From a966f941329565c819ef3b1787349dd601525b42 Mon Sep 17 00:00:00 2001
From: Soyoung Shin <63990199+0sophy1@users.noreply.github.com>
Date: Fri, 4 Sep 2026 14:06:56 -0700
Subject: [PATCH 1/7] Update projected-quantum-kernels.ipynb
---
.../tutorials/projected-quantum-kernels.ipynb | 185 +++++++++++++++---
1 file changed, 161 insertions(+), 24 deletions(-)
diff --git a/docs/tutorials/projected-quantum-kernels.ipynb b/docs/tutorials/projected-quantum-kernels.ipynb
index b2f9c3118225..8405b26325c3 100644
--- a/docs/tutorials/projected-quantum-kernels.ipynb
+++ b/docs/tutorials/projected-quantum-kernels.ipynb
@@ -62,6 +62,18 @@
"- Tqdm 4.67.1 (`pip install tqdm`)"
]
},
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "pip-install-requirements",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#!pip install -U \"qiskit[visualization]\" qiskit-ibm-runtime \\\n",
+ "# category-encoders==2.8.1 numpy==2.3.2 pandas==2.3.2 \\\n",
+ "# scikit-learn tqdm==4.67.1"
+ ]
+ },
{
"cell_type": "markdown",
"id": "2c676996-1361-4b3a-9c94-4784376097b0",
@@ -72,7 +84,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 1,
"id": "fe8a02d3-994a-45fe-823d-5e68ded0d717",
"metadata": {},
"outputs": [],
@@ -81,6 +93,8 @@
"\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",
@@ -322,29 +336,125 @@
"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",
+ " 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/test_data.csv \\\n",
- " https://raw.githubusercontent.com/Qiskit/documentation/main/datasets/tutorials/pqk/test_data.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_train.csv \\\n",
- " https://raw.githubusercontent.com/Qiskit/documentation/main/datasets/tutorials/pqk/projections_train.csv\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",
+ "DATA_DIR = download_pqk_dataset()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "sanitize-csv-fixed",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " Sanitized train_data.csv\n",
+ " Sanitized test_data.csv\n",
+ " Sanitized projections_train.csv\n",
+ " Sanitized projections_test.csv\n"
+ ]
+ }
+ ],
+ "source": [
+ "# ---------------------------------------------------------------------------\n",
+ "# Sanitize CSV files.\n",
+ "# ---------------------------------------------------------------------------\n",
+ "\n",
+ "\n",
+ "def sanitize_csv_files(data_dir):\n",
+ " \"\"\"Normalize quirky CSV files to well-formed UTF-8 comma-separated CSVs.\"\"\"\n",
+ " data_dir = Path(data_dir)\n",
+ "\n",
+ " # Motif files: strip BOM and outer line-wrap quotes if present.\n",
+ " for name in [\"train_data.csv\", \"test_data.csv\"]:\n",
+ " path = data_dir / name\n",
+ " if not path.exists():\n",
+ " continue\n",
+ " with open(path, \"rb\") as f:\n",
+ " raw = f.read()\n",
+ " text = raw.decode(\"utf-8-sig\")\n",
+ " lines = text.replace(\"\\r\\n\", \"\\n\").strip().split(\"\\n\")\n",
+ " wrapped = all(\n",
+ " len(L) >= 2 and L.startswith('\"') and L.endswith('\"')\n",
+ " for L in lines\n",
+ " )\n",
+ " if wrapped:\n",
+ " lines = [L[1:-1] for L in lines]\n",
+ " cleaned = \"\\n\".join(lines) + \"\\n\"\n",
+ " if cleaned.encode(\"utf-8\") != raw:\n",
+ " path.write_text(cleaned, encoding=\"utf-8\")\n",
+ " print(f\" Sanitized {name}\")\n",
+ "\n",
+ " # Projection files: convert whitespace-separated to comma-separated if needed.\n",
+ " for name in [\"projections_train.csv\", \"projections_test.csv\"]:\n",
+ " path = data_dir / name\n",
+ " if not path.exists():\n",
+ " continue\n",
+ " with open(path, \"rb\") as f:\n",
+ " head = f.read(2048)\n",
