From 416012c77ec35c3ed1638c3a68a976736b6be5f2 Mon Sep 17 00:00:00 2001 From: Sankalp Sanand Date: Fri, 10 May 2024 21:21:25 +0530 Subject: [PATCH 01/18] uploading notebook --- PennylaneTutorial.ipynb | 521 ++++++++++++++++++++++++++++++++++++++++ 1 file changed, 521 insertions(+) create mode 100644 PennylaneTutorial.ipynb diff --git a/PennylaneTutorial.ipynb b/PennylaneTutorial.ipynb new file mode 100644 index 0000000000..6ae77bb4cd --- /dev/null +++ b/PennylaneTutorial.ipynb @@ -0,0 +1,521 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Classification with GPU-quantum embedding kernels on Covalent Cloud" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Quantum support-vector machines (QSVMs) are essentially [traditional SVMs](https://en.wikipedia.org/wiki/Support_vector_machine) that rely on [embedding kernels](https://en.wikipedia.org/wiki/Kernel_method#Mathematics:_the_kernel_trick) evaluated on a quantum computer—a.k.a. [*quantum embedding kernel*](https://pennylane.ai/qml/demos/tutorial_kernels_module/#training-and-evaluating-quantum-kernels). These kernels provide a unique (and perhaps classically-intractable) means of measuring pairwise similarity.\n", + "\n", + "Using GPUs to simulate quantum computers is worthwhile when qubit capacity and/or fidelity requirements are not met by the available quantum hardware. While QSVMs are relatively tolerant to noise (an important reason for their current popularity), evaluating kernels on *real* quantum hardware is not always practical nor necessary.\n", + "\n", + "In this tutorial, we’ll demonstrate how to run GPU-accelerated quantum circuit simulations on Covalent Cloud." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let’s start by importing the required packages. " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import covalent as ct\n", + "import covalent_cloud as cc\n", + "import matplotlib.pyplot as plt\n", + "import pennylane as qml\n", + "from matplotlib.colors import ListedColormap\n", + "from pennylane import numpy as np\n", + "from sklearn.datasets import make_blobs\n", + "from sklearn.inspection import DecisionBoundaryDisplay\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.svm import SVC\n", + "\n", + "# cc.save_api_key(\"YOUR_API_KEY\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Covalent Cloud allows us to create [re-usable execution environments](https://docs.covalent.xyz/docs/cloud/guides/cloud_custom_environments/), as shown below. This environment represents a typical setup for running Pennylane on NVIDIA GPUs." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Waiting for environment pennylane-gpu to be ready............................................................................................................................................................................\n", + "Name: pennylane-gpu\n", + "Status: READY\n", + "Estimated Time: 1002 seconds\n", + "Notes:\n", + "\tpip was added to the dependencies.\n", + "\tPython version 3.10 was added to the dependencies.\n", + "Environment file contains:\n", + "==========================\n", + "channels:\n", + "- conda-forge\n", + "dependencies:\n", + "- python=3.10\n", + "- pip\n", + "- cudatoolkit>=11.8\n", + "- pip:\n", + " - cuquantum==23.10.0\n", + " - matplotlib==3.8.2\n", + " - Pennylane==0.34.0\n", + " - PennyLane-Lightning[GPU]==0.34.0\n", + " - scikit-learn==1.3.1\n", + " - torch==2.1.2\n", + " - covalent-cloud\n", + "name: pennylane-gpu\n", + "\n" + ] + } + ], + "source": [ + "cc.create_env(\n", + " name=\"pennylane-gpu\", # identifier for referring to this environment\n", + " conda={\n", + " \"channels\": [\"conda-forge\"],\n", + " \"dependencies\": [\"cudatoolkit>=11.8\"],\n", + " }, pip=[\n", + " \"cuquantum==23.10.0\",\n", + " \"matplotlib==3.8.2\",\n", + " \"Pennylane==0.34.0\",\n", + " \"PennyLane-Lightning[GPU]==0.34.0\",\n", + " \"scikit-learn==1.3.1\",\n", + " \"torch==2.1.2\",\n", + " ],\n", + " wait=True\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we'll define our resource specifications by creating some [executors](https://docs.covalent.xyz/docs/user-documentation/api-reference/executors/cloud_executor) for this workflow. Both executors will run tasks in our new environment, named `”pennylane-gpu”`." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "cpu_executor = cc.CloudExecutor( # for lightweight non-quantum tasks\n", + " env=\"pennylane-gpu\",\n", + " num_cpus=2,\n", + " memory=\"2GB\",\n", + ")\n", + "gpu_executor = cc.CloudExecutor( # for GPU-powered circuit simulations\n", + " env=\"pennylane-gpu\",\n", + " num_cpus=4,\n", + " memory=\"12GB\",\n", + " num_gpus=1,\n", + " gpu_type=\"v100\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "On to the algorithm!\n", + "\n", + "Here's a function returns a simple quantum kernel based on Pennylane's [IQP Embedding](https://docs.pennylane.ai/en/stable/code/api/pennylane.IQPEmbedding.html) template. We'll use it as-is inside our workflow." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "QML_DEVICE = \"lightning.qubit\"\n", + "\n", + "def get_kernel_circuit(n_wires):\n", + "\n", + " @qml.qnode(qml.device(QML_DEVICE, wires=n_wires, shots=None))\n", + " def circuit(x1, x2):\n", + " qml.IQPEmbedding(x1, wires=range(n_wires), n_repeats=4)\n", + " qml.adjoint(qml.IQPEmbedding)(x2, wires=range(n_wires), n_repeats=4)\n", + " return qml.probs(wires=range(n_wires))\n", + "\n", + " return lambda x1, x2: circuit(x1, x2)[0] # |0..0> state probability" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, each function destined for remote execution is decorated with `@ct.electron`, with an [executor](https://docs.covalent.xyz/docs/user-documentation/api-reference/executors/cloud_executor) specified therein. Only tasks that evaluate the simulated quantum kernel should require `gpu_executor`. For example, we don't need GPUs to generate our input data:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "@ct.electron(executor=cpu_executor) # lightweight non-quantum task\n", + "def get_split_data(n_samples=18, test_size=0.2):\n", + " centers = [(1, 1), (1, 2), (1, 3), (2, 1), (2, 2), (2, 3), (3, 1), (3, 2), (3, 3)]\n", + " X, y = make_blobs(n_samples, centers=centers, cluster_std=0.25, shuffle=False)\n", + " # rescale labels to be -1, 1\n", + " mapping = {0: -1, 1: 1, 2: -1, 3: 1, 4: -1, 5: 1, 6: -1, 7: 1, 8: -1}\n", + " y = np.array([mapping[i] for i in y])\n", + " X = X.astype(np.float32)\n", + " y = y.astype(int)\n", + "\n", + " # X_train, X_test, y_train, y_test\n", + " return train_test_split(X, y, test_size=test_size, random_state=3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Classifying with the SVM, on the other hand, requires $O(n^2)$ kernel evaluations, where $n$ is the dataset size. Accordingly, we'll use GPUs (i.e. `gpu_executor`) to speed up this process." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "DISP_SETTINGS = {\"grid_resolution\": 50, \"response_method\": \"predict\", \"alpha\": 0.5, \"cmap\": plt.cm.RdBu}\n", + "\n", + "@ct.electron(executor=gpu_executor)\n", + "def classify_with_qsvm(Xtr, Xte, ytr, yte):\n", + " kernel = get_kernel_circuit(n_wires=Xtr.shape[1])\n", + "\n", + " kernel_matrix_fn = lambda X, Z: qml.kernels.kernel_matrix(X, Z, kernel)\n", + " svc = SVC(kernel=kernel_matrix_fn).fit(Xtr, ytr)\n", + "\n", + " # train/test accuracy\n", + " accuracy_tr = svc.score(Xtr, ytr)\n", + " accuracy_te = svc.score(Xte, yte)\n", + "\n", + " # decision boundary\n", + " cm_bright = ListedColormap([\"#FF0000\", \"#0000FF\"])\n", + " disp = DecisionBoundaryDisplay.from_estimator(svc, Xte, **DISP_SETTINGS)\n", + " disp.ax_.scatter(Xtr[:, 0], Xtr[:, 1], c=ytr, cmap=cm_bright)\n", + " disp.ax_.scatter(Xte[:, 0], Xte[:, 1], c=yte, cmap=cm_bright, marker=\"$\\u25EF$\")\n", + "\n", + " return accuracy_tr, accuracy_te, disp" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Putting it all together, we can define a QSVM training and testing workflow. This special function gets decorated with `@ct.lattice`." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "@ct.lattice(workflow_executor=cpu_executor, executor=cpu_executor)\n", + "def run_qsvm(n_samples, test_size):\n", + " Xtr, Xte, ytr, yte = get_split_data(n_samples, test_size)\n", + " return classify_with_qsvm(Xtr, Xte, ytr, yte)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, to dispatch `run_qsvm` to Covalent Cloud, we call it after wrapping with `ct.dispatch`, as usual." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
/Users/neptune/anaconda3/envs/qml/lib/python3.10/site-packages/rich/live.py:231: UserWarning: install \"ipywidgets\" \n",
+       "for Jupyter support\n",
+       "  warnings.warn('install \"ipywidgets\" for Jupyter support')\n",
+       "
\n" + ], + "text/plain": [ + "/Users/neptune/anaconda3/envs/qml/lib/python3.10/site-packages/rich/live.py:231: UserWarning: install \"ipywidgets\" \n", + "for Jupyter support\n", + " warnings.warn('install \"ipywidgets\" for Jupyter support')\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
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+       "
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+      ],
+      "text/plain": [
+       "\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Dispatch ID: 0b5d3a08-fe9c-4dc2-910b-0be6eb925663\n"
+     ]
+    }
+   ],
+   "source": [
+    "dispatch_id = cc.dispatch(run_qsvm)(n_samples=64, test_size=0.2)\n",
+    "print(\"Dispatch ID:\", dispatch_id)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's what we get when we query and display the results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/neptune/anaconda3/envs/qml/lib/python3.10/site-packages/covalent/_workflow/transportable_object.py:205: UserWarning: This figure was saved with matplotlib version 3.8.2 and is unlikely to function correctly.\n",
+      "  return cloudpickle.loads(base64.b64decode(self._object.encode(\"utf-8\")))\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Train accuracy: 64.7%\n",
+      "Test accuracy: 76.9%\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       ""
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "
"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "result = cc.get_result(dispatch_id, wait=True)\n",
+    "result.result.load()\n",
+    "\n",
+    "train_acc, test_acc, decision_boundary_figure = result.result.value\n",
+    "print(f\"Train accuracy: {train_acc * 100:.1f}%\")\n",
+    "print(f\"Test accuracy: {test_acc * 100:.1f}%\")\n",
+    "\n",
+    "decision_boundary_figure"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Conclusion\n",
+    "\n",
+    "In this tutorial, we demonstrated how to run quantum circuit simulations on GPUs via Covalent Cloud. We used Pennylane to define a simple quantum kernel, and then trained and tested a QSVM on a 2-dimensional dataset. To make the most of this tutorial, try experimenting with different datasets/kernels or increasing the dataset dimension, to gain a greater advantage from GPU acceleration.\n",
+    "\n",
+    "The cost of running this workflow is approximately $0.27. The full code is available below."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Full Code"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "```python\n",
+    "import covalent as ct\n",
+    "import covalent_cloud as cc\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pennylane as qml\n",
+    "from matplotlib.colors import ListedColormap\n",
+    "from pennylane import numpy as np\n",
+    "from sklearn.datasets import make_blobs\n",
+    "from sklearn.inspection import DecisionBoundaryDisplay\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.svm import SVC\n",
+    "\n",
+    "cc.save_api_key(\"API_KEY\")\n",
+    "\n",
+    "cc.create_env(\n",
+    "    name=\"pennylane-gpu\",  # identifier for referring to this environment\n",
+    "    conda={\n",
+    "        \"channels\": [\"conda-forge\"],\n",
+    "        \"dependencies\": [\"cudatoolkit>=11.8\"],\n",
+    "    }, pip=[\n",
+    "        \"cuquantum==23.10.0\",\n",
+    "        \"matplotlib==3.8.2\",\n",
+    "        \"Pennylane==0.34.0\",\n",
+    "        \"PennyLane-Lightning[GPU]==0.34.0\",\n",
+    "        \"scikit-learn==1.3.1\",\n",
+    "        \"torch==2.1.2\",\n",
+    "    ],\n",
+    "    wait=True\n",
+    ")\n",
+    "\n",
+    "cpu_executor = cc.CloudExecutor(  # for lightweight non-quantum tasks\n",
+    "    env=\"pennylane-gpu\",\n",
+    "    num_cpus=2,\n",
+    "    memory=\"2GB\",\n",
+    ")\n",
+    "gpu_executor = cc.CloudExecutor(  # for GPU-powered circuit simulations\n",
+    "    env=\"pennylane-gpu\",\n",
+    "    num_cpus=4,\n",
+    "    memory=\"12GB\",\n",
+    "    num_gpus=1,\n",
+    "    gpu_type=\"v100\"\n",
+    ")\n",
+    "\n",
+    "QML_DEVICE = \"lightning.gpu\"\n",
+    "\n",
+    "def get_kernel_circuit(n_wires):\n",
+    "\n",
+    "    @qml.qnode(qml.device(QML_DEVICE, wires=n_wires, shots=None))\n",
+    "    def circuit(x1, x2):\n",
+    "        qml.IQPEmbedding(x1, wires=range(n_wires), n_repeats=4)\n",
+    "        qml.adjoint(qml.IQPEmbedding)(x2, wires=range(n_wires), n_repeats=4)\n",
+    "        return qml.probs(wires=range(n_wires))\n",
+    "\n",
+    "    return lambda x1, x2: circuit(x1, x2)[0]  # |0..0> state probability\n",
+    "\n",
+    "@ct.electron(executor=cpu_executor)  # lightweight non-quantum task\n",
+    "def get_split_data(n_samples=18, test_size=0.2):\n",
+    "    centers = [(1, 1), (1, 2), (1, 3), (2, 1), (2, 2), (2, 3), (3, 1), (3, 2), (3, 3)]\n",
+    "    X, y = make_blobs(n_samples, centers=centers, cluster_std=0.25, shuffle=False)\n",
+    "    # rescale labels to be -1, 1\n",
+    "    mapping = {0: -1, 1: 1, 2: -1, 3: 1, 4: -1, 5: 1, 6: -1, 7: 1, 8: -1}\n",
+    "    y = np.array([mapping[i] for i in y])\n",
+    "    X = X.astype(np.float32)\n",
+    "    y = y.astype(int)\n",
+    "\n",
+    "    # X_train, X_test, y_train, y_test\n",
+    "    return train_test_split(X, y, test_size=test_size, random_state=3)\n",
+    "\n",
+    "DISP_SETTINGS = {\"grid_resolution\": 50, \"response_method\": \"predict\", \"alpha\": 0.5, \"cmap\": plt.cm.RdBu}\n",
+    "\n",
+    "@ct.electron(executor=gpu_executor)\n",
+    "def classify_with_qsvm(Xtr, Xte, ytr, yte):\n",
+    "    kernel = get_kernel_circuit(n_wires=Xtr.shape[1])\n",
+    "\n",
+    "    kernel_matrix_fn = lambda X, Z: qml.kernels.kernel_matrix(X, Z, kernel)\n",
+    "    svc = SVC(kernel=kernel_matrix_fn).fit(Xtr, ytr)\n",
+    "\n",
+    "    # train/test accuracy\n",
+    "    accuracy_tr = svc.score(Xtr, ytr)\n",
+    "    accuracy_te = svc.score(Xte, yte)\n",
+    "\n",
+    "    # decision boundary\n",
+    "    cm_bright = ListedColormap([\"#FF0000\", \"#0000FF\"])\n",
+    "    disp = DecisionBoundaryDisplay.from_estimator(svc, Xte, **DISP_SETTINGS)\n",
+    "    disp.ax_.scatter(Xtr[:, 0], Xtr[:, 1], c=ytr, cmap=cm_bright)\n",
+    "    disp.ax_.scatter(Xte[:, 0], Xte[:, 1], c=yte, cmap=cm_bright, marker=\"$\\u25EF$\")\n",
+    "\n",
+    "    return accuracy_tr, accuracy_te, disp\n",
+    "\n",
+    "@ct.lattice(workflow_executor=cpu_executor, executor=cpu_executor)\n",
+    "def run_qsvm(n_samples, test_size):\n",
+    "    Xtr, Xte, ytr, yte = get_split_data(n_samples, test_size)\n",
+    "    return classify_with_qsvm(Xtr, Xte, ytr, yte)\n",
+    "\n",
+    "dispatch_id = cc.dispatch(run_qsvm)(n_samples=64, test_size=0.2)\n",
+    "print(\"Dispatch ID:\", dispatch_id)\n",
+    "\n",
+    "result = cc.get_result(dispatch_id, wait=True)\n",
+    "result.result.load()\n",
+    "\n",
+    "train_acc, test_acc, decision_boundary_figure = result.result.value\n",
+    "print(f\"Train accuracy: {train_acc * 100:.1f}%\")\n",
+    "print(f\"Test accuracy: {test_acc * 100:.1f}%\")\n",
+    "```"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.14"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

