From 9d7da6c6a1b601f5e532802858786a26e92dfea1 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?J=C3=A9r=C3=A9mie=20Gince?=
 <50332514+JeremieGince@users.noreply.github.com>
Date: Fri, 28 Nov 2025 11:34:49 -0500
Subject: [PATCH 1/3] Add CPU accelerator option and update dependencies

Added 'accelerator="cpu"' to pipeline initialization in all relevant notebooks for consistent device selection. Updated pyproject.toml to require matchcake>=0.1.2 and added jupyter and notebook to dev dependencies. Also replaced SptmfRxRx and SptmFHH with CompRxRx and CompHH in the deep learning notebook, and updated execution metadata and outputs.
---
 notebooks/automl_pipeline_tutorial.ipynb    |   1 +
 notebooks/ligthning_pipeline_tutorial.ipynb |   1 +
 notebooks/nif_deep_learning.ipynb           | 329 +++++++-
 pyproject.toml                              |   4 +-
 uv.lock                                     | 851 ++++++++++++++------
 5 files changed, 911 insertions(+), 275 deletions(-)

diff --git a/notebooks/automl_pipeline_tutorial.ipynb b/notebooks/automl_pipeline_tutorial.ipynb
index c1d8594..2802bab 100644
--- a/notebooks/automl_pipeline_tutorial.ipynb
+++ b/notebooks/automl_pipeline_tutorial.ipynb
@@ -297,6 +297,7 @@
     "    inner_max_epochs=10,  # increase at least to 128\n",
     "    inner_max_time=\"00:00:01:00\",  # increase at least to \"00:00:10:00\"\n",
     "    automl_overwrite_fit=True,\n",
+    "    accelerator=\"cpu\",\n",
     "    **pipeline_args\n",
     ")"
    ],
diff --git a/notebooks/ligthning_pipeline_tutorial.ipynb b/notebooks/ligthning_pipeline_tutorial.ipynb
index 9499d1b..dacad60 100644
--- a/notebooks/ligthning_pipeline_tutorial.ipynb
+++ b/notebooks/ligthning_pipeline_tutorial.ipynb
@@ -277,6 +277,7 @@
     "    max_epochs=10,\n",
     "    max_time=\"00:00:01:00\",  # DD:HH:MM:SS\n",
     "    overwrite_fit=True,\n",
+    "    accelerator=\"cpu\",\n",
     "    verbose=True,\n",
     "    **model_args,\n",
     ")"
diff --git a/notebooks/nif_deep_learning.ipynb b/notebooks/nif_deep_learning.ipynb
index 6214b22..959da37 100644
--- a/notebooks/nif_deep_learning.ipynb
+++ b/notebooks/nif_deep_learning.ipynb
@@ -10,7 +10,12 @@
    "id": "c11b257a04e4cb48"
   },
   {
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-11-28T16:32:17.598802Z",
+     "start_time": "2025-11-28T16:32:17.581649Z"
+    }
+   },
    "cell_type": "code",
    "source": [
     "import os\n",
@@ -27,7 +32,7 @@
     "import torch\n",
     "from ax import RangeParameterConfig\n",
     "from matchcake import NonInteractingFermionicDevice\n",
-    "from matchcake.operations import SptmAngleEmbedding, SptmfRxRx, SptmFHH\n",
+    "from matchcake.operations import SptmAngleEmbedding, CompRxRx, CompHH\n",
     "\n",
     "from matchcake_opt.datamodules.datamodule import DataModule\n",
     "from matchcake_opt.modules.classification_model import ClassificationModel\n",
@@ -36,7 +41,7 @@
    ],
    "id": "3b95af772cefb5b3",
    "outputs": [],
-   "execution_count": null
+   "execution_count": 9
   },
   {
    "metadata": {},
@@ -45,7 +50,12 @@
    "id": "e1f9ef65d39494b7"
   },
   {
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-11-28T16:32:17.629179Z",
+     "start_time": "2025-11-28T16:32:17.605834Z"
+    }
+   },
    "cell_type": "code",
    "source": [
     "class NIFDL(ClassificationModel):\n",
@@ -114,8 +124,8 @@
     "        SptmAngleEmbedding(inputs, wires=range(self.n_qubits))\n",
     "        for i in range(self.n_layers):\n",
     "          for j in range(self.n_qubits - 1):\n",
-    "            SptmfRxRx(weights[i, j*2 : j*2+2], wires=[j, j+1])\n",
-    "            SptmFHH(wires=[j, j+1])\n",
+    "            CompRxRx(weights[i, j*2 : j*2+2], wires=[j, j+1])\n",
+    "            CompHH(wires=[j, j+1])\n",
     "        return [qml.expval(qml.PauliZ(wires=i)) for i in range(self.n_qubits)]\n",
     "\n",
     "    def forward(self, x) -> Any:\n",
@@ -136,10 +146,15 @@
    ],
    "id": "586f30eed53a5ae",
    "outputs": [],
-   "execution_count": null
+   "execution_count": 10
   },
   {
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-11-28T16:32:17.644796Z",
+     "start_time": "2025-11-28T16:32:17.634277Z"
+    }
+   },
    "cell_type": "code",
    "source": [
     "# Dataset\n",
@@ -162,7 +177,7 @@
    ],
    "id": "412328c44c55e453",
    "outputs": [],
-   "execution_count": null
+   "execution_count": 11
   },
   {
    "metadata": {},
@@ -171,7 +186,12 @@
    "id": "3847ed18ac6e57fd"
   },
   {
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-11-28T16:32:17.691303Z",
+     "start_time": "2025-11-28T16:32:17.650083Z"
+    }
+   },
    "cell_type": "code",
    "source": [
     "datamodule = DataModule.from_dataset_name(\n",
@@ -184,7 +204,7 @@
    ],
    "id": "42cb94221bd39753",
    "outputs": [],
-   "execution_count": null
+   "execution_count": 12
   },
   {
    "metadata": {},
@@ -196,10 +216,13 @@
    "id": "d13cf82e118d84eb"
   },
   {
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-11-28T16:32:19.519727Z",
+     "start_time": "2025-11-28T16:32:17.696831Z"
+    }
+   },
    "cell_type": "code",
-   "outputs": [],
-   "execution_count": null,
    "source": [
     "model_args = dict(\n",
     "    n_qubits=16,\n",
@@ -214,16 +237,32 @@
     "    max_time=\"00:00:01:00\",  # DD:HH:MM:SS\n",
     "    overwrite_fit=True,\n",
     "    verbose=True,\n",
+    "    accelerator=\"cpu\",\n",
     "    **model_args,\n",
     ")"
    ],
-   "id": "8832b1d06113ac48"
+   "id": "8832b1d06113ac48",
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "GPU available: True (cuda), used: False\n",
+      "TPU available: False, using: 0 TPU cores\n",
+      "HPU available: False, using: 0 HPUs\n"
+     ]
+    }
+   ],
+   "execution_count": 13
   },
   {
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-11-28T16:34:11.900615Z",
+     "start_time": "2025-11-28T16:32:19.585075Z"
+    }
+   },
    "cell_type": "code",
-   "outputs": [],
-   "execution_count": null,
    "source": [
     "start_time = time.perf_counter()\n",
     "metrics = lightning_pipeline.run()\n",
@@ -234,7 +273,221 @@
     "test_metrics = lightning_pipeline.run_test()\n",
     "print(\"⚡\" * 20, \"\\nTest Metrics:\\n\", test_metrics, \"\\n\", \"⚡\" * 20)"
    ],
-   "id": "7d8a499f05426a0d"
+   "id": "7d8a499f05426a0d",
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "  | Name          | Type             | Params | Mode \n",
+      "-----------------------------------------------------------\n",
+      "0 | val_loss      | NLLLoss          | 0      | train\n",
+      "1 | train_loss    | NLLLoss          | 0      | train\n",
+      "2 | _metrics      | MetricCollection | 0      | train\n",
+      "3 | train_metrics | MetricCollection | 0      | train\n",
+      "4 | val_metrics   | MetricCollection | 0      | train\n",
+      "5 | test_metrics  | MetricCollection | 0      | train\n",
+      "6 | flatten       | Flatten          | 0      | train\n",
+      "7 | encoders      | ModuleList       | 1.4 K  | train\n",
+      "8 | readout       | Linear           | 650    | train\n",
+      "-----------------------------------------------------------\n",
+      "2.1 K     Trainable params\n",
+      "0         Non-trainable params\n",
+      "2.1 K     Total params\n",
+      "0.008     Total estimated model params size (MB)\n",
+      "37        Modules in train mode\n",