+ " text_head = head.decode(\"utf-8-sig\", errors=\"replace\")\n",
+ " first_line = text_head.split(\"\\n\")[0]\n",
+ " # If the first line has many whitespace-separated floats and no commas,\n",
+ " # it's the whitespace-separated format.\n",
+ " if \",\" not in first_line and len(first_line.split()) > 10:\n",
+ " from io import StringIO\n",
+ "\n",
+ " with open(path, \"rb\") as f:\n",
+ " text = f.read().decode(\"utf-8-sig\")\n",
+ " df = pd.read_csv(\n",
+ " StringIO(text), sep=r\"\\s+\", header=None, engine=\"python\"\n",
+ " )\n",
+ " df.to_csv(path, index=False, header=False)\n",
+ " print(f\" Sanitized {name}\")\n",
+ "\n",
"\n",
- "# Check the files have been downloaded\n",
- "!echo \"Dataset files downloaded:\"\n",
- "!ls -lh data_tutorial/pqk/*.csv"
+ "sanitize_csv_files(DATA_DIR)"
]
},
{
@@ -373,7 +483,9 @@
" \"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",
@@ -396,7 +508,7 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": null,
"id": "d40d9a0f-67d0-4704-8a94-cc0a466ffc92",
"metadata": {},
"outputs": [],
@@ -714,7 +826,7 @@
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": null,
"id": "7a56eda8-3fb2-43e9-8a82-ec64be05b699",
"metadata": {},
"outputs": [],
@@ -1193,6 +1305,31 @@
"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",
+ "# FIX: the original tutorial loaded .txt files with np.loadtxt, but the\n",
+ "# downloaded files are .csv (and may be comma- or whitespace-separated\n",
+ "# depending on the repo version). sanitize_csv_files() above has already\n",
+ "# normalised them to comma-separated CSV, so we just use pd.read_csv here.\n",
+ "# ---------------------------------------------------------------------------\n",
+ "\n",
+ "\n",
+ "def _load_projections(path):\n",
+ " \"\"\"Load a projections CSV regardless of whether it has a header row.\"\"\"\n",
+ " df = pd.read_csv(path)\n",
+ " # If every column name is parseable as a float the first row is actually\n",
+ " # data, not a header — reload without a header in that case.\n",
+ " try:\n",
+ " df.columns.astype(float)\n",
+ " df = pd.read_csv(path, header=None)\n",
+ " except (ValueError, TypeError):\n",
+ " pass\n",
+ " df.columns = [f\"q{i}\" for i in range(df.shape[1])]\n",
+ " return df\n",
+ "\n",
+ "\n",
"if run_experiment:\n",
" projections_train = np.array(projections_train).reshape(\n",
" len(projections_train), -1\n",
@@ -1201,8 +1338,8 @@
" 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 = _load_projections(DATA_DIR / \"projections_train.csv\")\n",
+ " projections_test = _load_projections(DATA_DIR / \"projections_test.csv\")"
]
},
{
@@ -1522,6 +1659,7 @@
}
],
"metadata": {
+ "hours": 2.5,
"kernelspec": {
"display_name": "Python 3",
"language": "python",
@@ -1539,15 +1677,14 @@
"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
From a21eb5a6af59e1b1f993ee692321b76c9a53a90f Mon Sep 17 00:00:00 2001
From: Soyoung Shin <63990199+0sophy1@users.noreply.github.com>
Date: Fri, 4 Sep 2026 14:09:08 -0700
Subject: [PATCH 2/7] Update projected-quantum-kernels.ipynb
---
docs/tutorials/projected-quantum-kernels.ipynb | 12 ------------
1 file changed, 12 deletions(-)
diff --git a/docs/tutorials/projected-quantum-kernels.ipynb b/docs/tutorials/projected-quantum-kernels.ipynb
index 8405b26325c3..81e13cc6f94d 100644
--- a/docs/tutorials/projected-quantum-kernels.ipynb
+++ b/docs/tutorials/projected-quantum-kernels.ipynb
@@ -62,18 +62,6 @@
"- Tqdm 4.67.1 (`pip install tqdm`)"
]
},
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "pip-install-requirements",
- "metadata": {},
- "outputs": [],
- "source": [
- "#!pip install -U \"qiskit[visualization]\" qiskit-ibm-runtime \\\n",
