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ciaranraciaranra
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Lines changed: 3 additions & 105 deletions

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‎examples/surface_code_experiments.ipynb‎

Lines changed: 2 additions & 3 deletions
Original file line numberDiff line numberDiff line change
@@ -39,7 +39,6 @@
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},
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"outputs": [],
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"source": [
42-
"import time\n",
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"from typing import Any\n",
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"\n",
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"import matplotlib.pyplot as plt\n",
@@ -1050,7 +1049,7 @@
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" label=f\"d={d}\", markersize=10, linewidth=2)\n",
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"\n",
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" ax.set_xlabel(\"Physical Error Rate\", fontsize=14)\n",
1053-
" ax.set_ylabel(f\"Logical Error Rate over d rounds\", fontsize=14)\n",
1052+
" ax.set_ylabel(\"Logical Error Rate over d rounds\", fontsize=14)\n",
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" ax.set_title(f\"Z Basis: {dt} (per d rounds)\", fontsize=14)\n",
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" ax.set_xscale(\"log\")\n",
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" ax.set_yscale(\"log\")\n",
@@ -1207,4 +1206,4 @@
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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}

‎examples/surface_code_threshold.ipynb‎

Lines changed: 1 addition & 102 deletions
Original file line numberDiff line numberDiff line change
@@ -633,108 +633,7 @@
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"id": "threshold",
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"metadata": {},
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"outputs": [],
636-
"source": [
637-
"def estimate_threshold_crossings(results, distances):\n",
638-
" \"\"\"Estimate threshold from crossings between consecutive distance curves.\"\"\"\n",
639-
" crossings = []\n",
640-
"\n",
641-
" for idx in range(len(distances) - 1):\n",
642-
" d1 = distances[idx]\n",
643-
" d2 = distances[idx + 1]\n",
644-
"\n",
645-
" pts1 = {p: ler for (d, p, ler, _) in results if d == d1}\n",
646-
" pts2 = {p: ler for (d, p, ler, _) in results if d == d2}\n",
647-
"\n",
648-
" common_ps = sorted(set(pts1.keys()) & set(pts2.keys()))\n",
649-
" if len(common_ps) < 2:\n",
650-
" continue\n",
651-
"\n",
652-
" for j in range(len(common_ps) - 1):\n",
653-
" p_lo = common_ps[j]\n",
654-
" p_hi = common_ps[j + 1]\n",
655-
" diff_lo = pts1[p_lo] - pts2[p_lo]\n",
656-
" diff_hi = pts1[p_hi] - pts2[p_hi]\n",
657-
"\n",
658-
" if diff_lo * diff_hi < 0:\n",
659-
" t = diff_lo / (diff_lo - diff_hi)\n",
660-
" p_cross = p_lo + t * (p_hi - p_lo)\n",
661-
" crossings.append((d1, d2, p_cross))\n",
662-
"\n",
663-
" return crossings\n",
664-
"\n",
665-
"\n",
666-
"def estimate_threshold_fit(results, distances, fit_func=func, p0=None):\n",
667-
" \"\"\"Estimate threshold using universal scaling fit.\"\"\"\n",
668-
" filtered = [(d, p, ler) for (d, p, ler, _) in results if ler > 0 and d in distances]\n",
669-
" if len(filtered) < 5:\n",
670-
" return None\n",
671-
"\n",
672-
" plist = np.array([p for _, p, _ in filtered])\n",
673-
" dlist = np.array([float(d) for d, _, _ in filtered])\n",
674-
" plog = np.array([ler for _, _, ler in filtered])\n",
675-
"\n",
676-
" if p0 is None:\n",
677-
" if fit_func == func6:\n",
678-
" p0 = [0.5, 0.005]\n",
679-
" else:\n",
680-
" p0 = [0.005, 1.5, 0.5, 1.0, 0.0]\n",
681-
"\n",
682-
" try:\n",
683-
" popt, stdev = threshold_fit(plist, dlist, plog, fit_func, p0)\n",
684-
" return popt, stdev\n",
685-
" except Exception as e:\n",
686-
" print(f\" Fit failed: {e}\")\n",
687-
" return None\n",
688-
"\n",
689-
"\n",
690-
"# === Build comparison table ===\n",
691-
"print(\"=== Threshold Comparison ===\")\n",
692-
"print()\n",
693-
"header = f\"{'Variant':<22} | {'Z crossing':>10} | {'Z fit (func6)':>16} | {'X crossing':>10} | {'X fit (func6)':>16}\"\n",
694-
"print(header)\n",
695-
"print(\"-\" * len(header))\n",
696-
"\n",
697-
"for v in VARIANTS:\n",
698-
" row = f\"{variant_labels[v]:<22}\"\n",
699-
"\n",
700-
" for basis_results in [results_z, results_x]:\n",
701-
" data = basis_results[v]\n",
702-