+      "0         Modules in eval mode\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Checkpoint folder: E:\\Github\\MatchCake-Opt\\notebooks\\data\\automl\\Digits2D\\NIFDL\\checkpoints\n",
+      "Model: NIFDL\n",
+      "NIFDL(\n",
+      "  (val_loss): NLLLoss()\n",
+      "  (train_loss): NLLLoss()\n",
+      "  (_metrics): MetricCollection(\n",
+      "    (MulticlassAccuracy): MulticlassAccuracy()\n",
+      "    (MulticlassF1Score): MulticlassF1Score()\n",
+      "    (MulticlassRecall): MulticlassRecall()\n",
+      "    (MulticlassPrecision): MulticlassPrecision()\n",
+      "    (MulticlassAUROC): MulticlassAUROC(),\n",
+      "    prefix=train_\n",
+      "  )\n",
+      "  (train_metrics): MetricCollection(\n",
+      "    (MulticlassAccuracy): MulticlassAccuracy()\n",
+      "    (MulticlassF1Score): MulticlassF1Score()\n",
+      "    (MulticlassRecall): MulticlassRecall()\n",
+      "    (MulticlassPrecision): MulticlassPrecision()\n",
+      "    (MulticlassAUROC): MulticlassAUROC(),\n",
+      "    prefix=train_\n",
+      "  )\n",
+      "  (val_metrics): MetricCollection(\n",
+      "    (MulticlassAccuracy): MulticlassAccuracy()\n",
+      "    (MulticlassF1Score): MulticlassF1Score()\n",
+      "    (MulticlassRecall): MulticlassRecall()\n",
+      "    (MulticlassPrecision): MulticlassPrecision()\n",
+      "    (MulticlassAUROC): MulticlassAUROC(),\n",
+      "    prefix=val_\n",
+      "  )\n",
+      "  (test_metrics): MetricCollection(\n",
+      "    (MulticlassAccuracy): MulticlassAccuracy()\n",
+      "    (MulticlassF1Score): MulticlassF1Score()\n",
+      "    (MulticlassRecall): MulticlassRecall()\n",
+      "    (MulticlassPrecision): MulticlassPrecision()\n",
+      "    (MulticlassAUROC): MulticlassAUROC(),\n",
+      "    prefix=test_\n",
+      "  )\n",
+      "  (flatten): Flatten(start_dim=1, end_dim=-1)\n",
+      "  (encoders): ModuleList(\n",
+      "    (0-7): 8 x <Quantum Torch Layer: func=circuit>\n",
+      "  )\n",
+      "  (readout): Linear(in_features=64, out_features=10, bias=True)\n",
+      ")\n",
+      "Max epochs: 10\n",
+      "Max time: 00:00:01:00\n",
+      "Monitor: val_loss (min)\n",
+      "Overwrite fit: True\n",
+      "\n",
+      "Epoch Progress:   0%|          | 0/10 [00:00<?, ?it/s]\u001B[A"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Time limit reached. Elapsed time is 0:01:00. Signaling Trainer to stop.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Epoch Progress:   0%|          | 0/10 [01:21<?, ?it/s, v_num=0, train_loss=2.32, val_loss=2.3, val_MulticlassAccuracy=0.0988, val_MulticlassF1Score=0.0988, val_MulticlassRecall=0.0988, val_MulticlassPrecision=0.0988, val_MulticlassAUROC=0.586]\u001B[A\n",
+      "Epoch Progress:  10%|█         | 1/10 [01:21<12:10, 81.16s/it, v_num=0, train_loss=2.32, val_loss=2.3, val_MulticlassAccuracy=0.0988, val_MulticlassF1Score=0.0988, val_MulticlassRecall=0.0988, val_MulticlassPrecision=0.0988, val_MulticlassAUROC=0.586]\u001B[A\n",
+      "Epoch Progress:  10%|█         | 1/10 [01:21<12:11, 81.23s/it, v_num=0, train_loss=2.32, val_loss=2.3, val_MulticlassAccuracy=0.0988, val_MulticlassF1Score=0.0988, val_MulticlassRecall=0.0988, val_MulticlassPrecision=0.0988, val_MulticlassAUROC=0.586]\u001B[A"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Restoring states from the checkpoint path at E:\\Github\\MatchCake-Opt\\notebooks\\data\\automl\\Digits2D\\NIFDL\\checkpoints\\epoch00-step0019.ckpt\n",
+      "Loaded model weights from the checkpoint at E:\\Github\\MatchCake-Opt\\notebooks\\data\\automl\\Digits2D\\NIFDL\\checkpoints\\epoch00-step0019.ckpt\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
+       "┃\u001B[1m \u001B[0m\u001B[1m     Validate metric     \u001B[0m\u001B[1m \u001B[0m┃\u001B[1m \u001B[0m\u001B[1m      DataLoader 0       \u001B[0m\u001B[1m \u001B[0m┃\n",
+       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
+       "│\u001B[36m \u001B[0m\u001B[36m   val_MulticlassAUROC   \u001B[0m\u001B[36m \u001B[0m│\u001B[35m \u001B[0m\u001B[35m   0.5861977338790894    \u001B[0m\u001B[35m \u001B[0m│\n",
+       "│\u001B[36m \u001B[0m\u001B[36m val_MulticlassAccuracy  \u001B[0m\u001B[36m \u001B[0m│\u001B[35m \u001B[0m\u001B[35m   0.09876543283462524   \u001B[0m\u001B[35m \u001B[0m│\n",
+       "│\u001B[36m \u001B[0m\u001B[36m  val_MulticlassF1Score  \u001B[0m\u001B[36m \u001B[0m│\u001B[35m \u001B[0m\u001B[35m   0.09876543283462524   \u001B[0m\u001B[35m \u001B[0m│\n",
+       "│\u001B[36m \u001B[0m\u001B[36m val_MulticlassPrecision \u001B[0m\u001B[36m \u001B[0m│\u001B[35m \u001B[0m\u001B[35m   0.09876543283462524   \u001B[0m\u001B[35m \u001B[0m│\n",
+       "│\u001B[36m \u001B[0m\u001B[36m  val_MulticlassRecall   \u001B[0m\u001B[36m \u001B[0m│\u001B[35m \u001B[0m\u001B[35m   0.09876543283462524   \u001B[0m\u001B[35m \u001B[0m│\n",
+       "│\u001B[36m \u001B[0m\u001B[36m        val_loss         \u001B[0m\u001B[36m \u001B[0m│\u001B[35m \u001B[0m\u001B[35m   2.3029329776763916    \u001B[0m\u001B[35m \u001B[0m│\n",
+       "└───────────────────────────┴───────────────────────────┘\n"
+      ],
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
+       "┃<span style=\"font-weight: bold\">      Validate metric      </span>┃<span style=\"font-weight: bold\">       DataLoader 0        </span>┃\n",
+       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
+       "│<span style=\"color: #008080; text-decoration-color: #008080\">    val_MulticlassAUROC    </span>│<span style=\"color: #800080; text-decoration-color: #800080\">    0.5861977338790894     </span>│\n",
+       "│<span style=\"color: #008080; text-decoration-color: #008080\">  val_MulticlassAccuracy   </span>│<span style=\"color: #800080; text-decoration-color: #800080\">    0.09876543283462524    </span>│\n",
+       "│<span style=\"color: #008080; text-decoration-color: #008080\">   val_MulticlassF1Score   </span>│<span style=\"color: #800080; text-decoration-color: #800080\">    0.09876543283462524    </span>│\n",
+       "│<span style=\"color: #008080; text-decoration-color: #008080\">  val_MulticlassPrecision  </span>│<span style=\"color: #800080; text-decoration-color: #800080\">    0.09876543283462524    </span>│\n",
+       "│<span style=\"color: #008080; text-decoration-color: #008080\">   val_MulticlassRecall    </span>│<span style=\"color: #800080; text-decoration-color: #800080\">    0.09876543283462524    </span>│\n",
+       "│<span style=\"color: #008080; text-decoration-color: #008080\">         val_loss          </span>│<span style=\"color: #800080; text-decoration-color: #800080\">    2.3029329776763916     </span>│\n",
+       "└───────────────────────────┴───────────────────────────┘\n",
+       "</pre>\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "jetTransient": {
+      "display_id": null
+     }
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Restoring states from the checkpoint path at E:\\Github\\MatchCake-Opt\\notebooks\\data\\automl\\Digits2D\\NIFDL\\checkpoints\\epoch00-step0019.ckpt\n",
+      "Loaded model weights from the checkpoint at E:\\Github\\MatchCake-Opt\\notebooks\\data\\automl\\Digits2D\\NIFDL\\checkpoints\\epoch00-step0019.ckpt\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Metrics saved to E:\\Github\\MatchCake-Opt\\notebooks\\data\\automl\\Digits2D\\NIFDL\\checkpoints\\validation_metrics.json\n",
+      "Time taken: 0:01:42.762405\n",
+      "⚡⚡⚡⚡⚡⚡⚡⚡⚡⚡⚡⚡⚡⚡⚡⚡⚡⚡⚡⚡ \n",
+      "Validation Metrics:\n",
