRORS_OVER_SHOTS[\"IBM\"][\"4\"]\n",
4720947237
"\n",
47210-
"fig, ((axy, axy_), (axx, axx_)) = plt.subplots(2, 2, figsize=(9, 5), gridspec_kw={'height_ratios': [10, 1]})\n",
47238+
"fig, ((axy, axy_), (axx, axx_)) = plt.subplots(2, 2, figsize=(9, 5), gridspec_kw={\"height_ratios\": [10, 1]})\n",
4721147239
"ibm_errs_arrs = list(zip(*ibm_limits_and_errors))[1]\n",
4721247240
"for i, errs_arr in enumerate(list(zip(*limits_and_errors))[1], 1):\n",
4721347241
" color = (\"r\", \"b\")[i - 1]\n",
@@ -47225,7 +47253,7 @@
4722547253
" # label=rf\"Noise simulator $R^2={r2:.2}$ ($d^\\mathrm{{max}} = {i}$)\",\n",
4722647254
" label=rf\"Noise simulator $R^2={r2:.2}$\",\n",
4722747255
" )\n",
47228-
" \n",
47256+
"\n",
4722947257
" ibm_marker = (\"^\", \"v\")[i - 1]\n",
4723047258
" ibm_color = (\"m\", \"c\")[i - 1]\n",
4723147259
" ibm_shots_arr_, ibm_errs_arr_ = np.array(ibm_shots_arr), np.array(ibm_errs_arrs[i - 1])\n",
@@ -47234,7 +47262,7 @@
4723447262
" # axy.scatter(ibm_shots_arr_, ibm_errs_arr_, marker=ibm_marker, c=ibm_color, label=rf\"IBM ($d^\\mathrm{{max}} = {i}$)\")\n",
4723547263
" axy.scatter(ibm_shots_arr_, ibm_errs_arr_, marker=ibm_marker, c=ibm_color, label=rf\"IBM Sherbrooke\")\n",
4723647264
" axx.scatter(ibm_shots_arr_, ibm_errs_arr_, marker=ibm_marker, c=ibm_color)\n",
47237-
" \n",
47265+
"\n",
4723847266
" break\n",
4723947267
"\n",
4724047268
"axy.set_ylabel(r\"$N_\\mathrm{e}$, number of wrong neighbours\")\n",
@@ -47246,13 +47274,13 @@
4724647274
"axx.set_yticklabels([0])\n",
4724747275
"axx.set_ylim(-0.5, 0.5)\n",
4724847276
"\n",
47249-
"axy.spines['bottom'].set_visible(False)\n",
47277+
"axy.spines[\"bottom\"].set_visible(False)\n",
4725047278
"axy.set_xticks(np.linspace(0, 18e3, 10))\n",
4725147279
"axy.set_xticklabels([])\n",
4725247280
"axy.tick_params(axis=\"x\", width=0)\n",
4725347281
"axy.set_xlim(0, 18e3)\n",
4725447282
"\n",
47255-
"axx.spines['top'].set_visible(False)\n",
47283+
"axx.spines[\"top\"].set_visible(False)\n",
4725647284
"axx.set_xlabel(\"$N_\\mathrm{s}$, number of shots\")\n",
4725747285
"axx.set_xticks(np.linspace(0, 18e3, 10))\n",
4725847286
"axx.set_xticklabels([0 if i == 0 else f\"{i}k\" for i in np.linspace(0, 18, 10).astype(int)])\n",
@@ -47269,7 +47297,7 @@
4726947297
"axx.set_axisbelow(True)\n",
4727047298
"\n",
4727147299
"\n",
47272-
"axx.text(-2000, -2, \"a\", color=\"k\", fontsize='14', fontweight=\"bold\")\n",
47300+
"axx.text(-2000, -2, \"a\", color=\"k\", fontsize=\"14\", fontweight=\"bold\")\n",
4727347301
"\n",
4727447302
"\n",
4727547303
"(shots_arr, limits_and_errors) = ERRORS_OVER_SHOTS[\"NOISE\"][\"5\"]\n",
@@ -47294,7 +47322,7 @@
4729447322
" # label=rf\"Noise simulator $R^2={r2:.2}$ ($d^\\mathrm{{max}} = {i}$)\",\n",
4729547323
" label=rf\"Noise simulator $R^2={r2:.2}$\",\n",
4729647324
" )\n",
47297-
" \n",
47325+
"\n",
4729847326
" ibm_marker = (\"^\", \"v\")[i - 1]\n",
4729947327
