diff --git a/blackwater/data/loaders/dataclasses.py b/blackwater/data/loaders/dataclasses.py index 6c02342..05946d5 100644 --- a/blackwater/data/loaders/dataclasses.py +++ b/blackwater/data/loaders/dataclasses.py @@ -7,7 +7,7 @@ from blackwater.data.dataio import ExpValDataReader - +# pylint: disable=abstract-method class ExpValDataSet(Dataset): """ExpValDataLoader.""" diff --git a/docs/guides/01_encoders.ipynb b/docs/guides/01_encoders.ipynb index 4364ee4..09e468a 100644 --- a/docs/guides/01_encoders.ipynb +++ b/docs/guides/01_encoders.ipynb @@ -10,7 +10,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 5, "id": "7e59d4a3-8076-49ae-ab90-b04958ce2733", "metadata": {}, "outputs": [], @@ -18,7 +18,7 @@ "from qiskit import QuantumCircuit, transpile\n", "from qiskit.providers.fake_provider import FakeLimaV2\n", "\n", - "from blackwater.data.encoders.torch import (\n", + "from blackwater.data.encoders.graph_utils import (\n", " circuit_to_json_graph, \n", " backend_to_json_graph, \n", " BackendNodeEncoder\n", @@ -27,7 +27,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 6, "id": "b2c2c043-e0b6-4c8d-8c25-5a8bfad1764d", "metadata": {}, "outputs": [ @@ -52,7 +52,7 @@ " 0 1 " ] }, - "execution_count": 3, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -67,7 +67,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 7, "id": "db427da1-12d6-4c47-9e21-47834b2636c1", "metadata": {}, "outputs": [ @@ -77,7 +77,7 @@ "GraphData(nodes=[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.0, 0.0, 0.0], [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.0, 0.0, 0.0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0.0, 0.0, 0.0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0.0, 0.0, 0.0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0.0, 0.0, 0.0]], edges=[[0, 1], [1, 2], [1, 2], [2, 4], [2, 3]], edge_features=[[0.0], [0.0], [0.0], [0.0], [0.0]])" ] }, - "execution_count": 4, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -89,7 +89,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 8, "id": "b372d23a-ae39-47a6-911d-5c323b8e465d", "metadata": {}, "outputs": [ @@ -99,7 +99,7 @@ "GraphData(nodes=[[0, 1, 0, 0, 0, 0, 0, 0, 0, 1.5707963267948966, 0.0, 0.0, 5.9698643286635694e-05, 9.355584184359312e-05, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0, 0, 1, 0, 0, 0, 0, 0, 0, 0.0, 0.0, 0.0, 5.9698643286635694e-05, 9.355584184359312e-05, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0, 1, 0, 0, 0, 0, 0, 0, 0, 1.5707963267948966, 0.0, 0.0, 5.9698643286635694e-05, 9.355584184359312e-05, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0, 0, 0, 0, 1, 0, 0, 0, 0, 0.0, 0.0, 0.0, 5.9698643286635694e-05, 9.355584184359312e-05, 8.305997230317399e-05, 0.00011553074510239035, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0, 0, 0, 0, 0, 0, 0, 0, 1, 0.0, 0.0, 0.0, 5.9698643286635694e-05, 9.355584184359312e-05, 8.305997230317399e-05, 0.00011553074510239035, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0, 0, 0, 0, 0, 0, 1, 0, 0, 0.0, 0.0, 0.0, 5.9698643286635694e-05, 9.355584184359312e-05, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0, 0, 0, 0, 0, 0, 1, 0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 8.305997230317399e-05, 0.00011553074510239035, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]], edges=[[0, 1], [1, 2], [2, 3], [3, 4], [3, 4], [4, 6], [4, 5]], edge_features=[[0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0]])" ] }, - "execution_count": 5, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -114,7 +114,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 9, "id": "74d1d837-0be4-4a32-8558-0ea82a4f29d7", "metadata": {}, "outputs": [ @@ -124,7 +124,7 @@ "GraphData(nodes=[[5.9698643286635694e-05, 9.355584184359312e-05, 5029685549.923759], [8.305997230317399e-05, 0.00011553074510239035, 5128321697.435369], [0.00010377694598809795, 9.477169960638749e-05, 5247491310.11471], [4.358447375590962e-05, 4.645933441447346e-05, 5303339662.601714], [1.7543975812787366e-05, 1.6441110002077736e-05, 5091790567.452984]], edges=[[4, 3], [3, 4], [0, 1], [1, 0], [3, 1], [1, 3], [2, 1], [1, 2]], edge_features=[[0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0]])" ] }, - "execution_count": 6, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -136,7 +136,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 11, "id": "1047bf61-566f-458a-a5de-787f16824e62", "metadata": {}, "outputs": [], @@ -146,12 +146,12 @@ "from qiskit.quantum_info import SparsePauliOp\n", "from torch_geometric.data import Data\n", "\n", - "from blackwater.data.encoders.torch import ExpValData" + "from blackwater.data.encoders.graph_utils import ExpValData" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 12, "id": "fc1a98e0-d9c6-4c2c-a6e0-5fa85cc5eaec", "metadata": {}, "outputs": [ @@ -176,7 +176,7 @@ " 0 1 " ] }, - "execution_count": 8, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -191,7 +191,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 13, "id": "aecbe254-2b9b-49f0-a05b-bf73620e46bf", "metadata": {}, "outputs": [], @@ -204,7 +204,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 14, "id": "12d5745b-23f2-44e8-872f-0f3c5690e956", "metadata": {}, "outputs": [ @@ -214,7 +214,7 @@ "ExpValData(circuit=GraphData(nodes=[[0, 1, 0, 0, 0, 0, 0, 0, 0, 1.5707963267948966, 0.0, 0.0, 5.9698643286635694e-05, 9.355584184359312e-05, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0, 0, 1, 0, 0, 0, 0, 0, 0, 0.0, 0.0, 0.0, 5.9698643286635694e-05, 9.355584184359312e-05, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0, 1, 0, 0, 0, 0, 0, 0, 0, 1.5707963267948966, 0.0, 0.0, 5.9698643286635694e-05, 9.355584184359312e-05, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0, 0, 0, 0, 1, 0, 0, 0, 0, 0.0, 0.0, 0.0, 5.9698643286635694e-05, 9.355584184359312e-05, 8.305997230317399e-05, 0.00011553074510239035, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0, 0, 0, 0, 0, 0, 0, 0, 1, 0.0, 0.0, 0.0, 5.9698643286635694e-05, 9.355584184359312e-05, 8.305997230317399e-05, 0.00011553074510239035, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0, 0, 0, 0, 0, 0, 1, 0, 0, 0.0, 0.0, 0.0, 5.9698643286635694e-05, 9.355584184359312e-05, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0, 0, 0, 0, 0, 0, 1, 0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 8.305997230317399e-05, 0.00011553074510239035, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]], edges=[[0, 1], [1, 2], [2, 3], [3, 4], [3, 4], [4, 6], [4, 5]], edge_features=[[0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0]]), circuit_depth=5, expectation_values=[0.0], observable=OperatorData(operator=[[1.0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0]]), backend=GraphData(nodes=[[5.9698643286635694e-05, 9.355584184359312e-05, 5029685549.923759], [8.305997230317399e-05, 0.00011553074510239035, 5128321697.435369], [0.00010377694598809795, 9.477169960638749e-05, 5247491310.11471], [4.358447375590962e-05, 4.645933441447346e-05, 5303339662.601714], [1.7543975812787366e-05, 1.6441110002077736e-05, 5091790567.452984]], edges=[[4, 3], [3, 4], [0, 1], [1, 0], [3, 1], [1, 3], [2, 1], [1, 2]], edge_features=[[0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0]]))" ] }, - "execution_count": 10, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -232,7 +232,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 