From 61da8ecf69a371999845b214f20d22b20a948a9d Mon Sep 17 00:00:00 2001
From: OkuyanBoga <mustafaemresahin@outlook.com>
Date: Mon, 9 Dec 2024 16:59:05 +0000
Subject: [PATCH] Fix: Potential Data Leakage in Quantum Data Tutorial.

---
 docs/tutorials/quantum_data.ipynb | 359 ++++++++++++++++--------------
 1 file changed, 190 insertions(+), 169 deletions(-)

diff --git a/docs/tutorials/quantum_data.ipynb b/docs/tutorials/quantum_data.ipynb
index 8877807dc..276b87438 100644
--- a/docs/tutorials/quantum_data.ipynb
+++ b/docs/tutorials/quantum_data.ipynb
@@ -3,7 +3,6 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "colab_type": "text",
     "id": "xLOXFOT5Q40E"
    },
    "source": [
@@ -12,11 +11,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {
     "cellView": "form",
-    "colab": {},
-    "colab_type": "code",
     "id": "iiQkM5ZgQ8r2"
    },
    "outputs": [],
@@ -37,7 +34,6 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "colab_type": "text",
     "id": "j6331ZSsQGY3"
    },
    "source": [
@@ -47,7 +43,6 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "colab_type": "text",
     "id": "i9Jcnb8bQQyd"
    },
    "source": [
@@ -91,25 +86,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
     },
     "id": "X3Y5vLL9K_Ai",
-    "outputId": "60d15a69-5a45-449f-bf63-29a5af8d8ffc"
+    "outputId": "ee9c47bc-35f4-4751-c200-283fe5da9768"
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "WARNING: pip is being invoked by an old script wrapper. This will fail in a future version of pip.\r\n",
-      "Please see https://github.com/pypa/pip/issues/5599 for advice on fixing the underlying issue.\r\n",
-      "To avoid this problem you can invoke Python with '-m pip' instead of running pip directly.\r\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "!pip install tensorflow==2.15.0 tensorflow-quantum==0.7.3"
    ]
@@ -118,9 +103,11 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "4Ql5PW-ACO0J"
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "4Ql5PW-ACO0J",
+    "outputId": "b39b3458-34fb-4334-dfd7-3c7981b3c5a0"
    },
    "outputs": [],
    "source": [
@@ -131,7 +118,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {
     "id": "FTKfetslL5eE"
    },
@@ -174,13 +161,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
     },
     "id": "VTKmzeH3MBvR",
-    "outputId": "cc705254-3db0-4c53-8b4c-e543f69fae31"
+    "outputId": "a08a8f64-7642-4773-bc05-2fa4eaea0b3a"