From b6b1038a4e8d639d974ff8b5f7862d9f36852871 Mon Sep 17 00:00:00 2001
From: Sankalp Sanand 
Date: Mon, 13 May 2024 17:30:39 +0530
Subject: [PATCH 02/18] renamed and improved slightly

---
 ...Tutorial.ipynb => covalent_cloud_gpu.ipynb | 48 -------------------
 1 file changed, 48 deletions(-)
 rename PennylaneTutorial.ipynb => covalent_cloud_gpu.ipynb (97%)

diff --git a/PennylaneTutorial.ipynb b/covalent_cloud_gpu.ipynb
similarity index 97%
rename from PennylaneTutorial.ipynb
rename to covalent_cloud_gpu.ipynb
index 6ae77bb4cd..16739350ed 100644
--- a/PennylaneTutorial.ipynb
+++ b/covalent_cloud_gpu.ipynb
@@ -255,46 +255,6 @@
    "execution_count": 8,
    "metadata": {},
    "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "
/Users/neptune/anaconda3/envs/qml/lib/python3.10/site-packages/rich/live.py:231: UserWarning: install \"ipywidgets\" \n",
-       "for Jupyter support\n",
-       "  warnings.warn('install \"ipywidgets\" for Jupyter support')\n",
-       "
\n"
-      ],
-      "text/plain": [
-       "/Users/neptune/anaconda3/envs/qml/lib/python3.10/site-packages/rich/live.py:231: UserWarning: install \"ipywidgets\" \n",
-       "for Jupyter support\n",
-       "  warnings.warn('install \"ipywidgets\" for Jupyter support')\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "
\n"
-      ],
-      "text/plain": []
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "
\n",
-       "
\n"
-      ],
-      "text/plain": [
-       "\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
     {
      "name": "stdout",
      "output_type": "stream",
@@ -320,14 +280,6 @@
    "execution_count": 10,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/neptune/anaconda3/envs/qml/lib/python3.10/site-packages/covalent/_workflow/transportable_object.py:205: UserWarning: This figure was saved with matplotlib version 3.8.2 and is unlikely to function correctly.\n",
-      "  return cloudpickle.loads(base64.b64decode(self._object.encode(\"utf-8\")))\n"
-     ]
-    },
     {
      "name": "stdout",
      "output_type": "stream",

From 33c464c799133c82f3012684a7550e1a216ccb92 Mon Sep 17 00:00:00 2001
From: Sankalp Sanand 
Date: Tue, 14 May 2024 18:08:53 +0530
Subject: [PATCH 03/18] adding .py file

---
 covalent_cloud_gpu.ipynb                      | 473 ------------------
 demonstrations/covalent_cloud_gpu.py          | 355 +++++++++++++
 .../covalent_cloud_gpu_19_1.png               | Bin 0 -> 36050 bytes
 3 files changed, 355 insertions(+), 473 deletions(-)
 delete mode 100644 covalent_cloud_gpu.ipynb
 create mode 100644 demonstrations/covalent_cloud_gpu.py
 create mode 100644 demonstrations/covalent_cloud_gpu/covalent_cloud_gpu_19_1.png

