improved version after CR1

Nadav138 · Jun 26, 2024 · b2f228e · b2f228e
1 parent 330453a
commit b2f228e
Showing 1 changed file with 27 additions and 17 deletions.
diff --git a/tutorials/QML_with_Classiq/QML_with_Classiq.ipynb b/tutorials/QML_with_Classiq/QML_with_Classiq.ipynb
@@ -13,14 +13,13 @@
    "id": "be6ccf91-07a9-42a3-8945-33353d608ab3",
    "metadata": {},
    "source": [
-    "Welcome to the \"Quantum Machine Learning with Classiq\" tutorial. This guide is designed for users already familiar with the Classiq platform, aiming to explore the exciting realm of Quantum Machine Learning (QML) using Classiq. In this tutorial, we will cover three main methods to implement QML with Classiq:\n",
+    "Welcome to the \"Quantum Machine Learning with Classiq\" tutorial. This guide is designed for users already familiar with the fundamentals of the Classiq platform and Quantum Machine Learning (QML) concepts. Our aim is to showcase how to implement QML using Classiq. In this tutorial, we will cover three main methods to implement QML with Classiq:\n",
     "\n",
     "1. **Using the VQE Primitive**\n",
     "2. **Using the PyTorch Integration**\n",
     "3. **Using the QSVM Built-in App**\n",
     "\n",
-    "\n",
-    "Each section will provide a brief explanation of the method followed by a simple, illustrative example to demonstrate the integration. /\n",
+    "Each section will provide a brief explanation of the method followed by a simple, illustrative example to demonstrate the integration. \\\n",
     "These examples are intended to be straightforward to help you get started quickly."
    ]
   },
@@ -97,7 +96,7 @@
     "Let's start with a simple example by creating a VQE algorithm that estimates the minimal eigenvalue of the following 2x2 matrix:\n",
     "\n",
     "<body><center>\n",
-    "$ \\begin{bmatrix} 1 & -1 \\\\ -1 & 0 \\end{bmatrix} = \\frac{1}{2}I + \\frac{1}{2}Z - X $\n",
+    "$ H = \\frac{1}{2}I + \\frac{1}{2}Z - X = \\begin{bmatrix} 1 & -1 \\\\ -1 & 0 \\end{bmatrix} $\n",
     "</center><body>"
    ]
   },
@@ -111,7 +110,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 3,
    "id": "8601b362-1488-4a91-a18e-9f9a1185267a",
    "metadata": {},
    "outputs": [],
@@ -131,7 +130,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 4,
    "id": "aacdd2c8-b92e-4cfa-8314-ff4a9d2a5fef",
    "metadata": {},
    "outputs": [],
@@ -182,7 +181,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 5,
    "id": "e99e3173-0f36-4ad1-91e0-766fa680b2cc",
    "metadata": {},
    "outputs": [],
@@ -203,7 +202,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 6,
    "id": "452706db-e41f-4d02-93aa-722c3b652267",
    "metadata": {},
    "outputs": [],
@@ -224,6 +223,14 @@
     "    save({\"result\": res})"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "2cb58910-b98f-479a-959b-084b567effdb",
+   "metadata": {},
+   "source": [
+    "Note that for our `cmain` function, we are using the `@cfunc` decorator rather than `@qfunc` as we commonly do in the SDK."
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "ce9d46d1-8906-420d-b08e-ac9bf5747acb",
@@ -263,7 +270,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 7,
    "id": "aa96096f-f2b3-4648-9958-f6c68749f00f",
    "metadata": {},
    "outputs": [],
@@ -282,15 +289,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 8,
    "id": "0a491c86-ba25-4099-a7dd-eeb13ae71c40",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Opening: https://platform.classiq.io/circuit/fc2a6eeb-dc3d-4264-8b61-6760ae2612bb?version=0.42.2\n"
+      "Opening: https://platform.classiq.io/circuit/49d3bfcb-b4f3-45e7-a8b8-9dc77e3ef56c?version=0.42.2\n"
      ]
     }
    ],
@@ -308,14 +315,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 9,
    "id": "8d6dc1f0-ad23-4f11-8c64-a827f0ebe97f",
    "metadata": {},
    "outputs": [],
    "source": [
     "res = execute(qprog)\n",
     "# res.open_in_ide()\n",
-    "vqe_result = res.result()[0].value"
+    "vqe_result = res.result()[0].value\n",
+    "last_std_dev = vqe_result.intermediate_results[-1].standard_deviation"
    ]
   },
   {
@@ -328,22 +336,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 10,
    "id": "f3bdc0d5-bd82-4797-b449-b7494101163b",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Optimal energy: -0.6240234375\n",
-      "Optimal parameters: {'angles_0': 4.231154537560012, 'angles_1': 3.147445806511723, 'angles_2': 2.7306574045708336}\n",
-      "Eigenstate: {'0': (0.516334738808072+0j), '1': (0.856386850377795+0j)}\n"
+      "Optimal energy: -0.603515625\n",
+      "Standard Deviation of the last iteration: 0.5886752446817956\n",
+      "Optimal parameters: {'angles_0': -1.911377748595144, 'angles_1': 3.16467642787642, 'angles_2': -1.4600485696049208}\n",
+      "Eigenstate: {'1': (0.8172935519138763+0j), '0': (0.5762215285808054+0j)}\n"
      ]
     }
    ],
    "source": [
     "print(f\"Optimal energy: {vqe_result.energy}\")\n",
+    "print(f\"Standard Deviation of the last iteration: {last_std_dev}\")\n",
     "print(f\"Optimal parameters: {vqe_result.optimal_parameters}\")\n",
     "print(f\"Eigenstate: {vqe_result.eigenstate}\")"
    ]