15, "id": "31ae08ea-d0f7-44aa-ae1f-35603694b991", "metadata": {}, "outputs": [ @@ -242,7 +242,7 @@ "Data(x=[7, 22], edge_index=[2, 7], edge_attr=[7, 1], y=[1, 1], circuit_depth=[1, 1], observable=[1, 1, 13], backend_nodes=[5, 3], backend_edges=[2, 8], backend_edge_features=[8, 1])" ] }, - "execution_count": 11, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -255,7 +255,7 @@ { "cell_type": "code", "execution_count": null, - "id": "361ead2b-831a-49b7-8b9c-fe89273e9f28", + "id": "3bdd86e6", "metadata": {}, "outputs": [], "source": [] @@ -277,7 +277,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.16" + "version": "3.9.19" } }, "nbformat": 4, diff --git a/docs/guides/03_data_io.ipynb b/docs/guides/03_data_io.ipynb index f80a936..52502b0 100644 --- a/docs/guides/03_data_io.ipynb +++ b/docs/guides/03_data_io.ipynb @@ -10,7 +10,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 2, "id": "5283f6a6-6131-4518-aff0-5228df3ec2ce", "metadata": {}, "outputs": [], @@ -19,14 +19,14 @@ "from qiskit.providers.fake_provider import FakeLimaV2\n", "from qiskit.quantum_info import SparsePauliOp\n", "\n", - "from blackwater.data.encoders.torch import ExpValData\n", + "from blackwater.data.encoders.graph_utils import ExpValData\n", "from blackwater.data.dataio.dataio import ExpValDataWriter\n", "from blackwater.data.loaders.dataclasses import ExpValDataSet" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 3, "id": "e75f5374-5632-458a-8b97-7051d0a2f740", "metadata": {}, "outputs": [ @@ -51,7 +51,7 @@ " 0 1 " ] }, - "execution_count": 6, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -66,7 +66,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 4, "id": "d76e0ba4-67d5-4e3f-92c4-052898d0d3df", "metadata": {}, "outputs": [], @@ -79,7 +79,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 5, "id": "293609ae-43d0-4ddf-a19b-43c84303a0dd", "metadata": {}, "outputs": [ @@ -89,7 +89,7 @@ "ExpValData(circuit=GraphData(nodes=[[0, 1, 0, 0, 0, 0, 0, 0, 0, 1.5707963267948966, 0.0, 0.0, 5.9698643286635694e-05, 9.355584184359312e-05, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0, 0, 1, 0, 0, 0, 0, 0, 0, 0.0, 0.0, 0.0, 5.9698643286635694e-05, 9.355584184359312e-05, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0, 1, 0, 0, 0, 0, 0, 0, 0, 1.5707963267948966, 0.0, 0.0, 5.9698643286635694e-05, 9.355584184359312e-05, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0, 0, 0, 0, 1, 0, 0, 0, 0, 0.0, 0.0, 0.0, 5.9698643286635694e-05, 9.355584184359312e-05, 8.305997230317399e-05, 0.00011553074510239035, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0, 0, 0, 0, 0, 0, 0, 0, 1, 0.0, 0.0, 0.0, 5.9698643286635694e-05, 9.355584184359312e-05, 8.305997230317399e-05, 0.00011553074510239035, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0, 0, 0, 0, 0, 0, 1, 0, 0, 0.0, 0.0, 0.0, 5.9698643286635694e-05, 9.355584184359312e-05, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0, 0, 0, 0, 0, 0, 1, 0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 8.305997230317399e-05, 0.00011553074510239035, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]], edges=[[0, 1], [1, 2], [2, 3], [3, 4], [3, 4], [4, 6], [4, 5]], edge_features=[[0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0]]), circuit_depth=5, expectation_values=[0.0], observable=OperatorData(operator=[[1.0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0]]), backend=GraphData(nodes=[[5.9698643286635694e-05, 9.355584184359312e-05, 5029685549.923759], [8.305997230317399e-05, 0.00011553074510239035, 5128321697.435369], [0.00010377694598809795, 9.477169960638749e-05, 5247491310.11471], [4.358447375590962e-05, 4.645933441447346e-05, 5303339662.601714], [1.7543975812787366e-05, 