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guanmingchiu pushed a commit to branch main
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The following commit(s) were added to refs/heads/main by this push:
new 64c437b57 Re-enable ruff on ipynb (#1149)
64c437b57 is described below
commit 64c437b572acef4846aa075d038f6170e919f3b3
Author: Tim Hsiung <[email protected]>
AuthorDate: Sun Mar 8 23:30:08 2026 +0800
Re-enable ruff on ipynb (#1149)
---
examples/qdp/simple.ipynb | 311 ++++++++++---------
pyproject.toml | 1 -
.../benchmark/notebooks/mahout_benchmark.ipynb | 333 +++++++++++----------
3 files changed, 330 insertions(+), 315 deletions(-)
diff --git a/examples/qdp/simple.ipynb b/examples/qdp/simple.ipynb
index fa9146d42..fb8b98c52 100644
--- a/examples/qdp/simple.ipynb
+++ b/examples/qdp/simple.ipynb
@@ -1,154 +1,169 @@
{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/";
- },
- "collapsed": true,
- "id": "y5xLkFQ4sLOV",
- "outputId": "b3b21a29-a232-4cf0-ef94-4b2be060f48b"
- },
- "outputs": [],
- "source": [
- "%pip install qumat[qdp]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/";
- },
- "id": "ZvmpEUJFscx-",
- "outputId": "0c70d8eb-c7b4-4a87-914f-95c01d3fb26c"
- },
- "outputs": [],
- "source": [
- "\"\"\"\n",
- "QDP + QML: Full GPU Pipeline (float64)\n",
- "CPU → GPU (QDP batch encode) → GPU (real projection) → GPU (QML
training)\n",
- "\"\"\"\n",
- "\n",
- "import torch\n",
- "import torch.nn as nn\n",
- "import torch.optim as optim\n",
- "from qumat.qdp import QdpEngine\n",
- "\n",
- "# ─────────────────────────────────────────────\n",
- "# 1. Setup\n",
- "# ─────────────────────────────────────────────\n",
- "DEVICE_ID = 0\n",
- "TORCH_DEVICE = torch.device(\"cuda\", DEVICE_ID)\n",
- "NUM_QUBITS = 2\n",
- "EPOCHS = 60\n",
- "LR = 0.01\n",
- "\n",
- "engine = QdpEngine(DEVICE_ID)\n",
- "\n",
- "# ─────────────────────────────────────────────\n",
- "# 2. Raw Data on CPU — float64\n",
- "# ─────────────────────────────────────────────\n",
- "raw = torch.tensor([\n",
- " [0.5, 0.5, 0.5, 0.5],\n",
- " [0.7, 0.1, 0.5, 0.3],\n",
- " [0.1, 0.8, 0.4, 0.4],\n",
- " [0.6, 0.2, 0.6, 0.4],\n",
- "], dtype=torch.float64) # ← float64\n",
- "\n",
- "labels = torch.tensor([0, 1, 0, 1], dtype=torch.float64,
device=TORCH_DEVICE)\n",
- "\n",
- "# ─────────────────────────────────────────────\n",
- "# 3. CPU → GPU: QDP Batch Encode\n",
- "# ─────────────────────────────────────────────\n",
- "print(\"CPU → GPU: Batch encoding with QDP...\")\n",
- "cuda_batch = raw.cuda()\n",
- "\n",
- "qtensor = engine.encode(cuda_batch, num_qubits=NUM_QUBITS,
encoding_method=\"amplitude\")\n",
- "\n",
- "# DLPack → complex128 CUDA tensor (two float64s per element)\n",
- "X_complex = torch.from_dlpack(qtensor)\n",
- "print(f\"Raw encoded: shape={X_complex.shape},
dtype={X_complex.dtype}, device={X_complex.device}\")\n",
- "\n",
- "# Concatenate real + imag → float64 [N, 8], stays on GPU\n",
- "X_quantum = torch.cat([X_complex.real, X_complex.imag],
dim=-1).double()\n",
- "print(f\"Real features: shape={X_quantum.shape},
dtype={X_quantum.dtype}, device={X_quantum.device}\")\n",
- "\n",
- "# ─────────────────────────────────────────────\n",
- "# 4. QML Model on GPU — double precision\n",
- "# ─────────────────────────────────────────────\n",
- "class VariationalLayer(nn.Module):\n",
- " def __init__(self, dim):\n",
- " super().__init__()\n",
- " self.theta = nn.Parameter(torch.randn(dim,
dtype=torch.float64))\n",
- "\n",