+      " {'val_loss': 2.3029329776763916, 'val_MulticlassAccuracy': 0.09876543283462524, 'val_MulticlassF1Score': 0.09876543283462524, 'val_MulticlassRecall': 0.09876543283462524, 'val_MulticlassPrecision': 0.09876543283462524, 'val_MulticlassAUROC': 0.5861977338790894, 'validation_time': 17.814280099992175, 'training_time': 84.94243890000507} \n",
+      " ⚡⚡⚡⚡⚡⚡⚡⚡⚡⚡⚡⚡⚡⚡⚡⚡⚡⚡⚡⚡\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
+       "┃\u001B[1m \u001B[0m\u001B[1m       Test metric       \u001B[0m\u001B[1m \u001B[0m┃\u001B[1m \u001B[0m\u001B[1m      DataLoader 0       \u001B[0m\u001B[1m \u001B[0m┃\n",
+       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
+       "│\u001B[36m \u001B[0m\u001B[36m  test_MulticlassAUROC   \u001B[0m\u001B[36m \u001B[0m│\u001B[35m \u001B[0m\u001B[35m    0.579858660697937    \u001B[0m\u001B[35m \u001B[0m│\n",
+       "│\u001B[36m \u001B[0m\u001B[36m test_MulticlassAccuracy \u001B[0m\u001B[36m \u001B[0m│\u001B[35m \u001B[0m\u001B[35m   0.10555555671453476   \u001B[0m\u001B[35m \u001B[0m│\n",
+       "│\u001B[36m \u001B[0m\u001B[36m test_MulticlassF1Score  \u001B[0m\u001B[36m \u001B[0m│\u001B[35m \u001B[0m\u001B[35m   0.10555555671453476   \u001B[0m\u001B[35m \u001B[0m│\n",
+       "│\u001B[36m \u001B[0m\u001B[36mtest_MulticlassPrecision \u001B[0m\u001B[36m \u001B[0m│\u001B[35m \u001B[0m\u001B[35m   0.10555555671453476   \u001B[0m\u001B[35m \u001B[0m│\n",
+       "│\u001B[36m \u001B[0m\u001B[36m  test_MulticlassRecall  \u001B[0m\u001B[36m \u001B[0m│\u001B[35m \u001B[0m\u001B[35m   0.10555555671453476   \u001B[0m\u001B[35m \u001B[0m│\n",
+       "│\u001B[36m \u001B[0m\u001B[36m        test_loss        \u001B[0m\u001B[36m \u001B[0m│\u001B[35m \u001B[0m\u001B[35m    2.303359031677246    \u001B[0m\u001B[35m \u001B[0m│\n",
+       "└───────────────────────────┴───────────────────────────┘\n"
+      ],
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
+       "┃<span style=\"font-weight: bold\">        Test metric        </span>┃<span style=\"font-weight: bold\">       DataLoader 0        </span>┃\n",
+       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
+       "│<span style=\"color: #008080; text-decoration-color: #008080\">   test_MulticlassAUROC    </span>│<span style=\"color: #800080; text-decoration-color: #800080\">     0.579858660697937     </span>│\n",
+       "│<span style=\"color: #008080; text-decoration-color: #008080\">  test_MulticlassAccuracy  </span>│<span style=\"color: #800080; text-decoration-color: #800080\">    0.10555555671453476    </span>│\n",
+       "│<span style=\"color: #008080; text-decoration-color: #008080\">  test_MulticlassF1Score   </span>│<span style=\"color: #800080; text-decoration-color: #800080\">    0.10555555671453476    </span>│\n",
+       "│<span style=\"color: #008080; text-decoration-color: #008080\"> test_MulticlassPrecision  </span>│<span style=\"color: #800080; text-decoration-color: #800080\">    0.10555555671453476    </span>│\n",
+       "│<span style=\"color: #008080; text-decoration-color: #008080\">   test_MulticlassRecall   </span>│<span style=\"color: #800080; text-decoration-color: #800080\">    0.10555555671453476    </span>│\n",
+       "│<span style=\"color: #008080; text-decoration-color: #008080\">         test_loss         </span>│<span style=\"color: #800080; text-decoration-color: #800080\">     2.303359031677246     </span>│\n",
+       "└───────────────────────────┴───────────────────────────┘\n",
+       "</pre>\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "jetTransient": {
+      "display_id": null
+     }
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Metrics saved to E:\\Github\\MatchCake-Opt\\notebooks\\data\\automl\\Digits2D\\NIFDL\\checkpoints\\test_metrics.json\n",
+      "⚡⚡⚡⚡⚡⚡⚡⚡⚡⚡⚡⚡⚡⚡⚡⚡⚡⚡⚡⚡ \n",
+      "Test Metrics:\n",
+      " {'test_loss': 2.303359031677246, 'test_MulticlassAccuracy': 0.10555555671453476, 'test_MulticlassF1Score': 0.10555555671453476, 'test_MulticlassRecall': 0.10555555671453476, 'test_MulticlassPrecision': 0.10555555671453476, 'test_MulticlassAUROC': 0.579858660697937, 'test_time': 9.541893699992215} \n",
+      " ⚡⚡⚡⚡⚡⚡⚡⚡⚡⚡⚡⚡⚡⚡⚡⚡⚡⚡⚡⚡\n"
+     ]
+    }
+   ],
+   "execution_count": 14
   },
   {
    "metadata": {},
@@ -243,10 +496,13 @@
    "id": "688b66fca9889c36"
   },
   {
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-11-28T16:34:11.978217Z",
+     "start_time": "2025-11-28T16:34:11.967195Z"
+    }
+   },
    "cell_type": "code",
-   "outputs": [],
-   "execution_count": null,
    "source": [
     "automl_pipeline = AutoMLPipeline(\n",
     "    model_cls=model_cls,\n",
@@ -256,13 +512,23 @@
     "    inner_max_epochs=10,  # increase at least to 128\n",
     "    inner_max_time=\"00:00:01:00\",  # increase at least to \"00:00:10:00\"\n",
     "    automl_overwrite_fit=True,\n",
+    "    accelerator=\"cpu\",\n",
     "    **pipeline_args\n",
     ")"
    ],
-   "id": "16001dd8071bc8b0"
+   "id": "16001dd8071bc8b0",
+   "outputs": [],
+   "execution_count": 15
   },
   {
-   "metadata": {},
+   "metadata": {
+    "jupyter": {
+     "is_executing": true
+    },
+    "ExecuteTime": {
+     "start_time": "2025-11-28T16:34:11.982237Z"
+    }
+   },
    "cell_type": "code",
    "source": [
     "start_time = time.perf_counter()\n",
@@ -271,7 +537,20 @@
     "print(f\"Time taken: {end_time - start_time:.4f} seconds\")"
    ],
    "id": "8c84ee5352220970",
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "AutoML iterations:   0%|          | 0/5 [00:00<?, ?it/s]\u001B[A[INFO 11-28 11:34:11] ax.api.client: GenerationStrategy(name='Center+Sobol+MBM:fast', nodes=[CenterGenerationNode(next_node_name='Sobol'), GenerationNode(node_name='Sobol', generator_specs=[GeneratorSpec(generator_enum=Sobol, model_key_override=None)], transition_criteria=[MinTrials(transition_to='MBM'), MinTrials(transition_to='MBM')]), GenerationNode(node_name='MBM', generator_specs=[GeneratorSpec(generator_enum=BoTorch, model_key_override=None)], transition_criteria=[])]) chosen based on user input and problem structure.\n",
+      "[INFO 11-28 11:34:11] ax.api.client: Generated new trial 0 with parameters {'learning_rate': 0.050005, 'n_qubits': 18, 'n_layers': 5} using GenerationNode CenterOfSearchSpace.\n",
+      "GPU available: True (cuda), used: False\n",
+      "TPU available: False, using: 0 TPU cores\n",
+      "HPU available: False, using: 0 HPUs\n"
+     ]
+    }
+   ],
    "execution_count": null
   },
   {
diff --git a/pyproject.toml b/pyproject.toml
index 75a97a0..e3b14a2 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -29,7 +29,7 @@ dependencies = [
     "tensorboard (>=2.19.0,<3.0.0)",
     "ax-platform[mysql] (>=1.0.0,<2.0.0)",
     "torcheval (>=0.0.7,<0.0.8)",
-    "matchcake (>=0.0.4)",
+    "matchcake>=0.1.2",
     "autoray (<=0.7.2)",
     "medmnist (>=3.0.2,<4.0.0)",
     "torch-geometric>=2.7.0",
@@ -52,6 +52,8 @@ dev = [
     "isort>=6.0.1,<7",
     "types-networkx>=3.5.0.20251001",
     "pip>=25.3",
+    "jupyter>=1.1.1",
+    "notebook>=7.5.0",
 ]
 docs = [
     "sphinx>=6.2.1,<6.3.0",
diff --git a/uv.lock b/uv.lock
index 63dc3ce..8173ec6 100644
--- a/uv.lock
+++ b/uv.lock
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+    { name = "idna" },
+    { name = "sniffio" },
+    { name = "typing-extensions" },
+]
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 name = "appdirs"
 version = "1.4.4"
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 ]
 