1.6441110002077736e-05, 5091790567.452984]], edges=[[4, 3], [3, 4], [0, 1], [1, 0], [3, 1], [1, 3], [2, 1], [1, 2]], edge_features=[[0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0]]))" ] }, - "execution_count": 8, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -107,7 +107,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 6, "id": "2bb31160-bd1b-429b-b313-29c5f0fe285b", "metadata": {}, "outputs": [], @@ -122,7 +122,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 7, "id": "83d50814-f4c9-4c16-bcae-83bdd61770b1", "metadata": {}, "outputs": [], @@ -133,7 +133,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 8, "id": "3271e649-3b58-4db0-8bf5-9886fc579831", "metadata": {}, "outputs": [ @@ -178,7 +178,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.16" + "version": "3.9.19" } }, "nbformat": 4, diff --git a/docs/installation_guide.md b/docs/installation_guide.md index 61c96fd..b78c8f7 100644 --- a/docs/installation_guide.md +++ b/docs/installation_guide.md @@ -2,9 +2,7 @@ ## PyPi -```shell -pip install blackwater -``` +Installation through PyPi is not available ## Local installation @@ -15,20 +13,14 @@ pip install blackwater pip install -r requirements.txt ``` -3. Installing Optional Dependencies - -```shell -pip install -r requirements-dev.txt -``` -4. Installing Blackwater +3. Installing Blackwater ```shell pip install . ``` -5. Testing the Installation +4. Testing the Installation ```shell -tox -epy39 -tox -elint -``` \ No newline at end of file +Explore `/docs/guides/` or `/docs/tutorials/` notebooks with examples. +``` diff --git a/docs/requirements-doc.txt b/docs/requirements-doc.txt index 1b2f22b..9f88409 100644 --- a/docs/requirements-doc.txt +++ b/docs/requirements-doc.txt @@ -18,8 +18,8 @@ qiskit-terra>=0.21.1 sphinx-copybutton>=0.5.0 qiskit-ibm-runtime==0.9.3 torch>=2.0.1 -qiskit-aer>=0.11.0 -qiskit-terra>=0.23.1 +qiskit-aer>=0.11.0,<=0.13.3 +qiskit>=0.23.1,<0.45.1 qiskit-experiments>=0.4.0 Gymnasium>=0.26.3 ray>=2.4.0 diff --git a/docs/tutorials/01_ngem.ipynb b/docs/tutorials/01_ngem.ipynb index 5e626ba..ba132ad 100644 --- a/docs/tutorials/01_ngem.ipynb +++ b/docs/tutorials/01_ngem.ipynb @@ -11,6 +11,45 @@ { "cell_type": "code", "execution_count": 1, + "id": "4d6f4441", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Collecting seaborn\n", + " Downloading seaborn-0.13.2-py3-none-any.whl.metadata (5.4 kB)\n", + "Requirement already satisfied: numpy!=1.24.0,>=1.20 in /opt/anaconda3/envs/blackwater/lib/python3.9/site-packages (from seaborn) (1.26.4)\n", + "Requirement already satisfied: pandas>=1.2 in /opt/anaconda3/envs/blackwater/lib/python3.9/site-packages (from seaborn) (2.2.1)\n", + "Requirement already satisfied: matplotlib!=3.6.1,>=3.4 in /opt/anaconda3/envs/blackwater/lib/python3.9/site-packages (from seaborn) (3.8.4)\n", + "Requirement already satisfied: contourpy>=1.0.1 in /opt/anaconda3/envs/blackwater/lib/python3.9/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (1.2.1)\n", + "Requirement already satisfied: cycler>=0.10 in /opt/anaconda3/envs/blackwater/lib/python3.9/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (0.12.1)\n", + "Requirement already satisfied: fonttools>=4.22.0 in /opt/anaconda3/envs/blackwater/lib/python3.9/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (4.51.0)\n", + "Requirement already satisfied: kiwisolver>=1.3.1 in /opt/anaconda3/envs/blackwater/lib/python3.9/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (1.4.5)\n", + "Requirement already satisfied: packaging>=20.0 in /opt/anaconda3/envs/blackwater/lib/python3.9/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (24.0)\n", + "Requirement already