- " def forward(self, x):\n",
- " return x * torch.cos(self.theta) + torch.roll(x, 1, dims=-1)
* torch.sin(self.theta)\n",
- "\n",
- "class QMLClassifier(nn.Module):\n",
- " def __init__(self, num_qubits):\n",
- " super().__init__()\n",
- " dim = 2 * (2 ** num_qubits) # real + imag\n",
- " self.layer1 = VariationalLayer(dim)\n",
- " self.layer2 = VariationalLayer(dim)\n",
- " self.readout = nn.Linear(dim, 1, dtype=torch.float64)\n",
- "\n",
- " def forward(self, x):\n",
- " x = torch.tanh(self.layer1(x))\n",
- " x = self.layer2(x)\n",
- " return torch.sigmoid(self.readout(x)).squeeze(-1)\n",
- "\n",
- "model = QMLClassifier(NUM_QUBITS).to(TORCH_DEVICE)\n",
- "optimizer = optim.Adam(model.parameters(), lr=LR)\n",
- "loss_fn = nn.BCELoss()\n",
- "\n",
- "# ─────────────────────────────────────────────\n",
- "# 5. GPU Training\n",
- "# ─────────────────────────────────────────────\n",
- "print(\"\\nGPU → Training QML model...\")\n",
- "for epoch in range(1, EPOCHS + 1):\n",
- " model.train()\n",
- " optimizer.zero_grad()\n",
- " preds = model(X_quantum)\n",
- " loss = loss_fn(preds, labels)\n",
- " loss.backward()\n",
- " optimizer.step()\n",
- "\n",
- " if epoch % 10 == 0:\n",
- " with torch.no_grad():\n",
- " acc = ((preds > 0.5).double() ==
labels).double().mean().item()\n",
- " print(f\"Epoch {epoch:3d} | Loss: {loss.item():.6f} |
Accuracy: {acc:.2f}\")\n",
- "\n",
- "# ─────────────────────────────────────────────\n",
- "# 6. Inference\n",
- "# ─────────────────────────────────────────────\n",
- "model.eval()\n",
- "with torch.no_grad():\n",
- " predicted = (model(X_quantum) > 0.5).int()\n",
- "\n",
- "print(\"\\n─── Results ───\")\n",
- "for i, (pred, true) in enumerate(zip(predicted.cpu().tolist(),
labels.int().cpu().tolist())):\n",
- " print(f\"Sample {i}: Predicted={pred} True={true} {'✓' if pred
== true else '✗'}\")"
- ]
- }
- ],
- "metadata": {
- "accelerator": "GPU",
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
"colab": {
- "gpuType": "T4",
- "provenance": []
+ "base_uri": "https://localhost:8080/";
},
- "kernelspec": {
- "display_name": "Python 3",
- "name": "python3"
+ "collapsed": true,
+ "id": "y5xLkFQ4sLOV",
+ "outputId": "b3b21a29-a232-4cf0-ef94-4b2be060f48b"
+ },
+ "outputs": [],
+ "source": [
+ "%pip install qumat[qdp]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/";
},
- "language_info": {
- "name": "python"
- }
+ "id": "ZvmpEUJFscx-",
+ "outputId": "0c70d8eb-c7b4-4a87-914f-95c01d3fb26c"
+ },
+ "outputs": [],
+ "source": [
+ "\"\"\"\n",
+ "QDP + QML: Full GPU Pipeline (float64)\n",
+ "CPU → GPU (QDP batch encode) → GPU (real projection) → GPU (QML
training)\n",
+ "\"\"\"\n",
+ "\n",
+ "import torch\n",
+ "import torch.nn as nn\n",
+ "import torch.optim as optim\n",
+ "\n",
+ "from qumat.qdp import QdpEngine\n",
+ "\n",
+ "# ─────────────────────────────────────────────\n",
+ "# 1. Setup\n",
+ "# ─────────────────────────────────────────────\n",
+ "DEVICE_ID = 0\n",
+ "TORCH_DEVICE = torch.device(\"cuda\", DEVICE_ID)\n",
+ "NUM_QUBITS = 2\n",
+ "EPOCHS = 60\n",
+ "LR = 0.01\n",
+ "\n",
+ "engine = QdpEngine(DEVICE_ID)\n",
+ "\n",
+ "# ─────────────────────────────────────────────\n",
+ "# 2. Raw Data on CPU — float64\n",
+ "# ─────────────────────────────────────────────\n",
+ "raw = torch.tensor(\n",
+ " [\n",
+ " [0.5, 0.5, 0.5, 0.5],\n",
+ " [0.7, 0.1, 0.5, 0.3],\n",
+ " [0.1, 0.8, 0.4, 0.4],\n",
+ " [0.6, 0.2, 0.6, 0.4],\n",
+ " ],\n",
+ " dtype=torch.float64,\n",
+ ") # ← float64\n",
+ "\n",