diff --git a/covalent_cloud_gpu.ipynb b/covalent_cloud_gpu.ipynb
deleted file mode 100644
index 16739350ed..0000000000
--- a/covalent_cloud_gpu.ipynb
+++ /dev/null
@@ -1,473 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Classification with GPU-quantum embedding kernels on Covalent Cloud"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Quantum support-vector machines (QSVMs) are essentially [traditional SVMs](https://en.wikipedia.org/wiki/Support_vector_machine) that rely on [embedding kernels](https://en.wikipedia.org/wiki/Kernel_method#Mathematics:_the_kernel_trick) evaluated on a quantum computer—a.k.a. [*quantum embedding kernel*](https://pennylane.ai/qml/demos/tutorial_kernels_module/#training-and-evaluating-quantum-kernels). These kernels provide a unique (and perhaps classically-intractable) means of measuring pairwise similarity.\n",
-    "\n",
-    "Using GPUs to simulate quantum computers is worthwhile when qubit capacity and/or fidelity requirements are not met by the available quantum hardware. While QSVMs are relatively tolerant to noise (an important reason for their current popularity), evaluating kernels on *real* quantum hardware is not always practical nor necessary.\n",
-    "\n",
-    "In this tutorial, we’ll demonstrate how to run GPU-accelerated quantum circuit simulations on Covalent Cloud."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let’s start by importing the required packages. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import covalent as ct\n",
-    "import covalent_cloud as cc\n",
-    "import matplotlib.pyplot as plt\n",
-    "import pennylane as qml\n",
-    "from matplotlib.colors import ListedColormap\n",
-    "from pennylane import numpy as np\n",
-    "from sklearn.datasets import make_blobs\n",
-    "from sklearn.inspection import DecisionBoundaryDisplay\n",
-    "from sklearn.model_selection import train_test_split\n",
-    "from sklearn.svm import SVC\n",
-    "\n",
-    "# cc.save_api_key(\"YOUR_API_KEY\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Covalent Cloud allows us to create [re-usable execution environments](https://docs.covalent.xyz/docs/cloud/guides/cloud_custom_environments/), as shown below. This environment represents a typical setup for running Pennylane on NVIDIA GPUs."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Waiting for environment pennylane-gpu to be ready............................................................................................................................................................................\n",
-      "Name: pennylane-gpu\n",
-      "Status: READY\n",
-      "Estimated Time: 1002 seconds\n",
-      "Notes:\n",
-      "\tpip was added to the dependencies.\n",
-      "\tPython version 3.10 was added to the dependencies.\n",
-      "Environment file contains:\n",
-      "==========================\n",
-      "channels:\n",
-      "- conda-forge\n",
-      "dependencies:\n",
-      "- python=3.10\n",
-      "- pip\n",
-      "- cudatoolkit>=11.8\n",
-      "- pip:\n",
-      "  - cuquantum==23.10.0\n",
-      "  - matplotlib==3.8.2\n",
-      "  - Pennylane==0.34.0\n",
-      "  - PennyLane-Lightning[GPU]==0.34.0\n",
-      "  - scikit-learn==1.3.1\n",
-      "  - torch==2.1.2\n",
-      "  - covalent-cloud\n",
-      "name: pennylane-gpu\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "cc.create_env(\n",
-    "    name=\"pennylane-gpu\",  # identifier for referring to this environment\n",
-    "    conda={\n",
-    "        \"channels\": [\"conda-forge\"],\n",
-    "        \"dependencies\": [\"cudatoolkit>=11.8\"],\n",
-    "    }, pip=[\n",
-    "        \"cuquantum==23.10.0\",\n",
-    "        \"matplotlib==3.8.2\",\n",
-    "        \"Pennylane==0.34.0\",\n",
-    "        \"PennyLane-Lightning[GPU]==0.34.0\",\n",
-    "        \"scikit-learn==1.3.1\",\n",
-    "        \"torch==2.1.2\",\n",
-    "    ],\n",
-    "    wait=True\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Next, we'll define our resource specifications by creating some [executors](https://docs.covalent.xyz/docs/user-documentation/api-reference/executors/cloud_executor) for this workflow. Both executors will run tasks in our new environment, named `”pennylane-gpu”`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cpu_executor = cc.CloudExecutor(  # for lightweight non-quantum tasks\n",
-    "    env=\"pennylane-gpu\",\n",
-    "    num_cpus=2,\n",
-    "    memory=\"2GB\",\n",
-    ")\n",
-    "gpu_executor = cc.CloudExecutor(  # for GPU-powered circuit simulations\n",
-    "    env=\"pennylane-gpu\",\n",
-    "    num_cpus=4,\n",
-    "    memory=\"12GB\",\n",
-    "    num_gpus=1,\n",
-    "    gpu_type=\"v100\"\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "On to the algorithm!\n",
-    "\n",
-    "Here's a function returns a simple quantum kernel based on Pennylane's [IQP Embedding](https://docs.pennylane.ai/en/stable/code/api/pennylane.IQPEmbedding.html) template. We'll use it as-is inside our workflow."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "QML_DEVICE = \"lightning.qubit\"\n",
-    "\n",
-    "def get_kernel_circuit(n_wires):\n",
-    "\n",
-    "    @qml.qnode(qml.device(QML_DEVICE, wires=n_wires, shots=None))\n",
-    "    def circuit(x1, x2):\n",
-    "        qml.IQPEmbedding(x1, wires=range(n_wires), n_repeats=4)\n",
-    "        qml.adjoint(qml.IQPEmbedding)(x2, wires=range(n_wires), n_repeats=4)\n",
-    "        return qml.probs(wires=range(n_wires))\n",
-    "\n",
-    "    return lambda x1, x2: circuit(x1, x2)[0]  # |0..0> state probability"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Next, each function destined for remote execution is decorated with `@ct.electron`, with an [executor](https://docs.covalent.xyz/docs/user-documentation/api-reference/executors/cloud_executor) specified therein. Only tasks that evaluate the simulated quantum kernel should require `gpu_executor`. For example, we don't need GPUs to generate our input data:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "@ct.electron(executor=cpu_executor)  # lightweight non-quantum task\n",
-    "def get_split_data(n_samples=18, test_size=0.2):\n",
-    "    centers = [(1, 1), (1, 2), (1, 3), (2, 1), (2, 2), (2, 3), (3, 1), (3, 2), (3, 3)]\n",
-    "    X, y = make_blobs(n_samples, centers=centers, cluster_std=0.25, shuffle=False)\n",
-    "    # rescale labels to be -1, 1\n",
-    "    mapping = {0: -1, 1: 1, 2: -1, 3: 1, 4: -1, 5: 1, 6: -1, 7: 1, 8: -1}\n",
-    "    y = np.array([mapping[i] for i in y])\n",
-    "    X = X.astype(np.float32)\n",
-    "    y = y.astype(int)\n",
-    "\n",
-    "    # X_train, X_test, y_train, y_test\n",
-    "    return train_test_split(X, y, test_size=test_size, random_state=3)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Classifying with the SVM, on the other hand, requires $O(n^2)$ kernel evaluations, where $n$ is the dataset size. Accordingly, we'll use GPUs (i.e. `gpu_executor`) to speed up this process."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "DISP_SETTINGS = {\"grid_resolution\": 50, \"response_method\": \"predict\", \"alpha\": 0.5, \"cmap\": plt.cm.RdBu}\n",
-    "\n",
-    "@ct.electron(executor=gpu_executor)\n",
-    "def classify_with_qsvm(Xtr, Xte, ytr, yte):\n",
-    "    kernel = get_kernel_circuit(n_wires=Xtr.shape[1])\n",
-    "\n",
-    "    kernel_matrix_fn = lambda X, Z: qml.kernels.kernel_matrix(X, Z, kernel)\n",
-    "    svc = SVC(kernel=kernel_matrix_fn).fit(Xtr, ytr)\n",
-    "\n",
-    "    # train/test accuracy\n",
-    "    accuracy_tr = svc.score(Xtr, ytr)\n",
-    "    accuracy_te = svc.score(Xte, yte)\n",
-    "\n",
-    "    # decision boundary\n",
-    "    cm_bright = ListedColormap([\"#FF0000\", \"#0000FF\"])\n",
-    "    disp = DecisionBoundaryDisplay.from_estimator(svc, Xte, **DISP_SETTINGS)\n",
-    "    disp.ax_.scatter(Xtr[:, 0], Xtr[:, 1], c=ytr, cmap=cm_bright)\n",
-    "    disp.ax_.scatter(Xte[:, 0], Xte[:, 1], c=yte, cmap=cm_bright, marker=\"$\\u25EF$\")\n",
-    "\n",
-    "    return accuracy_tr, accuracy_te, disp"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Putting it all together, we can define a QSVM training and testing workflow. This special function gets decorated with `@ct.lattice`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "@ct.lattice(workflow_executor=cpu_executor, executor=cpu_executor)\n",
-    "def run_qsvm(n_samples, test_size):\n",
-    "    Xtr, Xte, ytr, yte = get_split_data(n_samples, test_size)\n",
-    "    return classify_with_qsvm(Xtr, Xte, ytr, yte)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now, to dispatch `run_qsvm` to Covalent Cloud, we call it after wrapping with `ct.dispatch`, as usual."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Dispatch ID: 0b5d3a08-fe9c-4dc2-910b-0be6eb925663\n"
-     ]
-    }
-   ],
-   "source": [
-    "dispatch_id = cc.dispatch(run_qsvm)(n_samples=64, test_size=0.2)\n",
-    "print(\"Dispatch ID:\", dispatch_id)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's what we get when we query and display the results."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Train accuracy: 64.7%\n",
-      "Test accuracy: 76.9%\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       ""
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "
"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "result = cc.get_result(dispatch_id, wait=True)\n",
-    "result.result.load()\n",
-    "\n",
-    "train_acc, test_acc, decision_boundary_figure = result.result.value\n",
-    "print(f\"Train accuracy: {train_acc * 100:.1f}%\")\n",
-    "print(f\"Test accuracy: {test_acc * 100:.1f}%\")\n",
-    "\n",
-    "decision_boundary_figure"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Conclusion\n",
-    "\n",
-    "In this tutorial, we demonstrated how to run quantum circuit simulations on GPUs via Covalent Cloud. We used Pennylane to define a simple quantum kernel, and then trained and tested a QSVM on a 2-dimensional dataset. To make the most of this tutorial, try experimenting with different datasets/kernels or increasing the dataset dimension, to gain a greater advantage from GPU acceleration.\n",
-    "\n",
-    "The cost of running this workflow is approximately $0.27. The full code is available below."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Full Code"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "```python\n",
-    "import covalent as ct\n",
-    "import covalent_cloud as cc\n",
-    "import matplotlib.pyplot as plt\n",
-    "import pennylane as qml\n",
-    "from matplotlib.colors import ListedColormap\n",
-    "from pennylane import numpy as np\n",
-    "from sklearn.datasets import make_blobs\n",
-    "from sklearn.inspection import DecisionBoundaryDisplay\n",
-    "from sklearn.model_selection import train_test_split\n",
-    "from sklearn.svm import SVC\n",
-    "\n",
-    "cc.save_api_key(\"API_KEY\")\n",
-    "\n",
-    "cc.create_env(\n",
-    "    name=\"pennylane-gpu\",  # identifier for referring to this environment\n",
-    "    conda={\n",
-    "        \"channels\": [\"conda-forge\"],\n",
-    "        \"dependencies\": [\"cudatoolkit>=11.8\"],\n",
-    "    }, pip=[\n",
-    "        \"cuquantum==23.10.0\",\n",
-    "        \"matplotlib==3.8.2\",\n",
-    "        \"Pennylane==0.34.0\",\n",
-    "        \"PennyLane-Lightning[GPU]==0.34.0\",\n",
-    "        \"scikit-learn==1.3.1\",\n",
-    "        \"torch==2.1.2\",\n",
-    "    ],\n",
-    "    wait=True\n",
-    ")\n",
-    "\n",
-    "cpu_executor = cc.CloudExecutor(  # for lightweight non-quantum tasks\n",
-    "    env=\"pennylane-gpu\",\n",
-    "    num_cpus=2,\n",
-    "    memory=\"2GB\",\n",
-    ")\n",
-    "gpu_executor = cc.CloudExecutor(  # for GPU-powered circuit simulations\n",
-    "    env=\"pennylane-gpu\",\n",
-    "    num_cpus=4,\n",
-    "    memory=\"12GB\",\n",
-    "    num_gpus=1,\n",
-    "    gpu_type=\"v100\"\n",
-    ")\n",
-    "\n",
-    "QML_DEVICE = \"lightning.gpu\"\n",
-    "\n",
-    "def get_kernel_circuit(n_wires):\n",
-    "\n",
-    "    @qml.qnode(qml.device(QML_DEVICE, wires=n_wires, shots=None))\n",
-    "    def circuit(x1, x2):\n",
-    "        qml.IQPEmbedding(x1, wires=range(n_wires), n_repeats=4)\n",
-    "        qml.adjoint(qml.IQPEmbedding)(x2, wires=range(n_wires), n_repeats=4)\n",
-    "        return qml.probs(wires=range(n_wires))\n",
-    "\n",
-    "    return lambda x1, x2: circuit(x1, x2)[0]  # |0..0> state probability\n",
-    "\n",
-    "@ct.electron(executor=cpu_executor)  # lightweight non-quantum task\n",
-    "def get_split_data(n_samples=18, test_size=0.2):\n",
-    "    centers = [(1, 1), (1, 2), (1, 3), (2, 1), (2, 2), (2, 3), (3, 1), (3, 2), (3, 3)]\n",
-    "    X, y = make_blobs(n_samples, centers=centers, cluster_std=0.25, shuffle=False)\n",
-    "    # rescale labels to be -1, 1\n",
-    "    mapping = {0: -1, 1: 1, 2: -1, 3: 1, 4: -1, 5: 1, 6: -1, 7: 1, 8: -1}\n",
-    "    y = np.array([mapping[i] for i in y])\n",
-    "    X = X.astype(np.float32)\n",
-    "    y = y.astype(int)\n",
-    "\n",
-    "    # X_train, X_test, y_train, y_test\n",
-    "    return train_test_split(X, y, test_size=test_size, random_state=3)\n",
-    "\n",
-    "DISP_SETTINGS = {\"grid_resolution\": 50, \"response_method\": \"predict\", \"alpha\": 0.5, \"cmap\": plt.cm.RdBu}\n",
-    "\n",
-    "@ct.electron(executor=gpu_executor)\n",
-    "def classify_with_qsvm(Xtr, Xte, ytr, yte):\n",
-    "    kernel = get_kernel_circuit(n_wires=Xtr.shape[1])\n",
-    "\n",
-    "    kernel_matrix_fn = lambda X, Z: qml.kernels.kernel_matrix(X, Z, kernel)\n",
-    "    svc = SVC(kernel=kernel_matrix_fn).fit(Xtr, ytr)\n",
-    "\n",
-    "    # train/test accuracy\n",
-    "    accuracy_tr = svc.score(Xtr, ytr)\n",
-    "    accuracy_te = svc.score(Xte, yte)\n",
-    "\n",
-    "    # decision boundary\n",
-    "    cm_bright = ListedColormap([\"#FF0000\", \"#0000FF\"])\n",
-    "    disp = DecisionBoundaryDisplay.from_estimator(svc, Xte, **DISP_SETTINGS)\n",
-    "    disp.ax_.scatter(Xtr[:, 0], Xtr[:, 1], c=ytr, cmap=cm_bright)\n",
-    "    disp.ax_.scatter(Xte[:, 0], Xte[:, 1], c=yte, cmap=cm_bright, marker=\"$\\u25EF$\")\n",
-    "\n",
-    "    return accuracy_tr, accuracy_te, disp\n",
-    "\n",
-    "@ct.lattice(workflow_executor=cpu_executor, executor=cpu_executor)\n",
-    "def run_qsvm(n_samples, test_size):\n",