- "# category-encoders==2.8.1 numpy==2.3.2 pandas==2.3.2 \\\n",
- "# scikit-learn tqdm==4.67.1"
- ]
- },
{
"cell_type": "markdown",
"id": "2c676996-1361-4b3a-9c94-4784376097b0",
From 41956feee2410506247634b45ce456ef8da30de4 Mon Sep 17 00:00:00 2001
From: Soyoung Shin <63990199+0sophy1@users.noreply.github.com>
Date: Fri, 4 Sep 2026 14:11:58 -0700
Subject: [PATCH 3/7] Update projected-quantum-kernels.ipynb
---
docs/tutorials/projected-quantum-kernels.ipynb | 5 -----
1 file changed, 5 deletions(-)
diff --git a/docs/tutorials/projected-quantum-kernels.ipynb b/docs/tutorials/projected-quantum-kernels.ipynb
index 81e13cc6f94d..de1364cd770b 100644
--- a/docs/tutorials/projected-quantum-kernels.ipynb
+++ b/docs/tutorials/projected-quantum-kernels.ipynb
@@ -1296,11 +1296,6 @@
"# ---------------------------------------------------------------------------\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",
- "# FIX: the original tutorial loaded .txt files with np.loadtxt, but the\n",
- "# downloaded files are .csv (and may be comma- or whitespace-separated\n",
- "# depending on the repo version). sanitize_csv_files() above has already\n",
- "# normalised them to comma-separated CSV, so we just use pd.read_csv here.\n",
"# ---------------------------------------------------------------------------\n",
"\n",
"\n",
From 46c37ad35f92eca3e1234c2f23d03e28a71c4a8e Mon Sep 17 00:00:00 2001
From: Soyoung Shin <63990199+0sophy1@users.noreply.github.com>
Date: Thu, 10 Sep 2026 11:43:09 -0700
Subject: [PATCH 4/7] Update projected-quantum-kernels.ipynb
---
.../tutorials/projected-quantum-kernels.ipynb | 73 -------------------
1 file changed, 73 deletions(-)
diff --git a/docs/tutorials/projected-quantum-kernels.ipynb b/docs/tutorials/projected-quantum-kernels.ipynb
index 753bda4ee39b..3eaac1b45429 100644
--- a/docs/tutorials/projected-quantum-kernels.ipynb
+++ b/docs/tutorials/projected-quantum-kernels.ipynb
@@ -370,79 +370,6 @@
"DATA_DIR = download_pqk_dataset()"
]
},
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "sanitize-csv-fixed",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- " Sanitized train_data.csv\n",
- " Sanitized test_data.csv\n",
- " Sanitized projections_train.csv\n",
- " Sanitized projections_test.csv\n"
- ]
- }
- ],
- "source": [
- "# ---------------------------------------------------------------------------\n",
- "# Sanitize CSV files.\n",
- "# ---------------------------------------------------------------------------\n",
- "\n",
- "\n",
- "def sanitize_csv_files(data_dir):\n",
- " \"\"\"Normalize quirky CSV files to well-formed UTF-8 comma-separated CSVs.\"\"\"\n",
- " data_dir = Path(data_dir)\n",
- "\n",
- " # Motif files: strip BOM and outer line-wrap quotes if present.\n",
- " for name in [\"train_data.csv\", \"test_data.csv\"]:\n",
- " path = data_dir / name\n",
- " if not path.exists():\n",
- " continue\n",
- " with open(path, \"rb\") as f:\n",
- " raw = f.read()\n",
- " text = raw.decode(\"utf-8-sig\")\n",
- " lines = text.replace(\"\\r\\n\", \"\\n\").strip().split(\"\\n\")\n",
- " wrapped = all(\n",
- " len(L) >= 2 and L.startswith('\"') and L.endswith('\"')\n",
- " for L in lines\n",
- " )\n",
- " if wrapped:\n",
- " lines = [L[1:-1] for L in lines]\n",
- " cleaned = \"\\n\".join(lines) + \"\\n\"\n",
- " if cleaned.encode(\"utf-8\") != raw:\n",
- " path.write_text(cleaned, encoding=\"utf-8\")\n",
- " print(f\" Sanitized {name}\")\n",
- "\n",
- " # Projection files: convert whitespace-separated to comma-separated if needed.\n",
- " for name in [\"projections_train.csv\", \"projections_test.csv\"]:\n",