+ "labels = torch.tensor([0, 1, 0, 1], dtype=torch.float64,
device=TORCH_DEVICE)\n",
+ "\n",
+ "# ─────────────────────────────────────────────\n",
+ "# 3. CPU → GPU: QDP Batch Encode\n",
+ "# ─────────────────────────────────────────────\n",
+ "print(\"CPU → GPU: Batch encoding with QDP...\")\n",
+ "cuda_batch = raw.cuda()\n",
+ "\n",
+ "qtensor = engine.encode(cuda_batch, num_qubits=NUM_QUBITS,
encoding_method=\"amplitude\")\n",
+ "\n",
+ "# DLPack → complex128 CUDA tensor (two float64s per element)\n",
+ "X_complex = torch.from_dlpack(qtensor)\n",
+ "print(\n",
+ " f\"Raw encoded: shape={X_complex.shape}, dtype={X_complex.dtype},
device={X_complex.device}\"\n",
+ ")\n",
+ "\n",
+ "# Concatenate real + imag → float64 [N, 8], stays on GPU\n",
+ "X_quantum = torch.cat([X_complex.real, X_complex.imag],
dim=-1).double()\n",
+ "print(\n",
+ " f\"Real features: shape={X_quantum.shape}, dtype={X_quantum.dtype},
device={X_quantum.device}\"\n",
+ ")\n",
+ "\n",
+ "\n",
+ "# ─────────────────────────────────────────────\n",
+ "# 4. QML Model on GPU — double precision\n",
+ "# ─────────────────────────────────────────────\n",
+ "class VariationalLayer(nn.Module):\n",
+ " def __init__(self, dim):\n",
+ " super().__init__()\n",
+ " self.theta = nn.Parameter(torch.randn(dim,
dtype=torch.float64))\n",
+ "\n",
+ " def forward(self, x):\n",
+ " return x * torch.cos(self.theta) + torch.roll(x, 1, dims=-1) *
torch.sin(\n",
+ " self.theta\n",
+ " )\n",
+ "\n",
+ "\n",
+ "class QMLClassifier(nn.Module):\n",
+ " def __init__(self, num_qubits):\n",
+ " super().__init__()\n",
+ " dim = 2 * (2**num_qubits) # real + imag\n",
+ " self.layer1 = VariationalLayer(dim)\n",
+ " self.layer2 = VariationalLayer(dim)\n",
+ " self.readout = nn.Linear(dim, 1, dtype=torch.float64)\n",
+ "\n",
+ " def forward(self, x):\n",
+ " x = torch.tanh(self.layer1(x))\n",
+ " x = self.layer2(x)\n",
+ " return torch.sigmoid(self.readout(x)).squeeze(-1)\n",
+ "\n",
+ "\n",
+ "model = QMLClassifier(NUM_QUBITS).to(TORCH_DEVICE)\n",
+ "optimizer = optim.Adam(model.parameters(), lr=LR)\n",
+ "loss_fn = nn.BCELoss()\n",
+ "\n",
+ "# ─────────────────────────────────────────────\n",
+ "# 5. GPU Training\n",
+ "# ─────────────────────────────────────────────\n",
+ "print(\"\\nGPU → Training QML model...\")\n",
+ "for epoch in range(1, EPOCHS + 1):\n",
+ " model.train()\n",
+ " optimizer.zero_grad()\n",
+ " preds = model(X_quantum)\n",
+ " loss = loss_fn(preds, labels)\n",
+ " loss.backward()\n",
+ " optimizer.step()\n",
+ "\n",
+ " if epoch % 10 == 0:\n",
+ " with torch.no_grad():\n",
+ " acc = ((preds > 0.5).double() ==
labels).double().mean().item()\n",
+ " print(f\"Epoch {epoch:3d} | Loss: {loss.item():.6f} | Accuracy:
{acc:.2f}\")\n",
+ "\n",
+ "# ─────────────────────────────────────────────\n",
+ "# 6. Inference\n",
+ "# ─────────────────────────────────────────────\n",
+ "model.eval()\n",
+ "with torch.no_grad():\n",
+ " predicted = (model(X_quantum) > 0.5).int()\n",
+ "\n",
+ "print(\"\\n─── Results ───\")\n",
+ "for i, (pred, true) in enumerate(\n",
+ " zip(predicted.cpu().tolist(), labels.int().cpu().tolist())\n",
+ "):\n",
+ " print(f\"Sample {i}: Predicted={pred} True={true} {'✓' if pred ==
true else '✗'}\")"
+ ]
+ }
+ ],
+ "metadata": {
+ "accelerator": "GPU",
+ "colab": {
+ "gpuType": "T4",
+ "provenance": []
+ },
+ "kernelspec": {
+ "display_name": "Python 3",
+ "name": "python3"
},
- "nbformat": 4,
- "nbformat_minor": 0
+ "language_info": {