" ibm_color = (\"m\", \"c\")[i - 1]\n",
4730047328
" ibm_shots_arr_, ibm_errs_arr_ = np.array(ibm_shots_arr), np.array(ibm_errs_arrs[i - 1])\n",
@@ -47303,7 +47331,7 @@
4730347331
" # axy.scatter(ibm_shots_arr_, ibm_errs_arr_, marker=ibm_marker, c=ibm_color, label=rf\"IBM ($d^\\mathrm{{max}} = {i}$)\")\n",
4730447332
" axy.scatter(ibm_shots_arr_, ibm_errs_arr_, marker=ibm_marker, c=ibm_color, label=rf\"IBM Sherbrooke\")\n",
4730547333
" axx.scatter(ibm_shots_arr_, ibm_errs_arr_, marker=ibm_marker, c=ibm_color)\n",
47306-
" \n",
47334+
"\n",
4730747335
" break\n",
4730847336
"\n",
4730947337
"# axy.set_ylabel(r\"$N_\\mathrm{e}$, number of wrong neighbours\")\n",
@@ -47315,13 +47343,13 @@
4731547343
"axx.set_yticklabels([0])\n",
4731647344
"axx.set_ylim(-0.5, 0.5)\n",
4731747345
"\n",
47318-
"axy.spines['bottom'].set_visible(False)\n",
47346+
"axy.spines[\"bottom\"].set_visible(False)\n",
4731947347
"axy.set_xticks(np.linspace(0, 14e4, 8))\n",
4732047348
"axy.set_xticklabels([])\n",
4732147349
"axy.tick_params(axis=\"x\", width=0)\n",
4732247350
"axy.set_xlim(0, 14e4)\n",
4732347351
"\n",
47324-
"axx.spines['top'].set_visible(False)\n",
47352+
"axx.spines[\"top\"].set_visible(False)\n",
4732547353
"axx.set_xlabel(\"$N_\\mathrm{s}$, number of shots\")\n",
4732647354
"axx.set_xticks(np.linspace(0, 14e4, 8))\n",
4732747355
"axx.set_xticklabels([0 if i == 0 else f\"{i}0k\" for i in np.linspace(0, 14, 8).astype(int)])\n",
@@ -47334,7 +47362,7 @@
4733447362
"# axy.set_title(f\"{min_qubits_for_data_encoding(x)} data qubits\")\n",
4733547363
"axx.grid()\n",
4733647364
"\n",
47337-
"axx.text(-15500, -2, \"b\", color=\"k\", fontsize='14', fontweight=\"bold\")\n",
47365+
"axx.text(-15500, -2, \"b\", color=\"k\", fontsize=\"14\", fontweight=\"bold\")\n",
4733847366
"\n",
4733947367
"\n",
4734047368
"fig.tight_layout()\n",
@@ -47422,7 +47450,7 @@
4742247450
"\n",
4742347451
"# 99|101|117|119||98|102|116|120||100|110|118-cw89p5r9ezk00080zv40\n",
4742447452
"\n",
47425-
"results = run_on_backend(ibm_backend, circuit, shots=shots, use_cached=\"all\") # \"cw89p5r9ezk00080zv40\""
47453+
"results = run_on_backend(ibm_backend, circuit, shots=shots, use_cached=\"all\") # \"cw89p5r9ezk00080zv40\""
4742647454
]
4742747455
},
4742847456
{
@@ -55981,7 +56009,7 @@
5598156009
"# maps = [[109, 95, 91, 101, 110], [114, 94, 79, 102, 118], [96, 97, 98, 99, 100]]\n",
5598256010
"# maps = [[123, 121, 103, 93, 107], [124, 120, 102, 87, 108], [122, 111, 104, 105, 106]]\n",
5598356011
"# maps = [[46, 35, 50, 69, 67], [45, 28, 51, 70, 66], [47, 48, 49, 55, 68]]\n",
55984-
"results = run_on_backend(ibm_backend, circuit, shots=shots, use_cached=\"all\") # cxe6nf5ky7rg0083pew0"
56012+
"results = run_on_backend(ibm_backend, circuit, shots=shots, use_cached=\"all\") # cxe6nf5ky7rg0083pew0"
5598556013
]
5598656014
},
5598756015
{

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