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+version = "25.1.0"
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+version = "1.4.0"
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+dependencies = [
+    { name = "python-dateutil" },
+    { name = "tzdata" },
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+
 [[package]]
 name = "astroid"
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+    { name = "pywinpty", marker = "(os_name == 'nt' and platform_machine != 'aarch64' and sys_platform == 'linux') or (os_name == 'nt' and platform_python_implementation != 'CPython' and sys_platform == 'linux') or (os_name == 'nt' and sys_platform != 'darwin' and sys_platform != 'linux') or (os_name == 'nt' and sys_platform == 'darwin' and extra != 'extra-13-matchcake-opt-cpu') or (os_name == 'nt' and sys_platform == 'linux' and extra != 'extra-13-matchcake-opt-cpu' and extra != 'extra-13-matchcake-opt-cu128' and extra != 'extra-13-matchcake-opt-cu130') or (os_name != 'nt' and extra == 'extra-13-matchcake-opt-cpu' and extra == 'extra-13-matchcake-opt-cu128') or (os_name != 'nt' and extra == 'extra-13-matchcake-opt-cpu' and extra == 'extra-13-matchcake-opt-cu130') or (os_name != 'nt' and extra == 'extra-13-matchcake-opt-cu128' and extra == 'extra-13-matchcake-opt-cu130') or (platform_machine == 'aarch64' and platform_python_implementation == 'CPython' and sys_platform == 'linux' and extra == 'extra-13-matchcake-opt-cpu' and extra == 'extra-13-matchcake-opt-cu128') or (platform_machine == 'aarch64' and platform_python_implementation == 'CPython' and sys_platform == 'linux' and extra == 'extra-13-matchcake-opt-cpu' and extra == 'extra-13-matchcake-opt-cu130') or (platform_machine == 'aarch64' and platform_python_implementation == 'CPython' and sys_platform == 'linux' and extra == 'extra-13-matchcake-opt-cu128' and extra == 'extra-13-matchcake-opt-cu130') or (sys_platform == 'darwin' and extra == 'extra-13-matchcake-opt-cpu' and extra == 'extra-13-matchcake-opt-cu128') or (sys_platform == 'darwin' and extra == 'extra-13-matchcake-opt-cpu' and extra == 'extra-13-matchcake-opt-cu130')" },
+    { name = "tornado" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/8a/11/965c6fd8e5cc254f1fe142d547387da17a8ebfd75a3455f637c663fb38a0/terminado-0.18.1.tar.gz", hash = "sha256:de09f2c4b85de4765f7714688fff57d3e75bad1f909b589fde880460c753fd2e", size = 32701, upload-time = "2024-03-12T14:34:39.026Z" }
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 name = "threadpoolctl"
 version = "3.6.0"
@@ -4420,6 +4725,18 @@ wheels = [
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 ]
 