+ "name": "python"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
}
diff --git a/pyproject.toml b/pyproject.toml
index 8e6dd3c81..6a8ccc0f9 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -98,7 +98,6 @@ exclude = [
allowed-unresolved-imports = ["_qdp", "_qdp.*", "api", "api.*"]
[tool.ruff]
-extend-exclude = ["**/*.ipynb"]
target-version = "py310"
[tool.ruff.lint]
diff --git a/qdp/qdp-python/benchmark/notebooks/mahout_benchmark.ipynb
b/qdp/qdp-python/benchmark/notebooks/mahout_benchmark.ipynb
index b1583a500..35091ff8d 100644
--- a/qdp/qdp-python/benchmark/notebooks/mahout_benchmark.ipynb
+++ b/qdp/qdp-python/benchmark/notebooks/mahout_benchmark.ipynb
@@ -1,177 +1,178 @@
{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "pjstUzDHQHad"
- },
- "source": [
- "## Install environments"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/";
- },
- "collapsed": true,
- "id": "-hkLubLFXs_8",
- "outputId": "7bdf179b-71ed-455b-ef8e-17969acf1db5"
- },
- "outputs": [],
- "source": [
- "!sudo apt-get update -y > /dev/null\n",
- "!sudo apt-get install python3.11 python3.11-dev python3.11-distutils
libpython3.11-dev > /dev/null\n",
- "!sudo apt-get install python3.11-venv binfmt-support > /dev/null\n",
- "!sudo apt-get install python3-pip > /dev/null\n",
- "!python3 -m pip install --upgrade pip > /dev/null\n",
- "!python3 -m pip install ipykernel"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/";
- },
- "collapsed": true,
- "id": "_HEpQ4F3C4gV",
- "outputId": "b45d4dac-f093-4370-f9e9-8b9adc6972c7"
- },
- "outputs": [],
- "source": [
- "# 1. Install Rust Toolchain\n",
- "!curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh -s --
-y\n",
- "import os\n",
- "os.environ['PATH'] += \":/root/.cargo/bin\"\n",
- "\n",
- "# 2. Verify Installation\n",
- "!rustc --version\n",
- "!cargo --version"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/";
- },
- "collapsed": true,
- "id": "ljkluVL5ES4S",
- "outputId": "8c719c4d-cdcc-4474-f4cf-e1b0b41f63bc"
- },
- "outputs": [],
- "source": [
- "!curl -LsSf https://astral.sh/uv/install.sh | sh"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/";
- },
- "id": "9cgMNKOoEgYm",
- "outputId": "c2c1c8d4-ac57-415a-d4ae-94dfb422b619"
- },
- "outputs": [],
- "source": [
- "!nvcc --version"
- ]
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "pjstUzDHQHad"
+ },
+ "source": [
+ "## Install environments"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/";
},
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "rOja7HAaQL1h"
- },
- "source": [
- "## Install Mahout"
- ]
+ "collapsed": true,
+ "id": "-hkLubLFXs_8",
+ "outputId": "7bdf179b-71ed-455b-ef8e-17969acf1db5"
+ },
+ "outputs": [],
+ "source": [
+ "!sudo apt-get update -y > /dev/null\n",
+ "!sudo apt-get install python3.11 python3.11-dev python3.11-distutils
libpython3.11-dev > /dev/null\n",
+ "!sudo apt-get install python3.11-venv binfmt-support > /dev/null\n",
+ "!sudo apt-get install python3-pip > /dev/null\n",
+ "!python3 -m pip install --upgrade pip > /dev/null\n",
+ "!python3 -m pip install ipykernel"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/";
},
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/";
- },
- "collapsed": true,
- "id": "u7Skxs7lDBlq",
- "outputId": "9b594df0-a8b2-4c6c-8c88-2a76f16c9c1d"
- },
- "outputs": [],
- "source": [
- "# 1. Clone the repository\n",
- "!git clone https://github.com/apache/mahout.git\n";,
- "\n",
- "# 2. Install Python Dependencies\n",
- "# We use the requirements file provided in the benchmark folder\n",