- " path = data_dir / name\n",
- " if not path.exists():\n",
- " continue\n",
- " with open(path, \"rb\") as f:\n",
- " head = f.read(2048)\n",
- " text_head = head.decode(\"utf-8-sig\", errors=\"replace\")\n",
- " first_line = text_head.split(\"\\n\")[0]\n",
- " # If the first line has many whitespace-separated floats and no commas,\n",
- " # it's the whitespace-separated format.\n",
- " if \",\" not in first_line and len(first_line.split()) > 10:\n",
- " from io import StringIO\n",
- "\n",
- " with open(path, \"rb\") as f:\n",
- " text = f.read().decode(\"utf-8-sig\")\n",
- " df = pd.read_csv(\n",
- " StringIO(text), sep=r\"\\s+\", header=None, engine=\"python\"\n",
- " )\n",
- " df.to_csv(path, index=False, header=False)\n",
- " print(f\" Sanitized {name}\")\n",
- "\n",
- "\n",
- "sanitize_csv_files(DATA_DIR)"
- ]
- },
{
"cell_type": "code",
"execution_count": null,
From 74dda339ff2811e504763c110df50b6352d3a862 Mon Sep 17 00:00:00 2001
From: Soyoung Shin <63990199+0sophy1@users.noreply.github.com>
Date: Thu, 10 Sep 2026 12:38:03 -0700
Subject: [PATCH 5/7] Update projected-quantum-kernels.ipynb
---
.../tutorials/projected-quantum-kernels.ipynb | 27 ++++---------------
1 file changed, 5 insertions(+), 22 deletions(-)
diff --git a/docs/tutorials/projected-quantum-kernels.ipynb b/docs/tutorials/projected-quantum-kernels.ipynb
index 3eaac1b45429..4d3448f829b5 100644
--- a/docs/tutorials/projected-quantum-kernels.ipynb
+++ b/docs/tutorials/projected-quantum-kernels.ipynb
@@ -155,7 +155,7 @@
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": null,
"id": "f1ce0222-e7c7-451c-a11c-e61425a6bb8e",
"metadata": {},
"outputs": [],
@@ -167,12 +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",
@@ -1223,21 +1221,6 @@
"# pre-computed CSVs that were downloaded alongside the motif data.\n",
"# ---------------------------------------------------------------------------\n",
"\n",
- "\n",
- "def _load_projections(path):\n",
- " \"\"\"Load a projections CSV regardless of whether it has a header row.\"\"\"\n",
- " df = pd.read_csv(path)\n",
- " # If every column name is parseable as a float the first row is actually\n",
- " # data, not a header — reload without a header in that case.\n",
- " try:\n",
- " df.columns.astype(float)\n",
- " df = pd.read_csv(path, header=None)\n",
- " except (ValueError, TypeError):\n",
- " pass\n",
- " df.columns = [f\"q{i}\" for i in range(df.shape[1])]\n",
- " return df\n",
- "\n",
- "\n",
"if run_experiment:\n",
" projections_train = np.array(projections_train).reshape(\n",
" len(projections_train), -1\n",
@@ -1246,8 +1229,8 @@
" len(projections_test), -1\n",
" )\n",
"else:\n",
- " projections_train = _load_projections(DATA_DIR / \"projections_train.csv\")\n",
- " projections_test = _load_projections(DATA_DIR / \"projections_test.csv\")"
+ " projections_train = np.loadtxt(DATA_DIR / \"projections_train.csv\")\n",
+ " projections_test = np.loadtxt(DATA_DIR / \"projections_test.csv\")"
]
},
{
@@ -1569,7 +1552,7 @@
"metadata": {
"hours": 2.5,
"kernelspec": {
- "display_name": "Python 3",
+ "display_name": "qiskit",
"language": "python",
"name": "python3"
},
@@ -1583,7 +1566,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3"
+ "version": "3.11.11"
},
"qpuSeconds": 4800,
"widgets": {
From e6a86105c9f0c957c7b852d159d5053d84f60c85 Mon Sep 17 00:00:00 2001
From: Soyoung Shin <63990199+0sophy1@users.noreply.github.com>
Date: Thu, 10 Sep 2026 12:51:53 -0700
Subject: [PATCH 6/7] Update projected-quantum-kernels.ipynb
---
docs/tutorials/projected-quantum-kernels.ipynb | 4 ++--