-    "    Xtr, Xte, ytr, yte = get_split_data(n_samples, test_size)\n",
-    "    return classify_with_qsvm(Xtr, Xte, ytr, yte)\n",
-    "\n",
-    "dispatch_id = cc.dispatch(run_qsvm)(n_samples=64, test_size=0.2)\n",
-    "print(\"Dispatch ID:\", dispatch_id)\n",
-    "\n",
-    "result = cc.get_result(dispatch_id, wait=True)\n",
-    "result.result.load()\n",
-    "\n",
-    "train_acc, test_acc, decision_boundary_figure = result.result.value\n",
-    "print(f\"Train accuracy: {train_acc * 100:.1f}%\")\n",
-    "print(f\"Test accuracy: {test_acc * 100:.1f}%\")\n",
-    "```"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.14"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/demonstrations/covalent_cloud_gpu.py b/demonstrations/covalent_cloud_gpu.py
new file mode 100644
index 0000000000..d3036ff7c6
--- /dev/null
+++ b/demonstrations/covalent_cloud_gpu.py
@@ -0,0 +1,355 @@
+r"""Classification with GPU-quantum embedding kernels on Covalent Cloud
+===================================================================
+"""
+
+######################################################################
+# Quantum support-vector machines (QSVMs) are essentially `traditional
+# SVMs `__ that rely on `embedding
+# kernels `__ evaluated on
+# a quantum computer—a.k.a. `quantum embedding
+# kernel `__.
+# These kernels provide a unique (and perhaps classically-intractable) means of measuring pairwise
+# similarity.
+# 
+# Using GPUs to simulate quantum computers is worthwhile when qubit capacity and/or fidelity
+# requirements are not met by the available quantum hardware. While QSVMs are relatively tolerant to
+# noise (an important reason for their current popularity), evaluating kernels on *real* quantum
+# hardware is not always practical nor necessary.
+# 
+# In this tutorial, we’ll demonstrate how to run GPU-accelerated quantum circuit simulations on
+# Covalent Cloud.
+# 
+
+######################################################################
+# Let’s start by importing the required packages.
+# 
+
+import covalent as ct
+import covalent_cloud as cc
+import matplotlib.pyplot as plt
+import pennylane as qml
+from matplotlib.colors import ListedColormap
+from pennylane import numpy as np
+from sklearn.datasets import make_blobs
+from sklearn.inspection import DecisionBoundaryDisplay
+from sklearn.model_selection import train_test_split
+from sklearn.svm import SVC
+
+# cc.save_api_key("YOUR_API_KEY")
+
+######################################################################
+# Covalent Cloud allows us to create `re-usable execution
+# environments `__, as shown
+# below. This environment represents a typical setup for running Pennylane on NVIDIA GPUs.
+# 
+
+cc.create_env(
+    name="pennylane-gpu",  # identifier for referring to this environment
+    conda={
+        "channels": ["conda-forge"],
+        "dependencies": ["cudatoolkit>=11.8"],
+    }, pip=[
+        "cuquantum==23.10.0",
+        "matplotlib==3.8.2",
+        "Pennylane==0.34.0",
+        "PennyLane-Lightning[GPU]==0.34.0",
+        "scikit-learn==1.3.1",
+        "torch==2.1.2",
+    ],
+    wait=True
+)
+
+######################################################################
+# .. rst-class :: sphx-glr-script-out
+# 
+# .. code-block: none
+# 
+#    Waiting for environment pennylane-gpu to be ready............................................................................................................................................................................
+#    Name: pennylane-gpu
+#    Status: READY
+#    Estimated Time: 1002 seconds
+#    Notes:
+#    	pip was added to the dependencies.
+#    	Python version 3.10 was added to the dependencies.
+#    Environment file contains:
+#    ==========================
+#    channels:
+#    - conda-forge
+#    dependencies:
+#    - python=3.10
+#    - pip
+#    - cudatoolkit>=11.8
+#    - pip:
+#      - cuquantum==23.10.0
+#      - matplotlib==3.8.2
+#      - Pennylane==0.34.0
+#      - PennyLane-Lightning[GPU]==0.34.0
+#      - scikit-learn==1.3.1
+#      - torch==2.1.2
+#      - covalent-cloud
+#    name: pennylane-gpu
+#    
+
+######################################################################
+# Next, we’ll define our resource specifications by creating some
+# `executors `__
+# for this workflow. Both executors will run tasks in our new environment, named ``”pennylane-gpu”``.
+# 
+
+cpu_executor = cc.CloudExecutor(  # for lightweight non-quantum tasks
+    env="pennylane-gpu",
+    num_cpus=2,
+    memory="2GB",
+)
+gpu_executor = cc.CloudExecutor(  # for GPU-powered circuit simulations
+    env="pennylane-gpu",
+    num_cpus=4,
+    memory="12GB",
+    num_gpus=1,
+    gpu_type="v100"
+)
+
+######################################################################
+# On to the algorithm!
+# 
+# Here’s a function returns a simple quantum kernel based on Pennylane’s `IQP
+# Embedding `__ template.
+# We’ll use it as-is inside our workflow.
+# 
+
+QML_DEVICE = "lightning.qubit"
+
+def get_kernel_circuit(n_wires):
+
+    @qml.qnode(qml.device(QML_DEVICE, wires=n_wires, shots=None))
+    def circuit(x1, x2):
+        qml.IQPEmbedding(x1, wires=range(n_wires), n_repeats=4)
+        qml.adjoint(qml.IQPEmbedding)(x2, wires=range(n_wires), n_repeats=4)
+        return qml.probs(wires=range(n_wires))
+
+    return lambda x1, x2: circuit(x1, x2)[0]  # |0..0> state probability
+
+######################################################################
+# Next, each function destined for remote execution is decorated with ``@ct.electron``, with an
+# `executor `__
+# specified therein. Only tasks that evaluate the simulated quantum kernel should require
+# ``gpu_executor``. For example, we don’t need GPUs to generate our input data:
+# 
+
+@ct.electron(executor=cpu_executor)  # lightweight non-quantum task
+def get_split_data(n_samples=18, test_size=0.2):
+    centers = [(1, 1), (1, 2), (1, 3), (2, 1), (2, 2), (2, 3), (3, 1), (3, 2), (3, 3)]
+    X, y = make_blobs(n_samples, centers=centers, cluster_std=0.25, shuffle=False)
+    # rescale labels to be -1, 1
+    mapping = {0: -1, 1: 1, 2: -1, 3: 1, 4: -1, 5: 1, 6: -1, 7: 1, 8: -1}
+    y = np.array([mapping[i] for i in y])
+    X = X.astype(np.float32)
+    y = y.astype(int)
+
+    # X_train, X_test, y_train, y_test
+    return train_test_split(X, y, test_size=test_size, random_state=3)
+
+######################################################################
+# Classifying with the SVM, on the other hand, requires :math:`O(n^2)` kernel evaluations, where
+# :math:`n` is the dataset size. Accordingly, we’ll use GPUs (i.e. ``gpu_executor``) to speed up this
+# process.
+# 
+
+DISP_SETTINGS = {"grid_resolution": 50, "response_method": "predict", "alpha": 0.5, "cmap": plt.cm.RdBu}
+
+@ct.electron(executor=gpu_executor)
+def classify_with_qsvm(Xtr, Xte, ytr, yte):
+    kernel = get_kernel_circuit(n_wires=Xtr.shape[1])
+
+    kernel_matrix_fn = lambda X, Z: qml.kernels.kernel_matrix(X, Z, kernel)
+    svc = SVC(kernel=kernel_matrix_fn).fit(Xtr, ytr)
+
+    # train/test accuracy
+    accuracy_tr = svc.score(Xtr, ytr)
+    accuracy_te = svc.score(Xte, yte)
+
+    # decision boundary
+    cm_bright = ListedColormap(["#FF0000", "#0000FF"])
+    disp = DecisionBoundaryDisplay.from_estimator(svc, Xte, **DISP_SETTINGS)
+    disp.ax_.scatter(Xtr[:, 0], Xtr[:, 1], c=ytr, cmap=cm_bright)
+    disp.ax_.scatter(Xte[:, 0], Xte[:, 1], c=yte, cmap=cm_bright, marker="$\u25EF$")
+
+    return accuracy_tr, accuracy_te, disp
+
+######################################################################
+# Putting it all together, we can define a QSVM training and testing workflow. This special function
+# gets decorated with ``@ct.lattice``.
+# 
+
+@ct.lattice(workflow_executor=cpu_executor, executor=cpu_executor)
+def run_qsvm(n_samples, test_size):
+    Xtr, Xte, ytr, yte = get_split_data(n_samples, test_size)
+    return classify_with_qsvm(Xtr, Xte, ytr, yte)
+
+######################################################################
+# Now, to dispatch ``run_qsvm`` to Covalent Cloud, we call it after wrapping with ``ct.dispatch``, as
+# usual.
+# 
+
+dispatch_id = cc.dispatch(run_qsvm)(n_samples=64, test_size=0.2)
+print("Dispatch ID:", dispatch_id)
+
+######################################################################
+# .. rst-class :: sphx-glr-script-out
+# 
+# .. code-block: none
+# 
+#    Dispatch ID: 0b5d3a08-fe9c-4dc2-910b-0be6eb925663
+
+######################################################################
+# Here’s what we get when we query and display the results.
+# 
+
+result = cc.get_result(dispatch_id, wait=True)
+result.result.load()
+
+train_acc, test_acc, decision_boundary_figure = result.result.value
+print(f"Train accuracy: {train_acc * 100:.1f}%")
+print(f"Test accuracy: {test_acc * 100:.1f}%")
+
+decision_boundary_figure
+
+######################################################################
+# .. rst-class :: sphx-glr-script-out
+# 
+# .. code-block: none
+# 
+#    Train accuracy: 64.7%
+#    Test accuracy: 76.9%# .. rst-class :: sphx-glr-script-out
+# 
+# .. code-block: none
+# 
+#
+# .. figure:: ../demonstrations/covalent_cloud_gpu/covalent_cloud_gpu_19_1.png
+#    :align: center
+#    :width: 80%
+
+######################################################################
+# Conclusion
+# ==========
+# 
+# In this tutorial, we demonstrated how to run quantum circuit simulations on GPUs via Covalent Cloud.
+# We used Pennylane to define a simple quantum kernel, and then trained and tested a QSVM on a
+# 2-dimensional dataset. To make the most of this tutorial, try experimenting with different
+# datasets/kernels or increasing the dataset dimension, to gain a greater advantage from GPU
+# acceleration.
+# 
+# The cost of running this workflow is approximately $0.27. The full code is available below.
+# 
+
+######################################################################
+# Full Code
+# ---------
+# 
+
+######################################################################
+# .. code:: python
+# 
+#    import covalent as ct
+#    import covalent_cloud as cc
+#    import matplotlib.pyplot as plt
+#    import pennylane as qml
+#    from matplotlib.colors import ListedColormap
+#    from pennylane import numpy as np
+#    from sklearn.datasets import make_blobs
+#    from sklearn.inspection import DecisionBoundaryDisplay
+#    from sklearn.model_selection import train_test_split
+#    from sklearn.svm import SVC
+# 
+#    cc.save_api_key("API_KEY")
+# 
+#    cc.create_env(
+#        name="pennylane-gpu",  # identifier for referring to this environment
+#        conda={
+#            "channels": ["conda-forge"],
+#            "dependencies": ["cudatoolkit>=11.8"],
+#        }, pip=[
+#            "cuquantum==23.10.0",
+#            "matplotlib==3.8.2",
+#            "Pennylane==0.34.0",
+#            "PennyLane-Lightning[GPU]==0.34.0",
+#            "scikit-learn==1.3.1",
+#            "torch==2.1.2",
+#        ],
+#        wait=True
+#    )
+# 
+#    cpu_executor = cc.CloudExecutor(  # for lightweight non-quantum tasks
+#        env="pennylane-gpu",
+#        num_cpus=2,
+#        memory="2GB",
+#    )
+#    gpu_executor = cc.CloudExecutor(  # for GPU-powered circuit simulations
+#        env="pennylane-gpu",
+#        num_cpus=4,
+#        memory="12GB",
+#        num_gpus=1,
+#        gpu_type="v100"
+#    )
+# 
+#    QML_DEVICE = "lightning.gpu"
+# 
+#    def get_kernel_circuit(n_wires):
+# 
+#        @qml.qnode(qml.device(QML_DEVICE, wires=n_wires, shots=None))
+#        def circuit(x1, x2):
+#            qml.IQPEmbedding(x1, wires=range(n_wires), n_repeats=4)
+#            qml.adjoint(qml.IQPEmbedding)(x2, wires=range(n_wires), n_repeats=4)
+#            return qml.probs(wires=range(n_wires))
+# 
+#        return lambda x1, x2: circuit(x1, x2)[0]  # |0..0> state probability
+# 
+#    @ct.electron(executor=cpu_executor)  # lightweight non-quantum task
+#    def get_split_data(n_samples=18, test_size=0.2):
+#        centers = [(1, 1), (1, 2), (1, 3), (2, 1), (2, 2), (2, 3), (3, 1), (3, 2), (3, 3)]
+#        X, y = make_blobs(n_samples, centers=centers, cluster_std=0.25, shuffle=False)
+#        # rescale labels to be -1, 1
+#        mapping = {0: -1, 1: 1, 2: -1, 3: 1, 4: -1, 5: 1, 6: -1, 7: 1, 8: -1}
+#        y = np.array([mapping[i] for i in y])
+#        X = X.astype(np.float32)
+#        y = y.astype(int)
+# 
+#        # X_train, X_test, y_train, y_test
+#        return train_test_split(X, y, test_size=test_size, random_state=3)
+# 
+#    DISP_SETTINGS = {"grid_resolution": 50, "response_method": "predict", "alpha": 0.5, "cmap": plt.cm.RdBu}
+# 
+#    @ct.electron(executor=gpu_executor)
+#    def classify_with_qsvm(Xtr, Xte, ytr, yte):
+#        kernel = get_kernel_circuit(n_wires=Xtr.shape[1])
+# 
+#        kernel_matrix_fn = lambda X, Z: qml.kernels.kernel_matrix(X, Z, kernel)
+#        svc = SVC(kernel=kernel_matrix_fn).fit(Xtr, ytr)
+# 
+#        # train/test accuracy
+#        accuracy_tr = svc.score(Xtr, ytr)
+#        accuracy_te = svc.score(Xte, yte)
+# 
+#        # decision boundary
+#        cm_bright = ListedColormap(["#FF0000", "#0000FF"])
+#        disp = DecisionBoundaryDisplay.from_estimator(svc, Xte, **DISP_SETTINGS)
+#        disp.ax_.scatter(Xtr[:, 0], Xtr[:, 1], c=ytr, cmap=cm_bright)
+#        disp.ax_.scatter(Xte[:, 0], Xte[:, 1], c=yte, cmap=cm_bright, marker="$\u25EF$")
+# 
+#        return accuracy_tr, accuracy_te, disp
+# 
+#    @ct.lattice(workflow_executor=cpu_executor, executor=cpu_executor)
+#    def run_qsvm(n_samples, test_size):
+#        Xtr, Xte, ytr, yte = get_split_data(n_samples, test_size)
+#        return classify_with_qsvm(Xtr, Xte, ytr, yte)
+# 
+#    dispatch_id = cc.dispatch(run_qsvm)(n_samples=64, test_size=0.2)
+#    print("Dispatch ID:", dispatch_id)
+# 
+#    result = cc.get_result(dispatch_id, wait=True)
+#    result.result.load()
+# 
+#    train_acc, test_acc, decision_boundary_figure = result.result.value
+#    print(f"Train accuracy: {train_acc * 100:.1f}%")
+#    print(f"Test accuracy: {test_acc * 100:.1f}%")
+# 
\ No newline at end of file
diff --git a/demonstrations/covalent_cloud_gpu/covalent_cloud_gpu_19_1.png b/demonstrations/covalent_cloud_gpu/covalent_cloud_gpu_19_1.png
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z7En+C(9AELaJ>3{D5&*
Date: Tue, 14 May 2024 18:20:52 +0530
Subject: [PATCH 04/18] added metadata file