- "%cd /content/mahout/qdp/qdp-python\n",
- "!uv venv -p python3.11\n",
- "\n",
- "!uv sync --group benchmark"
- ]
+ "collapsed": true,
+ "id": "_HEpQ4F3C4gV",
+ "outputId": "b45d4dac-f093-4370-f9e9-8b9adc6972c7"
+ },
+ "outputs": [],
+ "source": [
+ "# 1. Install Rust Toolchain\n",
+ "!curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh -s --
-y\n",
+ "import os\n",
+ "\n",
+ "os.environ[\"PATH\"] += \":/root/.cargo/bin\"\n",
+ "\n",
+ "# 2. Verify Installation\n",
+ "!rustc --version\n",
+ "!cargo --version"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/";
},
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/";
- },
- "collapsed": true,
- "id": "qqmfUHGsGm8m",
- "outputId": "10b2598d-0d0c-47d0-fda8-b5e825c64e77"
- },
- "outputs": [],
- "source": [
- "!uv pip install matplotlib-inline --python .venv/bin/python"
- ]
+ "collapsed": true,
+ "id": "ljkluVL5ES4S",
+ "outputId": "8c719c4d-cdcc-4474-f4cf-e1b0b41f63bc"
+ },
+ "outputs": [],
+ "source": [
+ "!curl -LsSf https://astral.sh/uv/install.sh | sh"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/";
},
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "hj7sU3yJQeMj"
- },
- "source": [
- "## Run Benchmarks"
- ]
+ "id": "9cgMNKOoEgYm",
+ "outputId": "c2c1c8d4-ac57-415a-d4ae-94dfb422b619"
+ },
+ "outputs": [],
+ "source": [
+ "!nvcc --version"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "rOja7HAaQL1h"
+ },
+ "source": [
+ "## Install Mahout"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/";
},
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/";
- },
- "id": "iuP5BdI3E-oR",
- "outputId": "632019c3-da2e-4184-87ca-412b86555232"
- },
- "outputs": [],
- "source": [
- "!./.venv/bin/python
/content/mahout/qdp/qdp-python/benchmark/benchmark_e2e.py --frameworks all
--qubits 18 --samples 500"
- ]
- }
- ],
- "metadata": {
- "accelerator": "GPU",
+ "collapsed": true,
+ "id": "u7Skxs7lDBlq",
+ "outputId": "9b594df0-a8b2-4c6c-8c88-2a76f16c9c1d"
+ },
+ "outputs": [],
+ "source": [
+ "# 1. Clone the repository\n",
+ "!git clone https://github.com/apache/mahout.git\n";,
+ "\n",
+ "# 2. Install Python Dependencies\n",
+ "# We use the requirements file provided in the benchmark folder\n",
+ "%cd /content/mahout/qdp/qdp-python\n",
+ "!uv venv -p python3.11\n",
+ "\n",
+ "!uv sync --group benchmark"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
"colab": {
- "gpuType": "T4",
- "provenance": []
+ "base_uri": "https://localhost:8080/";
},
- "kernelspec": {
- "display_name": "Python 3",
- "name": "python3"
+ "collapsed": true,
+ "id": "qqmfUHGsGm8m",
+ "outputId": "10b2598d-0d0c-47d0-fda8-b5e825c64e77"
+ },
+ "outputs": [],
+ "source": [
+ "!uv pip install matplotlib-inline --python .venv/bin/python"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "hj7sU3yJQeMj"
+ },
+ "source": [
+ "## Run Benchmarks"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/";
},
- "language_info": {
- "name": "python"
- }
+ "id": "iuP5BdI3E-oR",
+ "outputId": "632019c3-da2e-4184-87ca-412b86555232"
+ },
+ "outputs": [],
+ "source": [
+ "!./.venv/bin/python
/content/mahout/qdp/qdp-python/benchmark/benchmark_e2e.py --frameworks all
--qubits 18 --samples 500"
+ ]
+ }
+ ],
+ "metadata": {
+ "accelerator": "GPU",
+ "colab": {
+ "gpuType": "T4",
+ "provenance": []
+ },
+ "kernelspec": {
+ "display_name": "Python 3",
+ "name": "python3"
},
- "nbformat": 4,
- "nbformat_minor": 0
+ "language_info": {
+ "name": "python"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
}