"\n",
703-
" # Crossing estimate\n",
704-
" crossings = estimate_threshold_crossings(data, DISTANCES)\n",
705-
" if crossings:\n",
706-
" avg_cross = np.mean([c[2] for c in crossings])\n",
707-
" row += f\" | {avg_cross:>10.4f}\"\n",
708-
" else:\n",
709-
" row += f\" | {'N/A':>10}\"\n",
710-
"\n",
711-
" # Fit estimate\n",
712-
" result = estimate_threshold_fit(data, DISTANCES, func6, p0=[0.5, 0.005])\n",
713-
" if result is not None:\n",
714-
" popt, stdev = result\n",
715-
" row += f\" | {popt[1]:>7.4f}+/-{stdev[1]:<5.4f}\"\n",
716-
" else:\n",
717-
" row += f\" | {'N/A':>16}\"\n",
718-
"\n",
719-
" print(row)\n",
720-
"\n",
721-
"print()\n",
722-
"\n",
723-
"# === Detailed crossing info ===\n",
724-
"print(\"\\n=== Detailed Curve Crossings ===\")\n",
725-
"for v in VARIANTS:\n",
726-
" print(f\"\\n--- {variant_labels[v]} ---\")\n",
727-
" for basis, basis_results in [(\"Z\", results_z[v]), (\"X\", results_x[v])]:\n",
728-
" crossings = estimate_threshold_crossings(basis_results, DISTANCES)\n",
729-
" print(f\" {basis} basis:\")\n",
730-
" if crossings:\n",
731-
" for d1, d2, p_cross in crossings:\n",
732-
" print(f\" d={d1}/d={d2} crossing at p ~ {p_cross:.4f}\")\n",
733-
" avg = np.mean([c[2] for c in crossings])\n",
734-
" print(f\" Average: p ~ {avg:.4f}\")\n",
735-
" else:\n",
736-
" print(\" No crossings found.\")"
737-
]
636+
"source": "def estimate_threshold_crossings(results, distances):\n \"\"\"Estimate threshold from crossings between consecutive distance curves.\"\"\"\n crossings = []\n\n for idx in range(len(distances) - 1):\n d1 = distances[idx]\n d2 = distances[idx + 1]\n\n pts1 = {p: ler for (d, p, ler, _) in results if d == d1}\n pts2 = {p: ler for (d, p, ler, _) in results if d == d2}\n\n common_ps = sorted(set(pts1.keys()) & set(pts2.keys()))\n if len(common_ps) < 2:\n continue\n\n for j in range(len(common_ps) - 1):\n p_lo = common_ps[j]\n p_hi = common_ps[j + 1]\n diff_lo = pts1[p_lo] - pts2[p_lo]\n diff_hi = pts1[p_hi] - pts2[p_hi]\n\n if diff_lo * diff_hi < 0:\n t = diff_lo / (diff_lo - diff_hi)\n p_cross = p_lo + t * (p_hi - p_lo)\n crossings.append((d1, d2, p_cross))\n\n return crossings\n\n\ndef estimate_threshold_fit(results, distances, fit_func=func, p0=None):\n \"\"\"Estimate threshold using universal scaling fit.\"\"\"\n filtered = [(d, p, ler) for (d, p, ler, _) in results if ler > 0 and d in distances]\n if len(filtered) < 5:\n return None\n\n plist = np.array([p for _, p, _ in filtered])\n dlist = np.array([float(d) for d, _, _ in filtered])\n plog = np.array([ler for _, _, ler in filtered])\n\n if p0 is None:\n p0 = [0.5, 0.005] if fit_func == func6 else [0.005, 1.5, 0.5, 1.0, 0.0]\n\n try:\n popt, stdev = threshold_fit(plist, dlist, plog, fit_func, p0)\n except (RuntimeError, ValueError) as e:\n print(f\" Fit failed: {e}\")\n return None\n else:\n return popt, stdev\n\n\n# === Build comparison table ===\nprint(\"=== Threshold Comparison ===\")\nprint()\nheader = f\"{'Variant':<22} | {'Z crossing':>10} | {'Z fit (func6)':>16} | {'X crossing':>10} | {'X fit (func6)':>16}\"\nprint(header)\nprint(\"-\" * len(header))\n\nfor v in VARIANTS:\n row = f\"{variant_labels[v]:<22}\"\n\n for basis_results in [results_z, results_x]:\n data = basis_results[v]\n\n # Crossing estimate\n crossings = estimate_threshold_crossings(data, DISTANCES)\n if crossings:\n avg_cross = np.mean([c[2] for c in crossings])\n row += f\" | {avg_cross:>10.4f}\"\n else:\n row += f\" | {'N/A':>10}\"\n\n # Fit estimate\n result = estimate_threshold_fit(data, DISTANCES, func6, p0=[0.5, 0.005])\n if result is not None:\n popt, stdev = result\n row += f\" | {popt[1]:>7.4f}+/-{stdev[1]:<5.4f}\"\n else:\n row += f\" | {'N/A':>16}\"\n\n print(row)\n\nprint()\n\n# === Detailed crossing info ===\nprint(\"\\n=== Detailed Curve Crossings ===\")\nfor v in VARIANTS:\n print(f\"\\n--- {variant_labels[v]} ---\")\n for basis, basis_results in [(\"Z\", results_z[v]), (\"X\", results_x[v])]:\n crossings = estimate_threshold_crossings(basis_results, DISTANCES)\n print(f\" {basis} basis:\")\n if crossings:\n for d1, d2, p_cross in crossings:\n print(f\" d={d1}/d={d2} crossing at p ~ {p_cross:.4f}\")\n avg = np.mean([c[2] for c in crossings])\n print(f\" Average: p ~ {avg:.4f}\")\n else:\n print(\" No crossings found.\")"
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},
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{
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"cell_type": "markdown",

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