added qiskit examples

iQuHACK · Jan 29, 2023 · 020a64c · 020a64c
1 parent a72ee6c
commit 020a64c
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Showing 3 changed files with 218 additions and 77 deletions.
diff --git a/part1_example_qiskit.ipynb b/part1_example_qiskit.ipynb
@@ -2,18 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Make sure to include this identifier at the beginning of your submission\n",
-    "teamname = 'your_team_name'\n",
-    "task = 'part 1'"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -75,22 +64,7 @@
     "def image_mse(image1,image2):\n",
     "    # Using sklearns mean squared error:\n",
     "    # https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_error.html\n",
-    "    return mean_squared_error(image1, image2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#load the mock data (for testing only)\n",
-    "files=os.listdir(\"mock_data\")\n",
-    "dataset=list()\n",
-    "for file in files:\n",
-    "    with open('mock_data/'+file, \"r\") as infile:\n",
-    "        loaded = json.load(infile)\n",
-    "        dataset.append(loaded)"
+    "    return mean_squared_error(255*image1,255*image2)"
    ]
   },
   {
@@ -101,7 +75,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.image.AxesImage at 0x1318dcb80>"
+       "<matplotlib.image.AxesImage at 0x126d3ebe0>"
       ]
      },
      "execution_count": 3,
@@ -110,7 +84,7 @@
     },
     {
      "data": {
-      "image/png": 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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -122,11 +96,13 @@
     }
    ],
    "source": [
+    "data_path='mock_data'\n",
     "#load the actual hackthon data (fashion-mnist)\n",
-    "images=np.load('data/images.npy')\n",
-    "labels=np.load('data/labels.npy')\n",
+    "images=np.load(data_path+'/images.npy')\n",
+    "labels=np.load(data_path+'/labels.npy')\n",
     "#you can visualize it\n",
-    "plt.imshow(images[1100])"
+    "import matplotlib.pyplot as plt\n",
+    "plt.imshow(images[1])"
    ]
   },
   {
@@ -137,7 +113,7 @@
    "source": [
     "#submission to part 1, you should make this into a .py file\n",
     "\n",
-    "n=len(dataset)\n",
+    "n=len(images)\n",
     "mse=0\n",
     "gatecount=0\n",
     "\n",
@@ -185,11 +161,11 @@
    "source": [
     "#how we grade your submission\n",
     "\n",
-    "n=len(dataset)\n",
+    "n=len(images)\n",
     "mse=0\n",
     "gatecount=0\n",
     "\n",
-    "for data in dataset:\n",
+    "for data in images:\n",
     "    #encode image into circuit\n",
     "    circuit,image_re=run_part1(data['image'])\n",
     "    \n",
@@ -200,7 +176,7 @@
     "    mse+=image_mse(data['image'],image_re)\n",
     "    \n",
     "#fidelity of reconstruction\n",
-    "f=1-mse\n",
+    "f=1-mse/n\n",
     "gatecount=gatecount/n\n",
     "\n",
     "#score for part1 \n",