Add CPU accelerator option and update dependencies

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",
     ")"
    ],

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",
     ")"

notebooks/nif_deep_learning.ipynb

@@ -27,7 +27,7 @@
     "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",
@@ -114,8 +114,8 @@
     "        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",
@@ -198,8 +198,6 @@
   {
    "metadata": {},
    "cell_type": "code",
-   "outputs": [],
-   "execution_count": null,
    "source": [
     "model_args = dict(\n",
     "    n_qubits=16,\n",
@@ -214,16 +212,17 @@
     "    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": [],
+   "execution_count": null
   },
   {
    "metadata": {},
    "cell_type": "code",
-   "outputs": [],
-   "execution_count": null,
    "source": [
     "start_time = time.perf_counter()\n",
     "metrics = lightning_pipeline.run()\n",
@@ -234,7 +233,9 @@
     "test_metrics = lightning_pipeline.run_test()\n",
     "print(\"⚡\" * 20, \"\\nTest Metrics:\\n\", test_metrics, \"\\n\", \"⚡\" * 20)"
    ],
-   "id": "7d8a499f05426a0d"
+   "id": "7d8a499f05426a0d",
+   "outputs": [],
+   "execution_count": null
   },
   {
    "metadata": {},
@@ -245,21 +246,22 @@
   {
    "metadata": {},
    "cell_type": "code",
-   "outputs": [],
-   "execution_count": null,
    "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",
     ")"
    ],
-   "id": "16001dd8071bc8b0"
+   "id": "16001dd8071bc8b0",
+   "outputs": [],
+   "execution_count": null
   },
   {
    "metadata": {},

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",