+[[package]]
+name = "tinycss2"
+version = "1.4.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "webencodings" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/7a/fd/7a5ee21fd08ff70d3d33a5781c255cbe779659bd03278feb98b19ee550f4/tinycss2-1.4.0.tar.gz", hash = "sha256:10c0972f6fc0fbee87c3edb76549357415e94548c1ae10ebccdea16fb404a9b7", size = 87085, upload-time = "2024-10-24T14:58:29.895Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/e6/34/ebdc18bae6aa14fbee1a08b63c015c72b64868ff7dae68808ab500c492e2/tinycss2-1.4.0-py3-none-any.whl", hash = "sha256:3a49cf47b7675da0b15d0c6e1df8df4ebd96e9394bb905a5775adb0d884c5289", size = 26610, upload-time = "2024-10-24T14:58:28.029Z" },
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 name = "tomli"
 version = "2.3.0"
@@ -5112,6 +5429,15 @@ wheels = [
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 ]
 
+[[package]]
+name = "uri-template"
+version = "1.3.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/31/c7/0336f2bd0bcbada6ccef7aaa25e443c118a704f828a0620c6fa0207c1b64/uri-template-1.3.0.tar.gz", hash = "sha256:0e00f8eb65e18c7de20d595a14336e9f337ead580c70934141624b6d1ffdacc7", size = 21678, upload-time = "2023-06-21T01:49:05.374Z" }
+wheels = [
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+
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 name = "urllib3"
 version = "2.5.0"
@@ -5139,6 +5465,33 @@ wheels = [
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 ]
 
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+wheels = [
+    { url = "https://files.pythonhosted.org/packages/e2/cc/e097523dd85c9cf5d354f78310927f1656c422bd7b2613b2db3e3f9a0f2c/webcolors-25.10.0-py3-none-any.whl", hash = "sha256:032c727334856fc0b968f63daa252a1ac93d33db2f5267756623c210e57a4f1d", size = 14905, upload-time = "2025-10-31T07:51:01.778Z" },
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+
+[[package]]
+name = "webencodings"
+version = "0.5.1"
+source = { registry = "https://pypi.org/simple" }
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+[[package]]
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+
 [[package]]
 name = "werkzeug"
 version = "3.1.3"

From d6cd179bfc928d5ee34d107b2b84eaed85426a24 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?J=C3=A9r=C3=A9mie=20Gince?=
 <50332514+JeremieGince@users.noreply.github.com>
Date: Fri, 28 Nov 2025 11:45:29 -0500
Subject: [PATCH 2/3] Clean notebook outputs and metadata

Removed execution counts, outputs, and execution metadata from all code cells in nif_deep_learning.ipynb. Also reduced AutoML iterations and max time for faster runs.
---
 notebooks/nif_deep_learning.ipynb | 317 ++----------------------------
 1 file changed, 20 insertions(+), 297 deletions(-)