satisfied: pillow>=8 in /opt/anaconda3/envs/blackwater/lib/python3.9/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (10.3.0)\n", + "Requirement already satisfied: pyparsing>=2.3.1 in /opt/anaconda3/envs/blackwater/lib/python3.9/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (3.1.2)\n", + "Requirement already satisfied: python-dateutil>=2.7 in /opt/anaconda3/envs/blackwater/lib/python3.9/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (2.9.0)\n", + "Requirement already satisfied: importlib-resources>=3.2.0 in /opt/anaconda3/envs/blackwater/lib/python3.9/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (6.4.0)\n", + "Requirement already satisfied: pytz>=2020.1 in /opt/anaconda3/envs/blackwater/lib/python3.9/site-packages (from pandas>=1.2->seaborn) (2024.1)\n", + "Requirement already satisfied: tzdata>=2022.7 in /opt/anaconda3/envs/blackwater/lib/python3.9/site-packages (from pandas>=1.2->seaborn) (2024.1)\n", + "Requirement already satisfied: zipp>=3.1.0 in /opt/anaconda3/envs/blackwater/lib/python3.9/site-packages (from importlib-resources>=3.2.0->matplotlib!=3.6.1,>=3.4->seaborn) (3.17.0)\n", + "Requirement already satisfied: six>=1.5 in /opt/anaconda3/envs/blackwater/lib/python3.9/site-packages (from python-dateutil>=2.7->matplotlib!=3.6.1,>=3.4->seaborn) (1.16.0)\n", + "Downloading seaborn-0.13.2-py3-none-any.whl (294 kB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m294.9/294.9 kB\u001b[0m \u001b[31m3.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0ma \u001b[36m0:00:01\u001b[0m\n", + "\u001b[?25hInstalling collected packages: seaborn\n", + "Successfully installed seaborn-0.13.2\n" + ] + } + ], + "source": [ + "! pip install seaborn" + ] + }, + { + "cell_type": "code", + "execution_count": 2, "id": "1aa350cd-4516-488d-8f12-4bc2afd04af7", "metadata": {}, "outputs": [], @@ -24,15 +63,13 @@ "from qiskit.quantum_info import SparsePauliOp\n", "from qiskit.primitives import BackendEstimator, Estimator\n", "\n", - "from tqdm.notebook import tqdm_notebook\n", - "\n", - "from blackwater.data.encoders.torch import ExpValData\n", + "from blackwater.data.encoders.graph_utils import ExpValData\n", "from blackwater.data.dataio import ExpValDataWriter" ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "id": "0bdc48cd-ed21-4c5d-87ec-ca3c43bb6f3b", "metadata": {}, "outputs": [], @@ -48,14 +85,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "id": "dfda266f-5e69-4e1f-8a25-da2f3ebee92b", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Processing 0 file...\n", + "Processing 1 file...\n" + ] + } + ], "source": [ "# generate data\n", "\n", - "N_FILES = 10\n", + "N_FILES = 2\n", "N_ENTRIES_PER_FILE = 100\n", "N_QUBITS = 3\n", "DEPTH = 3\n", @@ -63,7 +109,8 @@ "operator = SparsePauliOp([\"ZZZZZ\", \"ZIIIZ\"])\n", "writer = ExpValDataWriter()\n", "\n", - "for idx in tqdm_notebook(range(N_FILES)):\n", + "for idx in range(N_FILES):\n", + " print(f\"Processing {idx} file...\")\n", " \n", " entries = []\n", " for _ in range(N_ENTRIES_PER_FILE):\n", @@ -91,7 +138,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 5, "id": "4f740385-dbd1-450c-b831-7ae75ca8265a", "metadata": {}, "outputs": [ @@ -99,7 +146,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "DataBatch(x=[973, 22], edge_index=[2, 2080], edge_attr=[1107, 1], y=[32, 2], circuit_depth=[32, 1], observable=[32, 2, 21], backend_nodes=[160, 3], backend_edges=[64, 8], backend_edge_features=[256, 1], batch=[973], ptr=[33])\n" + "DataBatch(x=[847, 22], edge_index=[2, 1795], edge_attr=[948, 1], y=[32, 2], circuit_depth=[32, 1], observable=[32, 2, 21], backend_nodes=[160, 3], backend_edges=[64, 8], backend_edge_features=[256, 1], batch=[847], ptr=[33])\n" ] } ], @@ -126,7 +173,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 6, "id": "e0a69fb9-7eb7-4b91-ae8d-cd19e6b034c0", "metadata": {}, "outputs": [], @@ -144,10 +191,19 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 7, "id": "9c6f1883-05af-4174-b551-bd3508c32ced", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/anaconda3/envs/blackwater/lib/python3.9/site-packages/torch/optim/lr_scheduler.py:28: UserWarning: The verbose parameter is deprecated. Please use get_last_lr() to access the learning rate.