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Merged
Soyoung Shin (0sophy1) merged 8 commits into from
Sep 11, 2026
Merged

Improve PQK tutorial, closes 5604 #5608

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a966f94
Update projected-quantum-kernels.ipynb
0sophy1 Sep 4, 2026
a21eb5a
Update projected-quantum-kernels.ipynb
0sophy1 Sep 4, 2026
41956fe
Update projected-quantum-kernels.ipynb
0sophy1 Sep 4, 2026
70c8c8b
Merge branch 'Qiskit:main' into main
0sophy1 Sep 10, 2026
46c37ad
Update projected-quantum-kernels.ipynb
0sophy1 Sep 10, 2026
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Update projected-quantum-kernels.ipynb
0sophy1 Sep 10, 2026
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Update projected-quantum-kernels.ipynb
0sophy1 Sep 10, 2026
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updated parameter
0sophy1 Sep 11, 2026
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84 changes: 57 additions & 27 deletions docs/tutorials/projected-quantum-kernels.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -70,7 +70,7 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 1,
"id": "fe8a02d3-994a-45fe-823d-5e68ded0d717",
"metadata": {},
"outputs": [],
Expand All @@ -79,6 +79,8 @@
"\n",
"# Standard libraries\n",
"import os\n",
"import urllib.request\n",
"from pathlib import Path\n",
"import numpy as np\n",
"import pandas as pd\n",
"\n",
Expand Down Expand Up @@ -153,7 +155,7 @@
},
{
"cell_type": "code",
"execution_count": 2,
"execution_count": null,
"id": "f1ce0222-e7c7-451c-a11c-e61425a6bb8e",
"metadata": {},
"outputs": [],
Expand All @@ -165,12 +167,10 @@
" # Read from the csv files\n",
" train_data = pd.read_csv(\n",
" os.path.join(dir_root, args[\"file_train_data\"]),\n",
" encoding=\"unicode_escape\",\n",
" sep=\",\",\n",
" )\n",
" test_data = pd.read_csv(\n",
" os.path.join(dir_root, args[\"file_test_data\"]),\n",
" encoding=\"unicode_escape\",\n",
" sep=\",\",\n",
" )\n",
"\n",
Expand Down Expand Up @@ -320,29 +320,52 @@
"execution_count": null,
"id": "84495ee1-880a-48cb-a904-d83396e8b29e",
"metadata": {},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" train_data.csv ... OK (5,532 bytes)\n",
" test_data.csv ... OK (2,420 bytes)\n",
" projections_train.csv ... OK (779,903 bytes)\n",
" projections_test.csv ... OK (335,604 bytes)\n",
"\n",
"All files saved to data_tutorial/pqk/\n"
]
}
],
"source": [
"## Download dataset\n",
"\n",
"# Create data directory if it doesn't exist\n",
"!mkdir -p data_tutorial/pqk\n",
"\n",
"# Download the training and test sets from the official Qiskit documentation repo\n",
"!wget -q --show-progress -O data_tutorial/pqk/train_data.csv \\\n",
" https://raw.githubusercontent.com/Qiskit/documentation/main/datasets/tutorials/pqk/train_data.csv\n",
"def download_pqk_dataset(data_dir=\"data_tutorial/pqk\"):\n",
" \"\"\"Download the four CSV files from the Qiskit documentation repo.\"\"\"\n",
" data_dir = Path(data_dir)\n",
" data_dir.mkdir(parents=True, exist_ok=True)\n",
"\n",
"!wget -q --show-progress -O data_tutorial/pqk/test_data.csv \\\n",
" https://raw.githubusercontent.com/Qiskit/documentation/main/datasets/tutorials/pqk/test_data.csv\n",
" base_url = (\n",
" \"https://raw.githubusercontent.com/Qiskit/documentation/main/\"\n",
" \"datasets/tutorials/pqk\"\n",
" )\n",
" files = [\n",
" \"train_data.csv\",\n",
" \"test_data.csv\",\n",
" \"projections_train.csv\",\n",
" \"projections_test.csv\",\n",
" ]\n",
"\n",
" for filename in files:\n",
" url = f\"{base_url}/{filename}\"\n",
" dest = data_dir / filename\n",
" print(f\" {filename} ...\", end=\" \", flush=True)\n",
" urllib.request.urlretrieve(url, dest)\n",
" print(f\"OK ({dest.stat().st_size:,} bytes)\")\n",
"\n",
"!wget -q --show-progress -O data_tutorial/pqk/projections_train.csv \\\n",
" https://raw.githubusercontent.com/Qiskit/documentation/main/datasets/tutorials/pqk/projections_train.csv\n",
" print(f\"\\nAll files saved to {data_dir}/\")\n",
" return data_dir\n",
"\n",
"!wget -q --show-progress -O data_tutorial/pqk/projections_test.csv \\\n",
" https://raw.githubusercontent.com/Qiskit/documentation/main/datasets/tutorials/pqk/projections_test.csv\n",
"\n",
"# Check the files have been downloaded\n",
"!echo \"Dataset files downloaded:\"\n",
"!ls -lh data_tutorial/pqk/*.csv"
"DATA_DIR = download_pqk_dataset()"
]
},
{
Expand Down Expand Up @@ -371,7 +394,9 @@
" \"min_label_value\": -1,\n",
" \"encoder\": \"one-hot\",\n",
"}\n",
"dir_root = \"./\"\n",
"\n",
"# dir_root points to the folder where the downloaded CSVs live\n",
"dir_root = str(DATA_DIR)\n",
"\n",
"# Preprocess data\n",
"train_data, test_data, train_labels, test_labels, num_class, num_motifs = (\n",
Expand All @@ -394,7 +419,7 @@
},
{
"cell_type": "code",
"execution_count": 4,
"execution_count": null,
"id": "d40d9a0f-67d0-4704-8a94-cc0a466ffc92",
"metadata": {},
"outputs": [],
Expand Down Expand Up @@ -712,7 +737,7 @@
},
{
"cell_type": "code",
"execution_count": 11,
"execution_count": null,
"id": "7a56eda8-3fb2-43e9-8a82-ec64be05b699",
"metadata": {},
"outputs": [],
Expand Down Expand Up @@ -1191,6 +1216,11 @@
"metadata": {},
"outputs": [],
"source": [
"# ---------------------------------------------------------------------------\n",
"# Load projections — either from the hardware run above, or from the\n",
"# pre-computed CSVs that were downloaded alongside the motif data.\n",
"# ---------------------------------------------------------------------------\n",
"\n",
"if run_experiment:\n",
" projections_train = np.array(projections_train).reshape(\n",
" len(projections_train), -1\n",
Expand All @@ -1199,8 +1229,8 @@
" len(projections_test), -1\n",
" )\n",
"else:\n",
" projections_train = np.loadtxt(\"projections_train.txt\")\n",
" projections_test = np.loadtxt(\"projections_test.txt\")"
" projections_train = np.loadtxt(DATA_DIR / \"projections_train.csv\")\n",
" projections_test = np.loadtxt(DATA_DIR / \"projections_test.csv\")"
]
},
{
Expand Down Expand Up @@ -1520,6 +1550,7 @@
}
],
"metadata": {
"hours": 2.5,
"kernelspec": {
"display_name": "Python 3",
"language": "python",
Expand All @@ -1537,15 +1568,14 @@
"pygments_lexer": "ipython3",
"version": "3"
},
"qpuSeconds": 4800,
"widgets": {
"application/vnd.jupyter.widget-state+json": {
"state": {},
"version_major": 2,
"version_minor": 0
}
},
"hours": 2.5,
"qpuSeconds": 4800
}
},
"nbformat": 4,
"nbformat_minor": 4
Expand Down
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