1 file changed, 2 insertions(+), 2 deletions(-)
diff --git a/docs/tutorials/projected-quantum-kernels.ipynb b/docs/tutorials/projected-quantum-kernels.ipynb
index 4d3448f829b5..56e7359e269d 100644
--- a/docs/tutorials/projected-quantum-kernels.ipynb
+++ b/docs/tutorials/projected-quantum-kernels.ipynb
@@ -1552,7 +1552,7 @@
"metadata": {
"hours": 2.5,
"kernelspec": {
- "display_name": "qiskit",
+ "display_name": "Python 3",
"language": "python",
"name": "python3"
},
@@ -1566,7 +1566,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.11.11"
+ "version": "3"
},
"qpuSeconds": 4800,
"widgets": {
From 6f42aba41b65c99a7055954c2061958455c0d002 Mon Sep 17 00:00:00 2001
From: Soyoung Shin <63990199+0sophy1@users.noreply.github.com>
Date: Thu, 10 Sep 2026 17:10:45 -0700
Subject: [PATCH 7/7] updated parameter
---
.../tutorials/projected-quantum-kernels.ipynb | 232 +++++++++---------
...5956df4-5472-4394-a3e1-5514c456791d-0.avif | Bin 802434 -> 2261273 bytes
...f573436-ec5c-451b-976c-ad718b3c201d-1.avif | Bin 521664 -> 3310256 bytes
...59dbf23-fd3f-4e01-94b4-33e6d672172c-0.avif | Bin 121780 -> 1238598 bytes
4 files changed, 110 insertions(+), 122 deletions(-)
diff --git a/docs/tutorials/projected-quantum-kernels.ipynb b/docs/tutorials/projected-quantum-kernels.ipynb
index 56e7359e269d..07b3445c3498 100644
--- a/docs/tutorials/projected-quantum-kernels.ipynb
+++ b/docs/tutorials/projected-quantum-kernels.ipynb
@@ -155,7 +155,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 2,
"id": "f1ce0222-e7c7-451c-a11c-e61425a6bb8e",
"metadata": {},
"outputs": [],
@@ -317,7 +317,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 3,
"id": "84495ee1-880a-48cb-a904-d83396e8b29e",
"metadata": {},
"outputs": [
@@ -325,10 +325,10 @@
"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",
+ " train_data.csv ... OK (5,012 bytes)\n",
+ " test_data.csv ... OK (2,194 bytes)\n",
+ " projections_train.csv ... OK (779,730 bytes)\n",
+ " projections_test.csv ... OK (335,529 bytes)\n",
"\n",
"All files saved to data_tutorial/pqk/\n"
]
@@ -370,7 +370,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 4,
"id": "35013bc5-6b5e-44c8-8a8c-3af313b00a82",
"metadata": {},
"outputs": [
@@ -419,7 +419,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 5,
"id": "d40d9a0f-67d0-4704-8a94-cc0a466ffc92",
"metadata": {},
"outputs": [],
@@ -446,7 +446,7 @@
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": 6,
"id": "b98495f3-aeaa-4df1-a9fe-433e26aa7d4e",
"metadata": {},
"outputs": [
@@ -484,7 +484,7 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": 7,
"id": "45956df4-5472-4394-a3e1-5514c456791d",
"metadata": {},
"outputs": [
@@ -494,7 +494,7 @@
""
]
},
- "execution_count": 6,
+ "execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
@@ -526,7 +526,7 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": 8,
"id": "659dbf23-fd3f-4e01-94b4-33e6d672172c",
"metadata": {},
"outputs": [
@@ -536,7 +536,7 @@
""
]
},
- "execution_count": 7,
+ "execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
@@ -625,7 +625,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 9,
"id": "e1ab9cea-42ef-478c-bb9d-02ed4cf23ea6",
"metadata": {},
"outputs": [],
@@ -647,7 +647,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 10,
"id": "53b20cec-ef8a-4fdb-aeed-46546a32ea96",
"metadata": {},
"outputs": [],
@@ -737,7 +737,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 11,
"id": "7a56eda8-3fb2-43e9-8a82-ec64be05b699",
"metadata": {},
"outputs": [],
@@ -757,51 +757,36 @@
"name": "stdout",
"output_type": "stream",
"text": [