---
 .../covalent_cloud_gpu.metadata.json          |  75 +++++++++++
 demonstrations/covalent_cloud_gpu.py          | 120 ++++++++++--------
 2 files changed, 140 insertions(+), 55 deletions(-)
 create mode 100644 demonstrations/covalent_cloud_gpu.metadata.json

diff --git a/demonstrations/covalent_cloud_gpu.metadata.json b/demonstrations/covalent_cloud_gpu.metadata.json
new file mode 100644
index 0000000000..e3b1bbe45e
--- /dev/null
+++ b/demonstrations/covalent_cloud_gpu.metadata.json
@@ -0,0 +1,75 @@
+{
+    "title": "Classification with GPU-quantum embedding kernels on Covalent Cloud",
+    "authors": [
+        {
+            "id": "",
+            "affiliation": ""
+        },
+        {
+            "id": "",
+            "affiliation": ""
+        }
+    ],
+    "dateOfPublication": "",
+    "dateOfLastModification": "",
+    "categories": [],
+    "tags": [],
+    "previewImages": [
+        {
+            "type": "thumbnail",
+            "uri": ""
+        }
+    ],
+    "seoDescription": "",
+    "doi": "",
+    "canonicalURL": "",
+    "references": [
+        {
+            "id": "",
+            "type": "",
+            "title": "",
+            "authors": "",
+            "year": "",
+            "journal": "",
+            "url": ""
+        },
+        {
+            "id": "",
+            "type": "",
+            "title": "",
+            "authors": "",
+            "year": "",
+            "journal": "",
+            "url": ""
+        },
+        {
+            "id": "",
+            "type": "",
+            "title": "",
+            "authors": "",
+            "year": "",
+            "journal": "",
+            "url": ""
+        }
+    ],
+    "basedOnPapers": [],
+    "referencedByPapers": [
+        {
+            "type": "demonstration",
+            "id": "",
+            "weight": 1.0
+        },
+        {
+            "type": "demonstration",
+            "id": "",
+            "weight": 1.0
+        },
+        {
+            "type": "demonstration",
+            "id": "",
+            "weight": 1.0
+        }
+    ],
+    "relatedContent": [],
+    "hardware": []
+}
diff --git a/demonstrations/covalent_cloud_gpu.py b/demonstrations/covalent_cloud_gpu.py
index d3036ff7c6..fe93ab14b0 100644
--- a/demonstrations/covalent_cloud_gpu.py
+++ b/demonstrations/covalent_cloud_gpu.py
@@ -10,19 +10,19 @@
 # kernel `__.
 # These kernels provide a unique (and perhaps classically-intractable) means of measuring pairwise
 # similarity.
-# 
+#
 # Using GPUs to simulate quantum computers is worthwhile when qubit capacity and/or fidelity
 # requirements are not met by the available quantum hardware. While QSVMs are relatively tolerant to
 # noise (an important reason for their current popularity), evaluating kernels on *real* quantum
 # hardware is not always practical nor necessary.
-# 
+#
 # In this tutorial, we’ll demonstrate how to run GPU-accelerated quantum circuit simulations on
 # Covalent Cloud.
-# 
+#
 
 ######################################################################
 # Let’s start by importing the required packages.
-# 
+#
 
 import covalent as ct
 import covalent_cloud as cc
@@ -41,14 +41,15 @@
 # Covalent Cloud allows us to create `re-usable execution
 # environments `__, as shown
 # below. This environment represents a typical setup for running Pennylane on NVIDIA GPUs.
-# 
+#
 
 cc.create_env(
     name="pennylane-gpu",  # identifier for referring to this environment
     conda={
         "channels": ["conda-forge"],
         "dependencies": ["cudatoolkit>=11.8"],
-    }, pip=[
+    },
+    pip=[
         "cuquantum==23.10.0",
         "matplotlib==3.8.2",
         "Pennylane==0.34.0",
@@ -56,14 +57,14 @@
         "scikit-learn==1.3.1",
         "torch==2.1.2",
     ],
-    wait=True
+    wait=True,
 )
 
 ######################################################################
 # .. rst-class :: sphx-glr-script-out
-# 
+#
 # .. code-block: none
-# 
+#
 #    Waiting for environment pennylane-gpu to be ready............................................................................................................................................................................
 #    Name: pennylane-gpu
 #    Status: READY
@@ -88,13 +89,13 @@
 #      - torch==2.1.2
 #      - covalent-cloud
 #    name: pennylane-gpu
-#    
+#
 
 ######################################################################
 # Next, we’ll define our resource specifications by creating some
 # `executors `__
 # for this workflow. Both executors will run tasks in our new environment, named ``”pennylane-gpu”``.
-# 
+#
 
 cpu_executor = cc.CloudExecutor(  # for lightweight non-quantum tasks
     env="pennylane-gpu",
@@ -102,23 +103,20 @@
     memory="2GB",
 )
 gpu_executor = cc.CloudExecutor(  # for GPU-powered circuit simulations
-    env="pennylane-gpu",
-    num_cpus=4,
-    memory="12GB",
-    num_gpus=1,
-    gpu_type="v100"
+    env="pennylane-gpu", num_cpus=4, memory="12GB", num_gpus=1, gpu_type="v100"
 )
 
 ######################################################################
 # On to the algorithm!
-# 
+#
 # Here’s a function returns a simple quantum kernel based on Pennylane’s `IQP
 # Embedding `__ template.
 # We’ll use it as-is inside our workflow.
-# 
+#
 
 QML_DEVICE = "lightning.qubit"
 
+
 def get_kernel_circuit(n_wires):
 
     @qml.qnode(qml.device(QML_DEVICE, wires=n_wires, shots=None))
@@ -129,12 +127,14 @@ def circuit(x1, x2):
 
     return lambda x1, x2: circuit(x1, x2)[0]  # |0..0> state probability
 
+
 ######################################################################
 # Next, each function destined for remote execution is decorated with ``@ct.electron``, with an
 # `executor `__
 # specified therein. Only tasks that evaluate the simulated quantum kernel should require
 # ``gpu_executor``. For example, we don’t need GPUs to generate our input data:
-# 
+#
+
 
 @ct.electron(executor=cpu_executor)  # lightweight non-quantum task
 def get_split_data(n_samples=18, test_size=0.2):
@@ -149,13 +149,20 @@ def get_split_data(n_samples=18, test_size=0.2):
     # X_train, X_test, y_train, y_test
     return train_test_split(X, y, test_size=test_size, random_state=3)
 
+
 ######################################################################
 # Classifying with the SVM, on the other hand, requires :math:`O(n^2)` kernel evaluations, where
 # :math:`n` is the dataset size. Accordingly, we’ll use GPUs (i.e. ``gpu_executor``) to speed up this
 # process.
-# 
+#
+
+DISP_SETTINGS = {
+    "grid_resolution": 50,
+    "response_method": "predict",
+    "alpha": 0.5,
+    "cmap": plt.cm.RdBu,
+}
 
-DISP_SETTINGS = {"grid_resolution": 50, "response_method": "predict", "alpha": 0.5, "cmap": plt.cm.RdBu}
 
 @ct.electron(executor=gpu_executor)
 def classify_with_qsvm(Xtr, Xte, ytr, yte):
@@ -176,34 +183,37 @@ def classify_with_qsvm(Xtr, Xte, ytr, yte):
 
     return accuracy_tr, accuracy_te, disp
 
+
 ######################################################################
 # Putting it all together, we can define a QSVM training and testing workflow. This special function
 # gets decorated with ``@ct.lattice``.
-# 
+#
+
 
 @ct.lattice(workflow_executor=cpu_executor, executor=cpu_executor)
 def run_qsvm(n_samples, test_size):
     Xtr, Xte, ytr, yte = get_split_data(n_samples, test_size)
     return classify_with_qsvm(Xtr, Xte, ytr, yte)
 
+
 ######################################################################
 # Now, to dispatch ``run_qsvm`` to Covalent Cloud, we call it after wrapping with ``ct.dispatch``, as
 # usual.
-# 
+#
 
 dispatch_id = cc.dispatch(run_qsvm)(n_samples=64, test_size=0.2)
 print("Dispatch ID:", dispatch_id)
 
 ######################################################################
 # .. rst-class :: sphx-glr-script-out
-# 
+#
 # .. code-block: none
-# 
+#
 #    Dispatch ID: 0b5d3a08-fe9c-4dc2-910b-0be6eb925663
 
 ######################################################################
 # Here’s what we get when we query and display the results.
-# 
+#
 
 result = cc.get_result(dispatch_id, wait=True)
 result.result.load()
@@ -216,14 +226,14 @@ def run_qsvm(n_samples, test_size):
 
 ######################################################################
 # .. rst-class :: sphx-glr-script-out
-# 
+#
 # .. code-block: none
-# 
+#
 #    Train accuracy: 64.7%
 #    Test accuracy: 76.9%# .. rst-class :: sphx-glr-script-out
-# 
+#
 # .. code-block: none
-# 
+#
 #
 # .. figure:: ../demonstrations/covalent_cloud_gpu/covalent_cloud_gpu_19_1.png
 #    :align: center
@@ -232,24 +242,24 @@ def run_qsvm(n_samples, test_size):
 ######################################################################
 # Conclusion
 # ==========
-# 
+#
 # In this tutorial, we demonstrated how to run quantum circuit simulations on GPUs via Covalent Cloud.
 # We used Pennylane to define a simple quantum kernel, and then trained and tested a QSVM on a
 # 2-dimensional dataset. To make the most of this tutorial, try experimenting with different
 # datasets/kernels or increasing the dataset dimension, to gain a greater advantage from GPU
 # acceleration.
-# 
+#
 # The cost of running this workflow is approximately $0.27. The full code is available below.
-# 
+#
 
 ######################################################################
 # Full Code
 # ---------
-# 
+#
 