diff --git a/notebooks/nif_deep_learning.ipynb b/notebooks/nif_deep_learning.ipynb
index 959da37..70c6fe9 100644
--- a/notebooks/nif_deep_learning.ipynb
+++ b/notebooks/nif_deep_learning.ipynb
@@ -10,12 +10,7 @@
    "id": "c11b257a04e4cb48"
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-11-28T16:32:17.598802Z",
-     "start_time": "2025-11-28T16:32:17.581649Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": [
     "import os\n",
@@ -41,7 +36,7 @@
    ],
    "id": "3b95af772cefb5b3",
    "outputs": [],
-   "execution_count": 9
+   "execution_count": null
   },
   {
    "metadata": {},
@@ -50,12 +45,7 @@
    "id": "e1f9ef65d39494b7"
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-11-28T16:32:17.629179Z",
-     "start_time": "2025-11-28T16:32:17.605834Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": [
     "class NIFDL(ClassificationModel):\n",
@@ -146,15 +136,10 @@
    ],
    "id": "586f30eed53a5ae",
    "outputs": [],
-   "execution_count": 10
+   "execution_count": null
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-11-28T16:32:17.644796Z",
-     "start_time": "2025-11-28T16:32:17.634277Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": [
     "# Dataset\n",
@@ -177,7 +162,7 @@
    ],
    "id": "412328c44c55e453",
    "outputs": [],
-   "execution_count": 11
+   "execution_count": null
   },
   {
    "metadata": {},
@@ -186,12 +171,7 @@
    "id": "3847ed18ac6e57fd"
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-11-28T16:32:17.691303Z",
-     "start_time": "2025-11-28T16:32:17.650083Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": [
     "datamodule = DataModule.from_dataset_name(\n",
@@ -204,7 +184,7 @@
    ],
    "id": "42cb94221bd39753",
    "outputs": [],
-   "execution_count": 12
+   "execution_count": null
   },
   {
    "metadata": {},
@@ -216,12 +196,7 @@
    "id": "d13cf82e118d84eb"
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-11-28T16:32:19.519727Z",
-     "start_time": "2025-11-28T16:32:17.696831Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": [
     "model_args = dict(\n",
@@ -242,26 +217,11 @@
     ")"
    ],
    "id": "8832b1d06113ac48",
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "GPU available: True (cuda), used: False\n",
-      "TPU available: False, using: 0 TPU cores\n",
-      "HPU available: False, using: 0 HPUs\n"
-     ]
-    }
-   ],
-   "execution_count": 13
+   "outputs": [],
+   "execution_count": null
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-11-28T16:34:11.900615Z",
-     "start_time": "2025-11-28T16:32:19.585075Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": [
     "start_time = time.perf_counter()\n",
@@ -274,220 +234,8 @@
     "print(\"⚡\" * 20, \"\\nTest Metrics:\\n\", test_metrics, \"\\n\", \"⚡\" * 20)"
    ],
    "id": "7d8a499f05426a0d",
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "  | Name          | Type             | Params | Mode \n",
-      "-----------------------------------------------------------\n",
-      "0 | val_loss      | NLLLoss          | 0      | train\n",
-      "1 | train_loss    | NLLLoss          | 0      | train\n",
-      "2 | _metrics      | MetricCollection | 0      | train\n",
-      "3 | train_metrics | MetricCollection | 0      | train\n",
-      "4 | val_metrics   | MetricCollection | 0      | train\n",
-      "5 | test_metrics  | MetricCollection | 0      | train\n",
-      "6 | flatten       | Flatten          | 0      | train\n",
-      "7 | encoders      | ModuleList       | 1.4 K  | train\n",
-      "8 | readout       | Linear           | 650    | train\n",
-      "-----------------------------------------------------------\n",
-      "2.1 K     Trainable params\n",
-      "0         Non-trainable params\n",
-      "2.1 K     Total params\n",
-      "0.008     Total estimated model params size (MB)\n",
-      "37        Modules in train mode\n",
-      "0         Modules in eval mode\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Checkpoint folder: E:\\Github\\MatchCake-Opt\\notebooks\\data\\automl\\Digits2D\\NIFDL\\checkpoints\n",
-      "Model: NIFDL\n",
-      "NIFDL(\n",
-      "  (val_loss): NLLLoss()\n",
-      "  (train_loss): NLLLoss()\n",
-      "  (_metrics): MetricCollection(\n",
-      "    (MulticlassAccuracy): MulticlassAccuracy()\n",
-      "    (MulticlassF1Score): MulticlassF1Score()\n",
-      "    (MulticlassRecall): MulticlassRecall()\n",
-      "    (MulticlassPrecision): MulticlassPrecision()\n",
-      "    (MulticlassAUROC): MulticlassAUROC(),\n",
-      "    prefix=train_\n",
-      "  )\n",
-      "  (train_metrics): MetricCollection(\n",
-      "    (MulticlassAccuracy): MulticlassAccuracy()\n",
-      "    (MulticlassF1Score): MulticlassF1Score()\n",
-      "    (MulticlassRecall): MulticlassRecall()\n",
-      "    (MulticlassPrecision): MulticlassPrecision()\n",
-      "    (MulticlassAUROC): MulticlassAUROC(),\n",
-      "    prefix=train_\n",
-      "  )\n",
-      "  (val_metrics): MetricCollection(\n",
-      "    (MulticlassAccuracy): MulticlassAccuracy()\n",
-      "    (MulticlassF1Score): MulticlassF1Score()\n",
-      "    (MulticlassRecall): MulticlassRecall()\n",
-      "    (MulticlassPrecision): MulticlassPrecision()\n",
-      "    (MulticlassAUROC): MulticlassAUROC(),\n",
-      "    prefix=val_\n",
-      "  )\n",
-      "  (test_metrics): MetricCollection(\n",
-      "    (MulticlassAccuracy): MulticlassAccuracy()\n",
-      "    (MulticlassF1Score): MulticlassF1Score()\n",
-      "    (MulticlassRecall): MulticlassRecall()\n",
-      "    (MulticlassPrecision): MulticlassPrecision()\n",
-      "    (MulticlassAUROC): MulticlassAUROC(),\n",
-      "    prefix=test_\n",
-      "  )\n",
-      "  (flatten): Flatten(start_dim=1, end_dim=-1)\n",
-      "  (encoders): ModuleList(\n",
-      "    (0-7): 8 x <Quantum Torch Layer: func=circuit>\n",
-      "  )\n",
-      "  (readout): Linear(in_features=64, out_features=10, bias=True)\n",
-      ")\n",
-      "Max epochs: 10\n",
-      "Max time: 00:00:01:00\n",
-      "Monitor: val_loss (min)\n",
-      "Overwrite fit: True\n",
-      "\n",
-      "Epoch Progress:   0%|          | 0/10 [00:00<?, ?it/s]\u001B[A"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Time limit reached. Elapsed time is 0:01:00. Signaling Trainer to stop.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "Epoch Progress:   0%|          | 0/10 [01:21<?, ?it/s, v_num=0, train_loss=2.32, val_loss=2.3, val_MulticlassAccuracy=0.0988, val_MulticlassF1Score=0.0988, val_MulticlassRecall=0.0988, val_MulticlassPrecision=0.0988, val_MulticlassAUROC=0.586]\u001B[A\n",
-      "Epoch Progress:  10%|█         | 1/10 [01:21<12:10, 81.16s/it, v_num=0, train_loss=2.32, val_loss=2.3, val_MulticlassAccuracy=0.0988, val_MulticlassF1Score=0.0988, val_MulticlassRecall=0.0988, val_MulticlassPrecision=0.0988, val_MulticlassAUROC=0.586]\u001B[A\n",
-      "Epoch Progress:  10%|█         | 1/10 [01:21<12:11, 81.23s/it, v_num=0, train_loss=2.32, val_loss=2.3, val_MulticlassAccuracy=0.0988, val_MulticlassF1Score=0.0988, val_MulticlassRecall=0.0988, val_MulticlassPrecision=0.0988, val_MulticlassAUROC=0.586]\u001B[A"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Restoring states from the checkpoint path at E:\\Github\\MatchCake-Opt\\notebooks\\data\\automl\\Digits2D\\NIFDL\\checkpoints\\epoch00-step0019.ckpt\n",
-      "Loaded model weights from the checkpoint at E:\\Github\\MatchCake-Opt\\notebooks\\data\\automl\\Digits2D\\NIFDL\\checkpoints\\epoch00-step0019.ckpt\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    },
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-       "┃\u001B[1m \u001B[0m\u001B[1m     Validate metric     \u001B[0m\u001B[1m \u001B[0m┃\u001B[1m \u001B[0m\u001B[1m      DataLoader 0       \u001B[0m\u001B[1m \u001B[0m┃\n",
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-       "│\u001B[36m \u001B[0m\u001B[36m   val_MulticlassAUROC   \u001B[0m\u001B[36m \u001B[0m│\u001B[35m \u001B[0m\u001B[35m   0.5861977338790894    \u001B[0m\u001B[35m \u001B[0m│\n",
-       "│\u001B[36m \u001B[0m\u001B[36m val_MulticlassAccuracy  \u001B[0m\u001B[36m \u001B[0m│\u001B[35m \u001B[0m\u001B[35m   0.09876543283462524   \u001B[0m\u001B[35m \u001B[0m│\n",
-       "│\u001B[36m \u001B[0m\u001B[36m  val_MulticlassF1Score  \u001B[0m\u001B[36m \u001B[0m│\u001B[35m \u001B[0m\u001B[35m   0.09876543283462524   \u001B[0m\u001B[35m \u001B[0m│\n",
-       "│\u001B[36m \u001B[0m\u001B[36m val_MulticlassPrecision \u001B[0m\u001B[36m \u001B[0m│\u001B[35m \u001B[0m\u001B[35m   0.09876543283462524   \u001B[0m\u001B[35m \u001B[0m│\n",
-       "│\u001B[36m \u001B[0m\u001B[36m  val_MulticlassRecall   \u001B[0m\u001B[36m \u001B[0m│\u001B[35m \u001B[0m\u001B[35m   0.09876543283462524   \u001B[0m\u001B[35m \u001B[0m│\n",
-       "│\u001B[36m \u001B[0m\u001B[36m        val_loss         \u001B[0m\u001B[36m \u001B[0m│\u001B[35m \u001B[0m\u001B[35m   2.3029329776763916    \u001B[0m\u001B[35m \u001B[0m│\n",
-       "└───────────────────────────┴───────────────────────────┘\n"
-      ],
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