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- " 4.70201764e-02 3.53997968e-02 2.53130819e-02 3.23903401e-02\n",
- " 6.06327843e-03 1.16313667e-02 -1.12387504e-02 -3.18457725e-02\n",
- " -4.16445718e-04 -1.45609602e-03 -4.21737114e-01 2.83705669e-02\n",
- " 6.91332890e-03 -7.45363001e-02 -1.20139326e-02 -8.85566135e-02\n",
- " -3.22648394e-02 -3.24228074e-02 6.20431299e-04 3.04225434e-03\n",
- " 5.72795792e-03 1.11288428e-02 1.50395861e-01 9.18380197e-02\n",
- " 1.02553163e-01 2.98312847e-02 -3.30298912e-01 -1.13979648e-01\n",
- " 4.49159340e-03 8.63861493e-02 3.05666566e-02 2.21463145e-04\n",
- " 1.45946735e-02 8.54537275e-03 -8.09805979e-02 -2.92608104e-02\n",
- " -3.91243644e-02 -3.96632760e-02 -1.41187613e-01 -1.07363243e-01\n",
- " 1.81089440e-02 2.70778895e-02 1.45139414e-02 2.99480458e-02\n",
- " 4.99137134e-02 7.08789852e-02 4.30565759e-02 8.71287156e-02\n",
- " 1.04334798e-01 7.72191962e-02 7.10059720e-02 1.04650403e-01]\n",
- "[-7.31765102e-05 7.42669174e-03 9.82277344e-03 5.92638249e-02\n",
- " 4.24120486e-02 -9.06473416e-03 4.55057675e-03 8.43494094e-03\n",
- " 6.92097339e-02 -6.82234424e-02 6.13509008e-02 3.94200491e-02\n",
- " -1.24037979e-02 1.01976642e-01 7.90538600e-03 -7.19726160e-02\n",
- " -1.19501703e-16 -1.03796614e-02 7.37382463e-02 1.97238568e-01\n",
- " -3.59250635e-02 -2.67554009e-02 3.55010633e-02 7.68877990e-02\n",
- " 6.50677589e-05 -6.59298767e-03 -1.23719487e-02 -6.41938151e-02\n",
- " 1.95603072e-02 -2.48448551e-02 5.17784810e-02 -5.93767100e-02\n",
- " 3.11897681e-02 -3.91959720e-18 -4.47769148e-03 1.39202197e-01\n",
- " -6.56387523e-02 -5.85665483e-02 9.52905894e-03 -8.61460731e-02\n",
- " 3.91790656e-02 -1.27544375e-01 1.63712244e-01 3.36816934e-04\n",
- " 2.26230028e-02 -2.45023393e-05 4.95635588e-03 1.44779564e-01\n",
- " 3.71625177e-02 3.65675948e-03 2.83694017e-02 -7.10500602e-02\n",
- " -1.15467702e-01 6.21712129e-03 -4.80958959e-02 2.21021066e-02\n",
- " 7.99062499e-02 -1.87164076e-02 -3.67100369e-02 -2.38923731e-02]\n",
- "[ 6.85871605e-01 5.07725024e-01 8.71024642e-03 3.34823455e-02\n",
- " 4.58684961e-02 9.44384189e-17 -4.46829296e-02 -2.91296778e-02\n",
- " 4.15466461e-02 2.89628330e-02 1.88624017e-03 5.37110446e-02\n",
- " 2.59579053e-03 1.39327071e-02 -2.90781778e-02 5.07209866e-03\n",
- " 5.83403000e-02 2.60764440e-02 4.45999706e-17 -6.66701417e-03\n",
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- " -2.45530791e-02 6.26940708e-02 1.21058073e-02 2.76675948e-04\n",
- " 2.63980996e-02 2.58302364e-02 7.47856723e-02 8.42728943e-02\n",
- " 5.70989097e-02 6.92955086e-02 -5.68313712e-03 1.32199452e-01\n",
- " 8.90511238e-02 -3.45204621e-02 -1.05445836e-01 6.03864150e-03\n",
- " 2.16291384e-02 8.22303162e-03 1.00856715e-02 6.28973151e-02\n",
- " 6.26727169e-02 6.15399206e-02 9.67320897e-02 1.03045269e-16\n",
- " 1.79688783e-01 -1.59960520e-02 -1.15422952e-02 9.60200470e-03\n",
- " 6.58396672e-02 7.78329830e-03 6.53226955e-02 2.45778685e-03\n",
- " 4.36694753e-03 5.75098762e-03 -2.48896201e-02 8.33740755e-05]\n"
+ "[ 0.03530987 -0.06207794 -0.03529884 -0.1418671 0.00209782 0.0045834\n",
+ " 0.00407694 0.02528003 0.00233791 0.01800766 0.00718357 0.01927931\n",
+ " 0.0073651 -0.02009021 0.01144208 0.01333925 0.00521008 0.00535276\n",
+ " -0.04354042 -0.0383848 -0.04472125 0.00641964 -0.03954627 0.03207479\n",
+ " 0.01823132 0.02546267 -0. 0.16288225 0.03246113 0.\n",
+ " 0.06107868 0.01082782 0.00240078 0.13147612 0.14033432 0.14925945\n",
+ " 0.11577918 0.00016128 -0. 0.00604693 0.02433089 0.02033885\n",
+ " 0.01492506 0.00494294 0.00926954 0.00569533 0.09867722 0.05662552\n",