\n", + " warnings.warn(\"The verbose parameter is deprecated. Please use get_last_lr() \"\n" + ] + } + ], "source": [ "import torch\n", "from torch.optim import Adam\n", @@ -173,23 +229,39 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 8, "id": "853a3599-7985-4f70-b69d-bbd6aced4737", "metadata": {}, "outputs": [ { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "23197363d407457f8541182b2157cb92", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Model training: 0%| | 0/10 [00:00= 1: \n", " train_losses.append(train_loss / len(train_loader))\n", - " val_losses.append(valid_loss / len(val_loader))\n", - "\n", - " progress.set_description(f\"{round(train_losses[-1], 5)}, {round(val_losses[-1], 5)}\")\n", - " progress.refresh()" + " val_losses.append(valid_loss / len(val_loader))" ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 9, "id": "70ebe862-6aa5-4c5f-a9d9-b61b964790e4", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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+ "image/png": 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", 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", 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", 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" ] @@ -324,7 +393,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 11, "id": "5b58843b-ea16-4983-a75a-88538952bf5e", "metadata": {}, "outputs": [], @@ -336,7 +405,7 @@ " ScikitLearnEstimatorModel,\n", " TorchGeometricEstimatorModel,\n", ")\n", - "from blackwater.data.encoders.torch import DefaultPyGEstimatorEncoder\n", + "from blackwater.data.encoders.graph_utils import DefaultPyGEstimatorEncoder\n", "\n", "\n", "circuit = transpile(random_circuit(5, 2), fake_lima)\n", @@ -352,17 +421,17 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 12, "id": "1ecbdfae-1cc0-404a-9875-13fb3104d018", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "EstimatorResult(values=array([-0.44770062]), metadata=[{'original_value': 0.0}])" + "EstimatorResult(values=array([1.59029901]), metadata=[{'original_value': 1.1102230246251565e-16}])" ] }, - "execution_count": 20, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -370,6 +439,14 @@ "source": [ "estimator.run([circuit], [observable]).result()" ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c10db099", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { @@ -388,7 +465,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.16" + "version": "3.9.19" } }, "nbformat": 4, diff --git a/requirements.txt b/requirements.txt index a04ca5c..63670de 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,7 +1,8 @@ torch>=2.0.1 -qiskit-aer>=0.11.0 -qiskit-terra>=0.23.1 +qiskit-aer>=0.11.0,<=0.13.3 +qiskit>=0.23.1,<0.45.1 qiskit-experiments>=0.4.0 Gymnasium>=0.26.3 ray>=2.4.0 scikit-learn +torch_geometric>=2.0.0 \ No newline at end of file diff --git a/tox.ini b/tox.ini index c6e3a15..e08c306 100644 --- a/tox.ini +++ b/tox.ini @@ -14,7 +14,7 @@ deps = -rrequirements.txt -rrequirements-dev.txt commands = pip check - pip install -rrequirements-graph.txt + ; pip install -rrequirements-graph.txt python -m unittest -v [testenv:lint]