 ######################################################################
 # .. code:: python
-# 
+#
 #    import covalent as ct
 #    import covalent_cloud as cc
 #    import matplotlib.pyplot as plt
@@ -260,9 +270,9 @@ def run_qsvm(n_samples, test_size):
 #    from sklearn.inspection import DecisionBoundaryDisplay
 #    from sklearn.model_selection import train_test_split
 #    from sklearn.svm import SVC
-# 
+#
 #    cc.save_api_key("API_KEY")
-# 
+#
 #    cc.create_env(
 #        name="pennylane-gpu",  # identifier for referring to this environment
 #        conda={
@@ -278,7 +288,7 @@ def run_qsvm(n_samples, test_size):
 #        ],
 #        wait=True
 #    )
-# 
+#
 #    cpu_executor = cc.CloudExecutor(  # for lightweight non-quantum tasks
 #        env="pennylane-gpu",
 #        num_cpus=2,
@@ -291,19 +301,19 @@ def run_qsvm(n_samples, test_size):
 #        num_gpus=1,
 #        gpu_type="v100"
 #    )
-# 
+#
 #    QML_DEVICE = "lightning.gpu"
-# 
+#
 #    def get_kernel_circuit(n_wires):
-# 
+#
 #        @qml.qnode(qml.device(QML_DEVICE, wires=n_wires, shots=None))
 #        def circuit(x1, x2):
 #            qml.IQPEmbedding(x1, wires=range(n_wires), n_repeats=4)
 #            qml.adjoint(qml.IQPEmbedding)(x2, wires=range(n_wires), n_repeats=4)
 #            return qml.probs(wires=range(n_wires))
-# 
+#
 #        return lambda x1, x2: circuit(x1, x2)[0]  # |0..0> state probability
-# 
+#
 #    @ct.electron(executor=cpu_executor)  # lightweight non-quantum task
 #    def get_split_data(n_samples=18, test_size=0.2):
 #        centers = [(1, 1), (1, 2), (1, 3), (2, 1), (2, 2), (2, 3), (3, 1), (3, 2), (3, 3)]
@@ -313,43 +323,43 @@ def run_qsvm(n_samples, test_size):
 #        y = np.array([mapping[i] for i in y])
 #        X = X.astype(np.float32)
 #        y = y.astype(int)
-# 
+#
 #        # X_train, X_test, y_train, y_test
 #        return train_test_split(X, y, test_size=test_size, random_state=3)
-# 
+#
 #    DISP_SETTINGS = {"grid_resolution": 50, "response_method": "predict", "alpha": 0.5, "cmap": plt.cm.RdBu}
-# 
+#
 #    @ct.electron(executor=gpu_executor)
 #    def classify_with_qsvm(Xtr, Xte, ytr, yte):
 #        kernel = get_kernel_circuit(n_wires=Xtr.shape[1])
-# 
+#
 #        kernel_matrix_fn = lambda X, Z: qml.kernels.kernel_matrix(X, Z, kernel)
 #        svc = SVC(kernel=kernel_matrix_fn).fit(Xtr, ytr)
-# 
+#
 #        # train/test accuracy
 #        accuracy_tr = svc.score(Xtr, ytr)
 #        accuracy_te = svc.score(Xte, yte)
-# 
+#
 #        # decision boundary
 #        cm_bright = ListedColormap(["#FF0000", "#0000FF"])
 #        disp = DecisionBoundaryDisplay.from_estimator(svc, Xte, **DISP_SETTINGS)
 #        disp.ax_.scatter(Xtr[:, 0], Xtr[:, 1], c=ytr, cmap=cm_bright)
 #        disp.ax_.scatter(Xte[:, 0], Xte[:, 1], c=yte, cmap=cm_bright, marker="$\u25EF$")
-# 
+#
 #        return accuracy_tr, accuracy_te, disp
-# 
+#
 #    @ct.lattice(workflow_executor=cpu_executor, executor=cpu_executor)
 #    def run_qsvm(n_samples, test_size):
 #        Xtr, Xte, ytr, yte = get_split_data(n_samples, test_size)
 #        return classify_with_qsvm(Xtr, Xte, ytr, yte)
-# 
+#
 #    dispatch_id = cc.dispatch(run_qsvm)(n_samples=64, test_size=0.2)
 #    print("Dispatch ID:", dispatch_id)
-# 
+#
 #    result = cc.get_result(dispatch_id, wait=True)
 #    result.result.load()
-# 
+#
 #    train_acc, test_acc, decision_boundary_figure = result.result.value
 #    print(f"Train accuracy: {train_acc * 100:.1f}%")
 #    print(f"Test accuracy: {test_acc * 100:.1f}%")
-# 
\ No newline at end of file
+#

From 53da22726d88d9c064da625310dcd0dee47e1ac2 Mon Sep 17 00:00:00 2001
From: Sankalp Sanand 
Date: Tue, 14 May 2024 18:50:26 +0530
Subject: [PATCH 05/18] title update and link addition

---
 demonstrations/covalent_cloud_gpu.metadata.json |  2 +-
 demonstrations/covalent_cloud_gpu.py            | 11 ++++++-----
 2 files changed, 7 insertions(+), 6 deletions(-)

diff --git a/demonstrations/covalent_cloud_gpu.metadata.json b/demonstrations/covalent_cloud_gpu.metadata.json
index e3b1bbe45e..bdab9dd9cf 100644
--- a/demonstrations/covalent_cloud_gpu.metadata.json
+++ b/demonstrations/covalent_cloud_gpu.metadata.json
@@ -1,5 +1,5 @@
 {
-    "title": "Classification with GPU-quantum embedding kernels on Covalent Cloud",
+    "title": "Running GPU-accelerated quantum circuit simulations on Covalent Cloud using PennyLane",
     "authors": [
         {
             "id": "",
diff --git a/demonstrations/covalent_cloud_gpu.py b/demonstrations/covalent_cloud_gpu.py
index fe93ab14b0..ac49772298 100644
--- a/demonstrations/covalent_cloud_gpu.py
+++ b/demonstrations/covalent_cloud_gpu.py
@@ -1,8 +1,12 @@
-r"""Classification with GPU-quantum embedding kernels on Covalent Cloud
+r"""Running GPU-accelerated quantum circuit simulations on Covalent Cloud using PennyLane
 ===================================================================
 """
 
 ######################################################################
+
+# In this tutorial, we’ll demonstrate how to run GPU-accelerated quantum circuit simulations on
+# `Covalent Cloud `__.
+
 # Quantum support-vector machines (QSVMs) are essentially `traditional
 # SVMs `__ that rely on `embedding
 # kernels `__ evaluated on
@@ -16,9 +20,6 @@
 # noise (an important reason for their current popularity), evaluating kernels on *real* quantum
 # hardware is not always practical nor necessary.
 #
-# In this tutorial, we’ll demonstrate how to run GPU-accelerated quantum circuit simulations on
-# Covalent Cloud.
-#
 
 ######################################################################
 # Let’s start by importing the required packages.
@@ -65,7 +66,7 @@
 #
 # .. code-block: none
 #
-#    Waiting for environment pennylane-gpu to be ready............................................................................................................................................................................
+#    Waiting for environment pennylane-gpu to be ready..................
 #    Name: pennylane-gpu
 #    Status: READY
 #    Estimated Time: 1002 seconds

From 55c0ab0815e53126b3c02bd75cf4304a51cda778 Mon Sep 17 00:00:00 2001
From: Sankalp Sanand 
Date: Tue, 14 May 2024 18:56:20 +0530
Subject: [PATCH 06/18] updated more text

---
 demonstrations/covalent_cloud_gpu.py | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/demonstrations/covalent_cloud_gpu.py b/demonstrations/covalent_cloud_gpu.py
index ac49772298..64b73dad90 100644
--- a/demonstrations/covalent_cloud_gpu.py
+++ b/demonstrations/covalent_cloud_gpu.py
@@ -4,8 +4,9 @@
 
 ######################################################################
 
-# In this tutorial, we’ll demonstrate how to run GPU-accelerated quantum circuit simulations on
-# `Covalent Cloud `__.
+# In this tutorial, we’ll demonstrate how to run GPU-accelerated quantum circuit simulations
+# on `Covalent Cloud `__ using PennyLane. We will focus on a specific example around
+# quantum SVM to demonstrate how easy  it is to  run GPU accelerated quantum circuit simulation on Covalent Cloud.
 
 # Quantum support-vector machines (QSVMs) are essentially `traditional
 # SVMs `__ that rely on `embedding

From 94c6291617fe3e916939d086d9a38f5a6d96a5ae Mon Sep 17 00:00:00 2001
From: Sankalp Sanand 
Date: Fri, 17 May 2024 17:09:59 +0530
Subject: [PATCH 07/18] added bio and image

---
 _static/authors/ara_ghukasyan.jpeg              | Bin 0 -> 63812 bytes
 _static/authors/ara_ghukasyan.txt               |   6 ++++++
 demonstrations/covalent_cloud_gpu.metadata.json |   6 ++----
 demonstrations/covalent_cloud_gpu.py            |   5 +++++
 4 files changed, 13 insertions(+), 4 deletions(-)
 create mode 100644 _static/authors/ara_ghukasyan.jpeg
 create mode 100644 _static/authors/ara_ghukasyan.txt

diff --git a/_static/authors/ara_ghukasyan.jpeg b/_static/authors/ara_ghukasyan.jpeg
new file mode 100644
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diff --git a/_static/authors/ara_ghukasyan.txt b/_static/authors/ara_ghukasyan.txt
new file mode 100644
index 0000000000..f7295c4a64
--- /dev/null
+++ b/_static/authors/ara_ghukasyan.txt
@@ -0,0 +1,6 @@
+.. bio:: Ara Ghukasyan
+   :photo: ../_static/authors/ara_ghukasyan.jpeg
+
+   Ara is a Research Software Engineer at Agnostiq Inc. He has a B.Sc. in Math & Physics and a Ph.D. in Engineering Physics from McMaster University in Hamilton, Ontario. Ara’s interests include Machine Learning, Physics, and Quantum Computing.
+
+
diff --git a/demonstrations/covalent_cloud_gpu.metadata.json b/demonstrations/covalent_cloud_gpu.metadata.json
index bdab9dd9cf..78578f6784 100644
--- a/demonstrations/covalent_cloud_gpu.metadata.json
+++ b/demonstrations/covalent_cloud_gpu.metadata.json
@@ -2,12 +2,10 @@
     "title": "Running GPU-accelerated quantum circuit simulations on Covalent Cloud using PennyLane",
     "authors": [
         {
-            "id": "",
-            "affiliation": ""
+            "id": "ara_ghukasyan"
         },
         {
-            "id": "",
-            "affiliation": ""
+            "id": "santosh_kumar_radha"
         }
     ],
     "dateOfPublication": "",
diff --git a/demonstrations/covalent_cloud_gpu.py b/demonstrations/covalent_cloud_gpu.py
index 64b73dad90..342b65975f 100644
--- a/demonstrations/covalent_cloud_gpu.py
+++ b/demonstrations/covalent_cloud_gpu.py
@@ -365,3 +365,8 @@ def run_qsvm(n_samples, test_size):
 #    print(f"Train accuracy: {train_acc * 100:.1f}%")
 #    print(f"Test accuracy: {test_acc * 100:.1f}%")
 #
+
+##############################################################################
+# About the author
+# ----------------
+# 

From 4d09dc115967bcd29f26aa28f28d988d7009740c Mon Sep 17 00:00:00 2001
From: Sankalp Sanand 
Date: Fri, 17 May 2024 17:56:26 +0530
Subject: [PATCH 08/18] updated json metadata

---
 .../covalent_cloud_gpu.metadata.json          | 54 ++++---------------
 1 file changed, 9 insertions(+), 45 deletions(-)

diff --git a/demonstrations/covalent_cloud_gpu.metadata.json b/demonstrations/covalent_cloud_gpu.metadata.json
index 78578f6784..39ffc00502 100644
--- a/demonstrations/covalent_cloud_gpu.metadata.json
+++ b/demonstrations/covalent_cloud_gpu.metadata.json
@@ -10,7 +10,9 @@
     ],
     "dateOfPublication": "",
     "dateOfLastModification": "",
-    "categories": [],
+    "categories": [
+        "Quantum Machine Learning"
+    ],
     "tags": [],
     "previewImages": [
         {
@@ -18,56 +20,18 @@
             "uri": ""
         }
     ],
-    "seoDescription": "",
+    "seoDescription": "Explore the process of running GPU-accelerated quantum circuit simulations on Covalent Cloud using PennyLane.",
     "doi": "",
-    "canonicalURL": "",
-    "references": [
-        {
-            "id": "",
-            "type": "",
-            "title": "",
-            "authors": "",
-            "year": "",
-            "journal": "",
-            "url": ""
-        },
-        {
-            "id": "",
-            "type": "",
-            "title": "",
-            "authors": "",
-            "year": "",
-            "journal": "",
-            "url": ""
-        },
-        {
-            "id": "",
-            "type": "",
-            "title": "",
-            "authors": "",
-            "year": "",
-            "journal": "",
-            "url": ""
-        }
-    ],
+    "canonicalURL": "/qml/demos/covalent_cloud_gpu",
+    "references": [],
     "basedOnPapers": [],
-    "referencedByPapers": [
-        {
-            "type": "demonstration",
-            "id": "",
-            "weight": 1.0
-        },
-        {
-            "type": "demonstration",
-            "id": "",
-            "weight": 1.0
-        },
+    "referencedByPapers": [],
+    "relatedContent": [
         {
             "type": "demonstration",
-            "id": "",
+            "id": "tutorial_univariate_qvr",
             "weight": 1.0
         }
     ],
-    "relatedContent": [],
     "hardware": []
 }