+ " -0.00001734 0. 0. 0.04625459 -0.02480763 0.01360688\n",
+ " 0.11511306 0.01260572 -0.01656313 -0.02510078 -0.03256272 0.00058607]\n",
+ "[-0.0756078 -0.05445208 -0.0228333 -0.00015029 0.00006226 0.02925132\n",
+ " -0.00325556 -0.00889965 0.0177611 -0.00437065 0.01682502 -0.00229805\n",
+ " -0.01041899 -0.03208967 -0.03515749 0.17477371 0.03783633 0.2126005\n",
+ " 0. 0. 0.00754466 -0.08242599 0. 0.03263675\n",
+ " 0.00399151 -0.01984418 -0.02106749 -0.02580491 0.03973411 -0.02037816\n",
+ " -0.01769352 -0.09720746 0.00098896 -0.11840454 0.14392615 0.13647983\n",
+ " 0.08683845 0.04492138 0.0046172 0.04171398 -0.0000869 -0.00270916\n",
+ " -0.0019876 -0.00440696 0.0307905 -0.0284622 0.11237189 0.15042867\n",
+ " 0.1020601 -0.03812461 0.00302523 -0.05240398 -0.01304566 -0.00403933\n",
+ " -0.01324601 -0.03658085 0.00934269 -0.00105112 -0. 0.01761827]\n",
+ "[ 0.57921657 0.2865493 0. 0.00028356 0.03177571 0.01152152\n",
+ " 0.00843001 0.02320127 0.00273558 0.00976802 0.00060077 0.00942531\n",
+ " 0.00096361 -0.03950026 0.00560635 0.00591487 0.00788236 0.01346192\n",
+ " 0.60752971 0.80203507 0.65649176 0.00069473 0.06010304 0.05922109\n",
+ " 0.01670672 0.02900743 0.0162253 0.0668811 0.01573204 -0.00288162\n",
+ " 0.04216451 0.00848301 0.00052577 -0.33798808 0.68075471 0.89471233\n",
+ " 0.72272544 0.08096828 0.02387351 0.01723619 0.00774532 0.05513527\n",
+ " 0.08285531 0.08102448 0.10677406 0.27778995 0.28883482 0.21497224\n",
+ " 0.17569826 0.00063149 0.0320076 0.06735008 -0.00053637 -0.0006907\n",
+ " 0.00991596 0.00414575 -0.08425133 -0.09569482 0.00219474 0.00241873]\n"
]
}
],
@@ -830,9 +815,9 @@
"output_type": "stream",
"text": [
"qubits: 60\n",
- "2q-depth: 64\n",
- "2q-size: 1888\n",
- "Operator counts: OrderedDict({'rz': 6016, 'sx': 4576, 'cz': 1888, 'x': 896, 'barrier': 31})\n"
+ "2q-depth: 96\n",
+ "2q-size: 2832\n",
+ "Operator counts: OrderedDict([('rz', 8640), ('sx', 7104), ('cz', 2832), ('x', 720), ('barrier', 47)])\n"
]
},
{
@@ -876,7 +861,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 14,
"id": "81835ec2-210f-4176-ba79-c8046cc57d92",
"metadata": {},
"outputs": [],
@@ -887,18 +872,10 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 15,
"id": "932201c0-178b-4a98-b2dc-5b4c81953d49",
"metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "Training data progress: 100%|██████████| 172/172 [13:03<00:00, 4.55s/it]\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"# Identity operator on all qubits\n",
"id = \"I\" * num_qubits\n",
@@ -999,7 +976,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 16,
"id": "ccbd7603-1dd3-4aab-8ee8-8b0a98068b61",
"metadata": {},
"outputs": [],
@@ -1030,18 +1007,10 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 17,
"id": "f9e77a9c-d295-4893-aebe-74cc59168e1f",
"metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "Test data progress: 100%|██████████| 74/74 [00:13<00:00, 5.56it/s]\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"# Identity operator on all qubits\n",
"id = \"I\" * num_qubits\n",
@@ -1139,7 +1108,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 18,
"id": "721ed991-fee2-4f66-9bb6-a750ee935033",
"metadata": {},
"outputs": [],
@@ -1211,7 +1180,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 19,
"id": "a61e6c67-056b-4659-87a0-284bda432cfd",
"metadata": {},
"outputs": [],
@@ -1251,7 +1220,7 @@
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": null,
"id": "d2eef986-22a5-4528-8fa0-c7dbfd586071",
"metadata": {},
"outputs": [
@@ -1259,40 +1228,59 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "Fitting 10 folds for each of 6622 candidates, totalling 66220 fits\n",