-       "┃<span style=\"font-weight: bold\">      Validate metric      </span>┃<span style=\"font-weight: bold\">       DataLoader 0        </span>┃\n",
-       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
-       "│<span style=\"color: #008080; text-decoration-color: #008080\">    val_MulticlassAUROC    </span>│<span style=\"color: #800080; text-decoration-color: #800080\">    0.5861977338790894     </span>│\n",
-       "│<span style=\"color: #008080; text-decoration-color: #008080\">  val_MulticlassAccuracy   </span>│<span style=\"color: #800080; text-decoration-color: #800080\">    0.09876543283462524    </span>│\n",
-       "│<span style=\"color: #008080; text-decoration-color: #008080\">   val_MulticlassF1Score   </span>│<span style=\"color: #800080; text-decoration-color: #800080\">    0.09876543283462524    </span>│\n",
-       "│<span style=\"color: #008080; text-decoration-color: #008080\">  val_MulticlassPrecision  </span>│<span style=\"color: #800080; text-decoration-color: #800080\">    0.09876543283462524    </span>│\n",
-       "│<span style=\"color: #008080; text-decoration-color: #008080\">   val_MulticlassRecall    </span>│<span style=\"color: #800080; text-decoration-color: #800080\">    0.09876543283462524    </span>│\n",
-       "│<span style=\"color: #008080; text-decoration-color: #008080\">         val_loss          </span>│<span style=\"color: #800080; text-decoration-color: #800080\">    2.3029329776763916     </span>│\n",
-       "└───────────────────────────┴───────────────────────────┘\n",
-       "</pre>\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data",
-     "jetTransient": {
-      "display_id": null
-     }
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Restoring states from the checkpoint path at E:\\Github\\MatchCake-Opt\\notebooks\\data\\automl\\Digits2D\\NIFDL\\checkpoints\\epoch00-step0019.ckpt\n",
-      "Loaded model weights from the checkpoint at E:\\Github\\MatchCake-Opt\\notebooks\\data\\automl\\Digits2D\\NIFDL\\checkpoints\\epoch00-step0019.ckpt\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Metrics saved to E:\\Github\\MatchCake-Opt\\notebooks\\data\\automl\\Digits2D\\NIFDL\\checkpoints\\validation_metrics.json\n",
-      "Time taken: 0:01:42.762405\n",
-      "⚡⚡⚡⚡⚡⚡⚡⚡⚡⚡⚡⚡⚡⚡⚡⚡⚡⚡⚡⚡ \n",
-      "Validation Metrics:\n",
-      " {'val_loss': 2.3029329776763916, 'val_MulticlassAccuracy': 0.09876543283462524, 'val_MulticlassF1Score': 0.09876543283462524, 'val_MulticlassRecall': 0.09876543283462524, 'val_MulticlassPrecision': 0.09876543283462524, 'val_MulticlassAUROC': 0.5861977338790894, 'validation_time': 17.814280099992175, 'training_time': 84.94243890000507} \n",
-      " ⚡⚡⚡⚡⚡⚡⚡⚡⚡⚡⚡⚡⚡⚡⚡⚡⚡⚡⚡⚡\n"
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-       "┃\u001B[1m \u001B[0m\u001B[1m       Test metric       \u001B[0m\u001B[1m \u001B[0m┃\u001B[1m \u001B[0m\u001B[1m      DataLoader 0       \u001B[0m\u001B[1m \u001B[0m┃\n",
-       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
-       "│\u001B[36m \u001B[0m\u001B[36m  test_MulticlassAUROC   \u001B[0m\u001B[36m \u001B[0m│\u001B[35m \u001B[0m\u001B[35m    0.579858660697937    \u001B[0m\u001B[35m \u001B[0m│\n",
-       "│\u001B[36m \u001B[0m\u001B[36m test_MulticlassAccuracy \u001B[0m\u001B[36m \u001B[0m│\u001B[35m \u001B[0m\u001B[35m   0.10555555671453476   \u001B[0m\u001B[35m \u001B[0m│\n",
-       "│\u001B[36m \u001B[0m\u001B[36m test_MulticlassF1Score  \u001B[0m\u001B[36m \u001B[0m│\u001B[35m \u001B[0m\u001B[35m   0.10555555671453476   \u001B[0m\u001B[35m \u001B[0m│\n",
-       "│\u001B[36m \u001B[0m\u001B[36mtest_MulticlassPrecision \u001B[0m\u001B[36m \u001B[0m│\u001B[35m \u001B[0m\u001B[35m   0.10555555671453476   \u001B[0m\u001B[35m \u001B[0m│\n",
-       "│\u001B[36m \u001B[0m\u001B[36m  test_MulticlassRecall  \u001B[0m\u001B[36m \u001B[0m│\u001B[35m \u001B[0m\u001B[35m   0.10555555671453476   \u001B[0m\u001B[35m \u001B[0m│\n",
-       "│\u001B[36m \u001B[0m\u001B[36m        test_loss        \u001B[0m\u001B[36m \u001B[0m│\u001B[35m \u001B[0m\u001B[35m    2.303359031677246    \u001B[0m\u001B[35m \u001B[0m│\n",
-       "└───────────────────────────┴───────────────────────────┘\n"
-      ],
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
-       "┃<span style=\"font-weight: bold\">        Test metric        </span>┃<span style=\"font-weight: bold\">       DataLoader 0        </span>┃\n",
-       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
-       "│<span style=\"color: #008080; text-decoration-color: #008080\">   test_MulticlassAUROC    </span>│<span style=\"color: #800080; text-decoration-color: #800080\">     0.579858660697937     </span>│\n",
-       "│<span style=\"color: #008080; text-decoration-color: #008080\">  test_MulticlassAccuracy  </span>│<span style=\"color: #800080; text-decoration-color: #800080\">    0.10555555671453476    </span>│\n",
-       "│<span style=\"color: #008080; text-decoration-color: #008080\">  test_MulticlassF1Score   </span>│<span style=\"color: #800080; text-decoration-color: #800080\">    0.10555555671453476    </span>│\n",
-       "│<span style=\"color: #008080; text-decoration-color: #008080\"> test_MulticlassPrecision  </span>│<span style=\"color: #800080; text-decoration-color: #800080\">    0.10555555671453476    </span>│\n",
-       "│<span style=\"color: #008080; text-decoration-color: #008080\">   test_MulticlassRecall   </span>│<span style=\"color: #800080; text-decoration-color: #800080\">    0.10555555671453476    </span>│\n",
-       "│<span style=\"color: #008080; text-decoration-color: #008080\">         test_loss         </span>│<span style=\"color: #800080; text-decoration-color: #800080\">     2.303359031677246     </span>│\n",
-       "└───────────────────────────┴───────────────────────────┘\n",
-       "</pre>\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data",
-     "jetTransient": {
-      "display_id": null
-     }
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Metrics saved to E:\\Github\\MatchCake-Opt\\notebooks\\data\\automl\\Digits2D\\NIFDL\\checkpoints\\test_metrics.json\n",
-      "⚡⚡⚡⚡⚡⚡⚡⚡⚡⚡⚡⚡⚡⚡⚡⚡⚡⚡⚡⚡ \n",
-      "Test Metrics:\n",
-      " {'test_loss': 2.303359031677246, 'test_MulticlassAccuracy': 0.10555555671453476, 'test_MulticlassF1Score': 0.10555555671453476, 'test_MulticlassRecall': 0.10555555671453476, 'test_MulticlassPrecision': 0.10555555671453476, 'test_MulticlassAUROC': 0.579858660697937, 'test_time': 9.541893699992215} \n",
-      " ⚡⚡⚡⚡⚡⚡⚡⚡⚡⚡⚡⚡⚡⚡⚡⚡⚡⚡⚡⚡\n"
-     ]
-    }
-   ],
-   "execution_count": 14
+   "outputs": [],
+   "execution_count": null
   },
   {
    "metadata": {},
@@ -496,21 +244,16 @@
    "id": "688b66fca9889c36"
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-11-28T16:34:11.978217Z",
-     "start_time": "2025-11-28T16:34:11.967195Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": [
     "automl_pipeline = AutoMLPipeline(\n",
     "    model_cls=model_cls,\n",
     "    datamodule=datamodule,\n",
     "    checkpoint_folder=checkpoint_folder,\n",
-    "    automl_iterations=5,  # increase at least to 32\n",
+    "    automl_iterations=2,  # increase at least to 32\n",
     "    inner_max_epochs=10,  # increase at least to 128\n",
-    "    inner_max_time=\"00:00:01:00\",  # increase at least to \"00:00:10:00\"\n",
+    "    inner_max_time=\"00:00:00:10\",  # increase at least to \"00:00:10:00\"\n",
     "    automl_overwrite_fit=True,\n",
     "    accelerator=\"cpu\",\n",
     "    **pipeline_args\n",
@@ -518,17 +261,10 @@
    ],
    "id": "16001dd8071bc8b0",
    "outputs": [],
-   "execution_count": 15
+   "execution_count": null
   },
   {
-   "metadata": {
-    "jupyter": {
-     "is_executing": true
-    },
-    "ExecuteTime": {
-     "start_time": "2025-11-28T16:34:11.982237Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": [
     "start_time = time.perf_counter()\n",
@@ -537,20 +273,7 @@
     "print(f\"Time taken: {end_time - start_time:.4f} seconds\")"
    ],
    "id": "8c84ee5352220970",
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "AutoML iterations:   0%|          | 0/5 [00:00<?, ?it/s]\u001B[A[INFO 11-28 11:34:11] ax.api.client: GenerationStrategy(name='Center+Sobol+MBM:fast', nodes=[CenterGenerationNode(next_node_name='Sobol'), GenerationNode(node_name='Sobol', generator_specs=[GeneratorSpec(generator_enum=Sobol, model_key_override=None)], transition_criteria=[MinTrials(transition_to='MBM'), MinTrials(transition_to='MBM')]), GenerationNode(node_name='MBM', generator_specs=[GeneratorSpec(generator_enum=BoTorch, model_key_override=None)], transition_criteria=[])]) chosen based on user input and problem structure.\n",
-      "[INFO 11-28 11:34:11] ax.api.client: Generated new trial 0 with parameters {'learning_rate': 0.050005, 'n_qubits': 18, 'n_layers': 5} using GenerationNode CenterOfSearchSpace.\n",
-      "GPU available: True (cuda), used: False\n",
-      "TPU available: False, using: 0 TPU cores\n",
-      "HPU available: False, using: 0 HPUs\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "execution_count": null
   },
   {