From 9e08ee5afb816deb3832fad1c3f41a74abc658db Mon Sep 17 00:00:00 2001
From: Sankalp Sanand 
Date: Fri, 17 May 2024 18:01:40 +0530
Subject: [PATCH 09/18] commit to start the build

---
 demonstrations/covalent_cloud_gpu.metadata.json | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/demonstrations/covalent_cloud_gpu.metadata.json b/demonstrations/covalent_cloud_gpu.metadata.json
index 39ffc00502..81293b38f1 100644
--- a/demonstrations/covalent_cloud_gpu.metadata.json
+++ b/demonstrations/covalent_cloud_gpu.metadata.json
@@ -16,7 +16,7 @@
     "tags": [],
     "previewImages": [
         {
-            "type": "thumbnail",
+            "type": "",
             "uri": ""
         }
     ],

From d1380c2bdf0766d25fdb99ca4cc173957d5f1fe7 Mon Sep 17 00:00:00 2001
From: Sankalp Sanand 
Date: Sat, 18 May 2024 12:38:31 +0530
Subject: [PATCH 10/18] implemented suggestions

---
 .../covalent_cloud_gpu_19_1.png                 | Bin
 .../thumbnail_covalent_cloud_gpu.png            | Bin 0 -> 20147 bytes
 .../thumbnail_large_covalent_cloud_gpu.png      | Bin 0 -> 82619 bytes
 demonstrations/covalent_cloud_gpu.metadata.json |  12 ++++++++----
 demonstrations/covalent_cloud_gpu.py            |   4 ++--
 5 files changed, 10 insertions(+), 6 deletions(-)
 rename {demonstrations => _static/demonstration_assets}/covalent_cloud_gpu/covalent_cloud_gpu_19_1.png (100%)
 create mode 100644 _static/demonstration_assets/covalent_cloud_gpu/thumbnail_covalent_cloud_gpu.png
 create mode 100644 _static/large_demo_thumbnails/thumbnail_large_covalent_cloud_gpu.png

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similarity index 100%
rename from demonstrations/covalent_cloud_gpu/covalent_cloud_gpu_19_1.png
rename to _static/demonstration_assets/covalent_cloud_gpu/covalent_cloud_gpu_19_1.png
diff --git a/_static/demonstration_assets/covalent_cloud_gpu/thumbnail_covalent_cloud_gpu.png b/_static/demonstration_assets/covalent_cloud_gpu/thumbnail_covalent_cloud_gpu.png
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OY;zN9++$-x#D4&bfhKhT

literal 0
HcmV?d00001

diff --git a/demonstrations/covalent_cloud_gpu.metadata.json b/demonstrations/covalent_cloud_gpu.metadata.json
index 81293b38f1..dad273e414 100644
--- a/demonstrations/covalent_cloud_gpu.metadata.json
+++ b/demonstrations/covalent_cloud_gpu.metadata.json
@@ -8,16 +8,20 @@
             "id": "santosh_kumar_radha"
         }
     ],
-    "dateOfPublication": "",
-    "dateOfLastModification": "",
+    "dateOfPublication": "2024-03-18T00:00:00+00:00",
+    "dateOfLastModification": "2024-03-18T00:00:00+00:00",
     "categories": [
         "Quantum Machine Learning"
     ],
     "tags": [],
     "previewImages": [
         {
-            "type": "",
-            "uri": ""
+            "type": "thumbnail",
+            "uri": "/_static/demonstration_assets/covalent_cloud_gpu/thumbnail_covalent_cloud_gpu.png"
+        },
+        {
+            "type": "large_thumbnail",
+            "uri": "/_static/large_demo_thumbnails/thumbnail_large_covalent_cloud_gpu.png"
         }
     ],
     "seoDescription": "Explore the process of running GPU-accelerated quantum circuit simulations on Covalent Cloud using PennyLane.",
diff --git a/demonstrations/covalent_cloud_gpu.py b/demonstrations/covalent_cloud_gpu.py
index 342b65975f..c35c0a7e1a 100644
--- a/demonstrations/covalent_cloud_gpu.py
+++ b/demonstrations/covalent_cloud_gpu.py
@@ -237,9 +237,9 @@ def run_qsvm(n_samples, test_size):
 # .. code-block: none
 #
 #
-# .. figure:: ../demonstrations/covalent_cloud_gpu/covalent_cloud_gpu_19_1.png
+# .. figure:: ../_static/demonstration_assets/covalent_cloud_gpu/covalent_cloud_gpu_19_1.png
 #    :align: center
-#    :width: 80%
+#    :width: 90%
 
 ######################################################################
 # Conclusion

From cd5e1039a64803e3cbfc0d83a79431fce262f23e Mon Sep 17 00:00:00 2001
From: Sankalp Sanand 
Date: Sat, 18 May 2024 14:20:18 +0530
Subject: [PATCH 11/18] updating to 79 hashes

---
 demonstrations/covalent_cloud_gpu.py | 32 ++++++++++++++--------------
 1 file changed, 16 insertions(+), 16 deletions(-)

diff --git a/demonstrations/covalent_cloud_gpu.py b/demonstrations/covalent_cloud_gpu.py
index c35c0a7e1a..14726c6717 100644
--- a/demonstrations/covalent_cloud_gpu.py
+++ b/demonstrations/covalent_cloud_gpu.py
@@ -2,7 +2,7 @@
 ===================================================================
 """
 
-######################################################################
+##############################################################################
 
 # In this tutorial, we’ll demonstrate how to run GPU-accelerated quantum circuit simulations
 # on `Covalent Cloud `__ using PennyLane. We will focus on a specific example around
@@ -22,7 +22,7 @@
 # hardware is not always practical nor necessary.
 #
 
-######################################################################
+##############################################################################
 # Let’s start by importing the required packages.
 #
 
@@ -39,7 +39,7 @@
 
 # cc.save_api_key("YOUR_API_KEY")
 
-######################################################################
+##############################################################################
 # Covalent Cloud allows us to create `re-usable execution
 # environments `__, as shown
 # below. This environment represents a typical setup for running Pennylane on NVIDIA GPUs.
@@ -62,7 +62,7 @@
     wait=True,
 )
 
-######################################################################
+##############################################################################
 # .. rst-class :: sphx-glr-script-out
 #
 # .. code-block: none
@@ -93,7 +93,7 @@
 #    name: pennylane-gpu
 #
 
-######################################################################
+##############################################################################
 # Next, we’ll define our resource specifications by creating some
 # `executors `__
 # for this workflow. Both executors will run tasks in our new environment, named ``”pennylane-gpu”``.
@@ -108,7 +108,7 @@
     env="pennylane-gpu", num_cpus=4, memory="12GB", num_gpus=1, gpu_type="v100"
 )
 
-######################################################################
+##############################################################################
 # On to the algorithm!
 #
 # Here’s a function returns a simple quantum kernel based on Pennylane’s `IQP
@@ -130,7 +130,7 @@ def circuit(x1, x2):
     return lambda x1, x2: circuit(x1, x2)[0]  # |0..0> state probability
 
 
-######################################################################
+##############################################################################
 # Next, each function destined for remote execution is decorated with ``@ct.electron``, with an
 # `executor `__
 # specified therein. Only tasks that evaluate the simulated quantum kernel should require
@@ -152,7 +152,7 @@ def get_split_data(n_samples=18, test_size=0.2):
     return train_test_split(X, y, test_size=test_size, random_state=3)
 
 
-######################################################################
+##############################################################################
 # Classifying with the SVM, on the other hand, requires :math:`O(n^2)` kernel evaluations, where
 # :math:`n` is the dataset size. Accordingly, we’ll use GPUs (i.e. ``gpu_executor``) to speed up this
 # process.
@@ -186,7 +186,7 @@ def classify_with_qsvm(Xtr, Xte, ytr, yte):
     return accuracy_tr, accuracy_te, disp
 
 
-######################################################################
+##############################################################################
 # Putting it all together, we can define a QSVM training and testing workflow. This special function
 # gets decorated with ``@ct.lattice``.
 #
@@ -198,7 +198,7 @@ def run_qsvm(n_samples, test_size):
     return classify_with_qsvm(Xtr, Xte, ytr, yte)
 
 
-######################################################################
+##############################################################################
 # Now, to dispatch ``run_qsvm`` to Covalent Cloud, we call it after wrapping with ``ct.dispatch``, as
 # usual.
 #
@@ -206,14 +206,14 @@ def run_qsvm(n_samples, test_size):
 dispatch_id = cc.dispatch(run_qsvm)(n_samples=64, test_size=0.2)
 print("Dispatch ID:", dispatch_id)
 
-######################################################################
+##############################################################################
 # .. rst-class :: sphx-glr-script-out
 #
 # .. code-block: none
 #
 #    Dispatch ID: 0b5d3a08-fe9c-4dc2-910b-0be6eb925663
 
-######################################################################
+##############################################################################
 # Here’s what we get when we query and display the results.
 #
 
@@ -226,7 +226,7 @@ def run_qsvm(n_samples, test_size):
 
 decision_boundary_figure
 
-######################################################################
+##############################################################################
 # .. rst-class :: sphx-glr-script-out
 #
 # .. code-block: none
@@ -241,7 +241,7 @@ def run_qsvm(n_samples, test_size):
 #    :align: center
 #    :width: 90%
 
-######################################################################
+##############################################################################
 # Conclusion
 # ==========
 #
@@ -254,12 +254,12 @@ def run_qsvm(n_samples, test_size):
 # The cost of running this workflow is approximately $0.27. The full code is available below.
 #
 
-######################################################################
+##############################################################################
 # Full Code
 # ---------
 #
 
-######################################################################
+##############################################################################
 # .. code:: python
 #
 #    import covalent as ct

From 756b5814a463ff14aec45fa8f14a255e32ec5744 Mon Sep 17 00:00:00 2001
From: Sankalp Sanand 
Date: Tue, 21 May 2024 07:14:59 -0400
Subject: [PATCH 12/18] Apply suggestions from code review

Co-authored-by: Josh Izaac 
---
 demonstrations/covalent_cloud_gpu.py | 71 +++++++++++++---------------
 1 file changed, 34 insertions(+), 37 deletions(-)

diff --git a/demonstrations/covalent_cloud_gpu.py b/demonstrations/covalent_cloud_gpu.py
index 14726c6717..da2e80bb55 100644
--- a/demonstrations/covalent_cloud_gpu.py
+++ b/demonstrations/covalent_cloud_gpu.py
@@ -3,11 +3,11 @@
 """
 
 ##############################################################################
-
+#
 # In this tutorial, we’ll demonstrate how to run GPU-accelerated quantum circuit simulations
 # on `Covalent Cloud `__ using PennyLane. We will focus on a specific example around
 # quantum SVM to demonstrate how easy  it is to  run GPU accelerated quantum circuit simulation on Covalent Cloud.
-
+#
 # Quantum support-vector machines (QSVMs) are essentially `traditional
 # SVMs `__ that rely on `embedding
 # kernels `__ evaluated on
@@ -65,32 +65,32 @@
 ##############################################################################
 # .. rst-class :: sphx-glr-script-out
 #
-# .. code-block: none
-#
-#    Waiting for environment pennylane-gpu to be ready..................
-#    Name: pennylane-gpu
-#    Status: READY
-#    Estimated Time: 1002 seconds
-#    Notes:
-#    	pip was added to the dependencies.
-#    	Python version 3.10 was added to the dependencies.
-#    Environment file contains:
-#    ==========================
-#    channels:
-#    - conda-forge
-#    dependencies:
-#    - python=3.10
-#    - pip
-#    - cudatoolkit>=11.8
-#    - pip:
-#      - cuquantum==23.10.0
-#      - matplotlib==3.8.2
-#      - Pennylane==0.34.0
-#      - PennyLane-Lightning[GPU]==0.34.0
-#      - scikit-learn==1.3.1
-#      - torch==2.1.2
-#      - covalent-cloud
-#    name: pennylane-gpu
+#  .. code-block: none
+#
+#     Waiting for environment pennylane-gpu to be ready..................
+#     Name: pennylane-gpu
+#     Status: READY
+#     Estimated Time: 1002 seconds
+#     Notes:
+#     	pip was added to the dependencies.
+#     	Python version 3.10 was added to the dependencies.
+#     Environment file contains:
+#     ==========================
+#     channels:
+#     - conda-forge
+#     dependencies:
+#     - python=3.10
+#     - pip
+#     - cudatoolkit>=11.8
+#     - pip:
+#       - cuquantum==23.10.0
+#       - matplotlib==3.8.2
+#       - Pennylane==0.34.0
+#       - PennyLane-Lightning[GPU]==0.34.0
+#       - scikit-learn==1.3.1
+#       - torch==2.1.2
+#       - covalent-cloud
+#     name: pennylane-gpu
 #
 