+ "Fitting 10 folds for each of 154 candidates, totalling 1540 fits\n",
"The best parameters are {'C': 8.5, 'gamma': 0.01} with a score of 0.6980\n",
"Test accuracy with best model: 0.8108\n"
]
}
],
"source": [
- "# Range of 'C' and 'gamma' values as SVC hyperparameters\n",
- "C_range = [0.001, 0.005, 0.007]\n",
- "C_range.extend([x * 0.01 for x in range(1, 11)])\n",
- "C_range.extend([x * 0.25 for x in range(1, 60)])\n",
- "C_range.extend(\n",
- " [\n",
- " 20,\n",
- " 50,\n",
- " 100,\n",
- " 200,\n",
- " 500,\n",
- " 700,\n",
- " 1000,\n",
- " 1100,\n",
- " 1200,\n",
- " 1300,\n",
- " 1400,\n",
- " 1500,\n",
- " 1700,\n",
- " 2000,\n",
- " ]\n",
- ")\n",
- "\n",
- "gamma_range = [\"auto\", \"scale\", 0.001, 0.005, 0.007]\n",
- "gamma_range.extend([x * 0.01 for x in range(1, 11)])\n",
- "gamma_range.extend([x * 0.25 for x in range(1, 60)])\n",
- "gamma_range.extend([20, 50, 100])\n",
+ "# Range of 'C' and 'gamma' values as SVC hyperparameters.\n",
+ "#\n",
+ "# This is a reduced grid so the tutorial runs quickly (154 candidates).\n",
+ "# The optimal (C, gamma) reported below lie within it, so the results\n",
+ "# are unchanged. The full grid used originally had 6622 candidates and\n",
+ "# took roughly one hours to search:\n",
+ "#\n",
+ "# C_range = [0.001, 0.005, 0.007]\n",
+ "# C_range.extend([x * 0.01 for x in range(1, 11)]) # 0.01 .. 0.10\n",
+ "# C_range.extend([x * 0.25 for x in range(1, 60)]) # 0.25 .. 14.75\n",
+ "# C_range.extend([20, 50, 100, 200, 500, 700, 1000,\n",
+ "# 1100, 1200, 1300, 1400, 1500, 1700, 2000])\n",
+ "# gamma_range = [\"auto\", \"scale\", 0.001, 0.005, 0.007]\n",
+ "# gamma_range.extend([x * 0.01 for x in range(1, 11)])\n",
+ "# gamma_range.extend([x * 0.25 for x in range(1, 60)])\n",
+ "# gamma_range.extend([20, 50, 100])\n",
+ "\n",
+ "C_range = [\n",
+ " 0.001,\n",
+ " 0.01,\n",
+ " 0.1,\n",
+ " 0.5,\n",
+ " 1.0,\n",
+ " 2.0,\n",
+ " 4.0,\n",
+ " 6.0,\n",
+ " 8.5,\n",
+ " 10.75,\n",
+ " 14.0,\n",
+ " 20,\n",
+ " 50,\n",
+ " 100,\n",
+ "]\n",
+ "gamma_range = [\n",
+ " 0.001,\n",
+ " 0.005,\n",
+ " 0.007,\n",
+ " 0.01,\n",
+ " 0.02,\n",
+ " 0.03,\n",
+ " 0.04,\n",
+ " 0.05,\n",
+ " 0.1,\n",
+ " 0.5,\n",
+ " 1.0,\n",
+ "]\n",
"\n",
"param_grid = dict(C=C_range, gamma=gamma_range)\n",
"\n",
@@ -1330,7 +1318,7 @@
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": 21,
"id": "41867b5a-9091-4aa4-adab-a05cf6238966",
"metadata": {},
"outputs": [
@@ -1338,7 +1326,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "Fitting 10 folds for each of 6622 candidates, totalling 66220 fits\n",
+ "Fitting 10 folds for each of 154 candidates, totalling 1540 fits\n",
"The best parameters are {'C': 10.75, 'gamma': 0.04} with a score of 0.7830\n",
"Test accuracy with best model: 0.7432\n"
]
@@ -1386,7 +1374,7 @@
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": 22,
"id": "bc67f5c0-5d79-4633-807e-02b8cc9d39f5",
"metadata": {},
"outputs": [
@@ -1441,7 +1429,7 @@
},
{
"cell_type": "code",
- "execution_count": 13,
+ "execution_count": 23,
"id": "877cf344-7324-4154-b0d9-7bcfa03b6ac0",
"metadata": {},
"outputs": [
@@ -1481,7 +1469,7 @@
},
{
"cell_type": "code",
- "execution_count": 14,
+ "execution_count": 24,
"id": "90e9de08-cafc-4493-9358-581c148f3447",
"metadata": {},
"outputs": [
diff --git a/public/docs/images/tutorials/projected-quantum-kernels/extracted-outputs/45956df4-5472-4394-a3e1-5514c456791d-0.avif b/public/docs/images/tutorials/projected-quantum-kernels/extracted-outputs/45956df4-5472-4394-a3e1-5514c456791d-0.avif
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