From dedd67a2dbc23b145311f3f638dd09281ac5477f Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?J=C3=A9r=C3=A9mie=20Gince?=
 <50332514+JeremieGince@users.noreply.github.com>
Date: Fri, 28 Nov 2025 12:08:11 -0500
Subject: [PATCH 3/3] Add uv.lock handling to build workflow

The workflow now runs 'uv lock' after bumping the package version and adds 'uv.lock' to the commit. This ensures the lock file stays in sync with version changes. Also, the PyPI publish step was moved to the end of the workflow.
---
 .github/workflows/build_dist.yml | 16 +++++++++-------
 1 file changed, 9 insertions(+), 7 deletions(-)

diff --git a/.github/workflows/build_dist.yml b/.github/workflows/build_dist.yml
index 0e52588..b2c28e5 100644
--- a/.github/workflows/build_dist.yml
+++ b/.github/workflows/build_dist.yml
@@ -49,6 +49,7 @@ jobs:
     - name: Bump package version in pyproject.toml
       run: |
         uv version ${{steps.version.outputs.new_tag}}
+        uv lock
 
     - name: Build dist
       run: |
@@ -60,6 +61,7 @@ jobs:
         git config --local user.email "action@github.com"
         git config --local user.name "GitHub Action"
         git add pyproject.toml
+        git add uv.lock
         git diff-index --quiet HEAD || git commit -m "Updating version of pyproject.toml"
 
     - name: Push the new pyproject.toml
@@ -84,13 +86,6 @@ jobs:
         tag_name: ${{ steps.tag.outputs.new_tag }}
         generate_release_notes: true
 
-# TODO: Uncomment to publish on PyPI
-#    - name: Publish distribution to PyPI
-#      uses: pypa/gh-action-pypi-publish@release/v1
-#      with:
-#        skip_existing: true
-#        verbose: true
-
     - name: Merge 'main' branch -> 'dev' branch
       uses: devmasx/merge-branch@master
       with:
@@ -100,3 +95,10 @@ jobs:
         message: "Automatic merge from 'main' into 'dev' [skip actions]"
         label_name: "gh-actions"
         github_token: ${{ secrets.GITHUB_TOKEN }}
+
+# TODO: Uncomment to publish on PyPI
+#    - name: Publish distribution to PyPI
+#      uses: pypa/gh-action-pypi-publish@release/v1
+#      with:
+#        skip_existing: true
+#        verbose: true