 ##############################################################################
@@ -209,9 +209,9 @@ def run_qsvm(n_samples, test_size):
 ##############################################################################
 # .. rst-class :: sphx-glr-script-out
 #
-# .. code-block: none
+#  .. code-block: none
 #
-#    Dispatch ID: 0b5d3a08-fe9c-4dc2-910b-0be6eb925663
+#     Dispatch ID: 0b5d3a08-fe9c-4dc2-910b-0be6eb925663
 
 ##############################################################################
 # Here’s what we get when we query and display the results.
@@ -229,13 +229,10 @@ def run_qsvm(n_samples, test_size):
 ##############################################################################
 # .. rst-class :: sphx-glr-script-out
 #
-# .. code-block: none
-#
-#    Train accuracy: 64.7%
-#    Test accuracy: 76.9%# .. rst-class :: sphx-glr-script-out
-#
-# .. code-block: none
+#  .. code-block: none
 #
+#     Train accuracy: 64.7%
+#     Test accuracy: 76.9%
 #
 # .. figure:: ../_static/demonstration_assets/covalent_cloud_gpu/covalent_cloud_gpu_19_1.png
 #    :align: center
@@ -251,7 +248,7 @@ def run_qsvm(n_samples, test_size):
 # datasets/kernels or increasing the dataset dimension, to gain a greater advantage from GPU
 # acceleration.
 #
-# The cost of running this workflow is approximately $0.27. The full code is available below.
+# The cost of running this workflow is approximately $0.27$. The full code is available below.
 #
 
 ##############################################################################

From fe130847ec6f0cfb8eedfdfbc583ab55b50256b2 Mon Sep 17 00:00:00 2001
From: Sankalp Sanand 
Date: Tue, 21 May 2024 19:26:13 +0530
Subject: [PATCH 13/18] updated dollar sign rendering

---
 demonstrations/covalent_cloud_gpu.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/demonstrations/covalent_cloud_gpu.py b/demonstrations/covalent_cloud_gpu.py
index da2e80bb55..d1f63c37b1 100644
--- a/demonstrations/covalent_cloud_gpu.py
+++ b/demonstrations/covalent_cloud_gpu.py
@@ -248,7 +248,7 @@ def run_qsvm(n_samples, test_size):
 # datasets/kernels or increasing the dataset dimension, to gain a greater advantage from GPU
 # acceleration.
 #
-# The cost of running this workflow is approximately $0.27$. The full code is available below.
+# The cost of running this workflow is approximately \$0.27. The full code is available below.
 #
 
 ##############################################################################

From 2f72c10f07c2a8f65e2c3ed1614c589321be7cd3 Mon Sep 17 00:00:00 2001
From: Sankalp Sanand 
Date: Tue, 21 May 2024 11:44:26 -0400
Subject: [PATCH 14/18] Update demonstrations/covalent_cloud_gpu.py

Co-authored-by: Josh Izaac 
---
 demonstrations/covalent_cloud_gpu.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/demonstrations/covalent_cloud_gpu.py b/demonstrations/covalent_cloud_gpu.py
index d1f63c37b1..f7a1efede1 100644
--- a/demonstrations/covalent_cloud_gpu.py
+++ b/demonstrations/covalent_cloud_gpu.py
@@ -240,7 +240,7 @@ def run_qsvm(n_samples, test_size):
 
 ##############################################################################
 # Conclusion
-# ==========
+# ----------
 #
 # In this tutorial, we demonstrated how to run quantum circuit simulations on GPUs via Covalent Cloud.
 # We used Pennylane to define a simple quantum kernel, and then trained and tested a QSVM on a

From fd904ea1dc1c5154106e8ac3a2ac72ee55e8058c Mon Sep 17 00:00:00 2001
From: Sankalp Sanand 
Date: Thu, 23 May 2024 17:25:34 +0530
Subject: [PATCH 15/18] Updated date

---
 demonstrations/covalent_cloud_gpu.metadata.json | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/demonstrations/covalent_cloud_gpu.metadata.json b/demonstrations/covalent_cloud_gpu.metadata.json
index dad273e414..04a85d06f7 100644
--- a/demonstrations/covalent_cloud_gpu.metadata.json
+++ b/demonstrations/covalent_cloud_gpu.metadata.json
@@ -8,8 +8,8 @@
             "id": "santosh_kumar_radha"
         }
     ],
-    "dateOfPublication": "2024-03-18T00:00:00+00:00",
-    "dateOfLastModification": "2024-03-18T00:00:00+00:00",
+    "dateOfPublication": "2024-05-23T00:00:00+00:00",
+    "dateOfLastModification": "2024-05-23T00:00:00+00:00",
     "categories": [
         "Quantum Machine Learning"
     ],

From 92d0eaff8a19c4bba525a00aff1db9351650acab Mon Sep 17 00:00:00 2001
From: Josh Izaac 
Date: Thu, 23 May 2024 18:09:39 -0400
Subject: [PATCH 16/18] Update demonstrations/covalent_cloud_gpu.metadata.json

---
 demonstrations/covalent_cloud_gpu.metadata.json | 8 +++++++-
 1 file changed, 7 insertions(+), 1 deletion(-)

diff --git a/demonstrations/covalent_cloud_gpu.metadata.json b/demonstrations/covalent_cloud_gpu.metadata.json
index 04a85d06f7..2c9dc490d3 100644
--- a/demonstrations/covalent_cloud_gpu.metadata.json
+++ b/demonstrations/covalent_cloud_gpu.metadata.json
@@ -37,5 +37,11 @@
             "weight": 1.0
         }
     ],
-    "hardware": []
+    "hardware": [
+        {
+            "id": "covalent",
+            "link": "https://docs.covalent.xyz/docs/cloud/tutorials-cloud/pennylane_tutorial/",
+            "logo": "/_static/hardware_logos/covalent.png"
+        }
+    ]
 }

From f8ba616fb92a3ddc007e5a2347b1feddc8a27751 Mon Sep 17 00:00:00 2001
From: Sankalp Sanand 
Date: Fri, 24 May 2024 13:54:54 -0400
Subject: [PATCH 17/18] Apply suggestions from code review
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

Co-authored-by: Ivana Kurečić 
---
 _static/authors/ara_ghukasyan.txt              |  2 +-
 .../covalent_cloud_gpu.metadata.json           | 18 ++++++++++++++----
 demonstrations/covalent_cloud_gpu.py           | 15 +++++++++------
 3 files changed, 24 insertions(+), 11 deletions(-)

diff --git a/_static/authors/ara_ghukasyan.txt b/_static/authors/ara_ghukasyan.txt
index f7295c4a64..751deefe4d 100644
--- a/_static/authors/ara_ghukasyan.txt
+++ b/_static/authors/ara_ghukasyan.txt
@@ -1,6 +1,6 @@
 .. bio:: Ara Ghukasyan
    :photo: ../_static/authors/ara_ghukasyan.jpeg
 
-   Ara is a Research Software Engineer at Agnostiq Inc. He has a B.Sc. in Math & Physics and a Ph.D. in Engineering Physics from McMaster University in Hamilton, Ontario. Ara’s interests include Machine Learning, Physics, and Quantum Computing.
+   Ara is a Research Software Engineer at Agnostiq Inc. He has a B.Sc. in Math & Physics and a Ph.D. in Engineering Physics from McMaster University in Hamilton, Ontario. Ara’s interests include machine learning, physics, and quantum computing.
 
 
diff --git a/demonstrations/covalent_cloud_gpu.metadata.json b/demonstrations/covalent_cloud_gpu.metadata.json
index 2c9dc490d3..53d65d671e 100644
--- a/demonstrations/covalent_cloud_gpu.metadata.json
+++ b/demonstrations/covalent_cloud_gpu.metadata.json
@@ -2,14 +2,14 @@
     "title": "Running GPU-accelerated quantum circuit simulations on Covalent Cloud using PennyLane",
     "authors": [
         {
-            "id": "ara_ghukasyan"
+            "id": "santosh_kumar_radha"
         },
         {
-            "id": "santosh_kumar_radha"
+            "id": "ara_ghukasyan"
         }
     ],
-    "dateOfPublication": "2024-05-23T00:00:00+00:00",
-    "dateOfLastModification": "2024-05-23T00:00:00+00:00",
+    "dateOfPublication": "2024-05-24T00:00:00+00:00",
+    "dateOfLastModification": "2024-05-24T00:00:00+00:00",
     "categories": [
         "Quantum Machine Learning"
     ],
@@ -31,6 +31,16 @@
     "basedOnPapers": [],
     "referencedByPapers": [],
     "relatedContent": [
+        {
+            "type": "demonstration",
+            "id": "tutorial_kernels_module",
+            "weight": 1.0
+        },
+        {
+            "type": "demonstration",
+            "id": "tutorial_kernel_based_training",
+            "weight": 1.0
+        },
         {
             "type": "demonstration",
             "id": "tutorial_univariate_qvr",
diff --git a/demonstrations/covalent_cloud_gpu.py b/demonstrations/covalent_cloud_gpu.py
index f7a1efede1..c8dcbef82f 100644
--- a/demonstrations/covalent_cloud_gpu.py
+++ b/demonstrations/covalent_cloud_gpu.py
@@ -6,14 +6,14 @@
 #
 # In this tutorial, we’ll demonstrate how to run GPU-accelerated quantum circuit simulations
 # on `Covalent Cloud `__ using PennyLane. We will focus on a specific example around
-# quantum SVM to demonstrate how easy  it is to  run GPU accelerated quantum circuit simulation on Covalent Cloud.
+# quantum support vector machines (QSVMs) to demonstrate how easy  it is to  run GPU-accelerated quantum circuit simulations on Covalent Cloud.
 #
-# Quantum support-vector machines (QSVMs) are essentially `traditional
+# QSVMs are essentially `traditional
 # SVMs `__ that rely on `embedding
 # kernels `__ evaluated on
 # a quantum computer—a.k.a. `quantum embedding
 # kernel `__.
-# These kernels provide a unique (and perhaps classically-intractable) means of measuring pairwise
+# These kernels provide a unique (and perhaps classically intractable) means of measuring pairwise
 # similarity.
 #
 # Using GPUs to simulate quantum computers is worthwhile when qubit capacity and/or fidelity
@@ -23,6 +23,9 @@
 #
 
 ##############################################################################
+# Implementation
+# --------------
+#
 # Let’s start by importing the required packages.
 #
 
@@ -40,7 +43,7 @@
 # cc.save_api_key("YOUR_API_KEY")
 
 ##############################################################################
-# Covalent Cloud allows us to create `re-usable execution
+# Covalent Cloud allows us to create `reusable execution
 # environments `__, as shown
 # below. This environment represents a typical setup for running Pennylane on NVIDIA GPUs.
 #
@@ -243,12 +246,12 @@ def run_qsvm(n_samples, test_size):
 # ----------
 #
 # In this tutorial, we demonstrated how to run quantum circuit simulations on GPUs via Covalent Cloud.
-# We used Pennylane to define a simple quantum kernel, and then trained and tested a QSVM on a
+# We used PennyLane to define a simple quantum kernel, and then trained and tested a QSVM on a
 # 2-dimensional dataset. To make the most of this tutorial, try experimenting with different
 # datasets/kernels or increasing the dataset dimension, to gain a greater advantage from GPU
 # acceleration.
 #
-# The cost of running this workflow is approximately \$0.27. The full code is available below.
+# The cost of running this workflow is approximately \$0.27. The full code is available below, and you can try it yourself with the _Run on Covalent Cloud_ button in the side menu on the right.
 #
 
 ##############################################################################

From cb7b92d25fd4b249993a14f2e99c8e37da4fd8b9 Mon Sep 17 00:00:00 2001
From: Josh Izaac 
Date: Mon, 27 May 2024 20:12:40 -0400
Subject: [PATCH 18/18] Update demonstrations/covalent_cloud_gpu.py
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

Co-authored-by: Ivana Kurečić 
---
 demonstrations/covalent_cloud_gpu.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/demonstrations/covalent_cloud_gpu.py b/demonstrations/covalent_cloud_gpu.py
index c8dcbef82f..2dbcf02894 100644
--- a/demonstrations/covalent_cloud_gpu.py
+++ b/demonstrations/covalent_cloud_gpu.py
@@ -251,7 +251,7 @@ def run_qsvm(n_samples, test_size):
 # datasets/kernels or increasing the dataset dimension, to gain a greater advantage from GPU
 # acceleration.
 #
-# The cost of running this workflow is approximately \$0.27. The full code is available below, and you can try it yourself with the _Run on Covalent Cloud_ button in the side menu on the right.
+# The cost of running this workflow is approximately \$0.27. The full code is available below, and you can try it yourself with the *Run on Covalent Cloud* button in the side menu on the right.
 #
 
 ##############################################################################