    },
    "outputs": [
     {
@@ -213,7 +200,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {
     "id": "LmprnNbDP4Z6"
    },
@@ -228,13 +215,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
     },
     "id": "KycvXPllQH-t",
-    "outputId": "7dd10133-1fa3-48ba-e7d9-1cf350107c01"
+    "outputId": "86271e72-784c-4a48-ea92-95409363fe99"
    },
    "outputs": [
     {
@@ -256,14 +243,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
-     "height": 303
+     "height": 470
     },
     "id": "c-2Fx9E1O63h",
-    "outputId": "a8cc82ef-de3a-44ee-a3d9-14b3d30c9758"
+    "outputId": "c90802e2-7c89-4011-82a3-06f81556a545"
    },
    "outputs": [
     {
@@ -276,23 +263,21 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.colorbar.Colorbar at 0x7f6db42c3460>"
+       "<matplotlib.colorbar.Colorbar at 0x787cc7261f90>"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 67,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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uWeb9u+EvPzCO2XDVOuMYSUr1JhvHVB68yjhmS4F5UY7jNgrcDEv+0jhGkjos8/51B83fVt9oH2ocs//6DOMYScqttxXmWImywIxkDQBwraAivN2oS65Zu+MjBQAACYyRNQDAtawIV4NbLhlZk6wBAK5F1S0AABwuURaYuaOXAAAkMEbWAADXYhocAACHS5TbjTINDgCAwzGyBgC4FtPgAAA4XKIka6bBAQBwOEbWAADXSpSRNcnaBmvKBOOYrZ07jGPabVQXSvIEjGNSPOaVuiQpN6nVOOaj48NttWVq5pw7bMV5T5ifi0vyzf/YZy6fZhyT5jGvCPavnSXGMZIkr/lzOlJ8uXFMmrYYx7z3d/N2pmY2GsdIUrfl65OYgyfTjGM6io4Zx0iSqu2FOVWiJGumwQEAcDhG1gAA17IU2Xelreh1JaZI1gAA10qUaXCSNQDAtRIlWXPNGgAAh2NkDQBwrUQZWZOsAQCulSjJmmlwAAAcjpE1AMC1LMsjK4LRcSSxfYlkDQBwLepZAwAAR2BkDQBwrURZYEaytqHlx53GMbm+NuOYPRpsHNMZTDKOybFRkEOSDpxMN445Hkg2jjl50zeNY04MNj8PknQi03yyycYpV3vuaOMYr416K/067N1MMZBs/gbWOdA8puOuIuOYbw3YaBxzoNv8tSpJl6fsN47x2biBZYav3ThmwTe2GsdI0kb1txXnVIlyzZppcAAAHI6RNQDAtZgGBwDA4RJlGpxkDQBwLSvCkbVbkjXXrAEAcDhG1gAA17IkWfa+9BCKdwOSNQDAtYLyyMMdzAAAQLwxsgYAuBarwQEAcLig5ZEnAb5nzTQ4AAAOx8gaAOBalhXhanCXLAcnWdtwctvFxjE/z5phHDM3+/8zjrks+YBxTL4vaBwjSc+3jjWO6Qyav+Te/v0q45huK2AccyrO/Fx02IhJ8ZhPaqV6zSuGeG1OnnVa5lVDkjw+45jPus3bWf3lFOOYof6/G8dIUorHznk4aRyz8cgY45j/eme8cYwkDdf7tuKcKlGuWTMNDgCAwzGyBgC4VqKMrEnWAADXYjX4Wbz33nuaNWuW8vLy5PF49Prrr4f93rIsLV++XEOGDFH//v1VXFysTz/9NFr9BQAg5PQCs0g2NzBO1u3t7SooKFBNTU2Pv1+xYoWefvpprVq1Slu3btVFF12kkpISdXR0RNxZAAASkfE0+IwZMzRjRs8rmy3LUnV1tR566CF9//vflyT9/ve/V05Ojl5//XXdcsstZ8R0dnaqs7Mz9HNbW5tplwAACerU6DiSa9ZR7EwMRXU1+O7du9Xc3Kzi4uLQvoyMDBUWFmrz5s09xlRVVSkjIyO05efnR7NLAIAL2OkFZpFsbhDVZN3c3CxJysnJCdufk5MT+t3XVVRUqLW1NbQ1NTVFs0sAALhe3FeD+/1++f3+eHcDAOBCliKrSe2SWfDojqxzc3MlSS0tLWH7W1paQr8DACBamAa3YeTIkcrNzVVtbW1oX1tbm7Zu3aqioqJoNgUAQMIwngY/duyYdu7cGfp59+7damhoUGZmpi655BItWbJEjz/+uC677DKNHDlSy5YtU15enmbPnh3NfgMAkDDz4MbJ+oMPPtCNN94Y+rm8vFyStGDBAq1Zs0b333+/2tvbdeedd+rIkSO67rrrtH79eqWkpESv13E27AnzG+G3PmHezupc89mIE+PNV9M332nvO/APj/+jccx/H8szjnnqsHnBkE+PZxvHSNJFvi7jGL/XvNiD03k95u9gSR7z4imHuy8yjrk01bxYze92XmscI0nZ399hK87cMeOIC60gh22RTmXbjK2pqdGTTz6p5uZmFRQU6Je//KUmT5581uOPHDmin/70p3r11Vf15Zdfavjw4aqurtbMmTN71Z5xsp46daqsc3wxzePx6NFHH9Wjjz5q+tAAABiJR4nMdevWqby8XKtWrVJhYaGqq6tVUlKixsZGZWefOVDo6urSv/zLvyg7O1t/+MMfNHToUP3tb3/TwIEDe91m3FeDAwDgJitXrtTixYu1cOFCSdKqVav01ltvafXq1XrwwQfPOH716tX68ssv9f777ysp6VSp2xEjRhi1SYlMAIBrRWs1eFtbW9j21TtrflVXV5fq6+vDbv7l9XpVXFx81pt//cd//IeKiopUWlqqnJwcjR07Vk888YQCgd5fOiJZAwDcy/JEvknKz88Pu5tmVVVVj80dOnRIgUDA6OZfn332mf7whz8oEAjo7bff1rJly/TUU0/p8ccf7/XTZBocAJDwmpqalJ6eHvo5mjfrCgaDys7O1q9//Wv5fD5NnDhRX3zxhZ588klVVlb26jFI1gAA14rWArP09PSwZH02WVlZ8vl8Rjf/GjJkiJKSkuTz+UL7vvGNb6i5uVldXV1KTk4+b7tMgwMA3MuKwmYgOTlZEydODLv5VzAYVG1t7Vlv/jVlyhTt3LlTwWAwtO+TTz7RkCFDepWoJZI1AABGysvL9dxzz+l3v/ud/vrXv+ruu+9We3t7aHX4/PnzVVFRETr+7rvv1pdffql7771Xn3zyid566y098cQTKi0t7XWbTIMDAFwr0vt724mdO3euDh48qOXLl6u5uVkTJkzQ+vXrQ4vO9u7dK6/3n2Ph/Px8vfPOO1q6dKnGjx+voUOH6t5779UDDzzQ6zZJ1gAAd4vDLUPLyspUVlbW4+/q6urO2FdUVKQtW7bYbo9pcAAAHI6RNQDAteIxDR4PJGsAgHtRdQvxdrK55fwHfU2SjZihJ642jpGklNXm1aaCMv8Um9HvuHHMEH+rcYwk+b0njWO6Ld/5D4oCnyd4/oO+xmvzncjOc8pKOmoc03ayv3HM4H7m7XRuyzSOgVt4/rFFEu98XLMGAMDhGFkDANyLaXAAABwuQZI10+AAADgcI2sAgHt9pcyl7XgXIFkDAFwrWlW3nI5pcAAAHI6RNQDAvRJkgRnJGgDgXglyzZppcAAAHI6RNQDAtTzWqS2SeDcgWQMA3Itr1ogqj/l1Ea/fbxwT7OgwjrH73YXPurKNY5L7qFBGoA+v8NgpsBGwuAIlSX6veTEYW+3Yq+tii6ef+duqFQiYN+SW7xzFGtesAQCAEzCyBgC4F9PgAAA4XIIka6bBAQBwOEbWAAD3SpCRNckaAOBerAYHAABOwMgaAOBa3MEMAACnS5Br1kyDAwDgcCRrAAAcjmlwAIBreRThNeuo9SS2SNZ9xcZN94OdnTHoyJmStu+2FbfzeI5xTH+feeGGv5+8yDjGrqCNP12vjYteNso22GKnyIhkr3iKnX+nAf365jWe3NaHFyZ95udOJ80L3OAf+OoWAABwAkbWAAD3SpDV4CRrAIB7JUiyZhocAACHY2QNAHAt7mAGAIDTMQ0OAACcgJE1AMC9EmRkTbIGALhWolyzZhocAACHY2QNAHCvBLndKMkaAOBeXLNGvHlsFASwbBQECLQdM46RpDYbhRsGJp0wjjkeSDaOSfV1GcdI9opy2Cn+YafAhp2+JXnslQwJeMyvkP39ZKpxzJDkVuMYr8zPnSfgkndkGOOaNQAAcARG1gAA92IaHAAAh4twGtwtydp4Gvy9997TrFmzlJeXJ4/Ho9dffz3s93fccYc8Hk/YNn369Gj1FwCAhGOcrNvb21VQUKCampqzHjN9+nTt378/tL344osRdRIAgB5ZUdhcwHgafMaMGZoxY8Y5j/H7/crNze3V43V2dqqzszP0c1tbm2mXAACJKkGuWcdkNXhdXZ2ys7N1xRVX6O6779bhw4fPemxVVZUyMjJCW35+fiy6BACAa0U9WU+fPl2///3vVVtbq5///OfauHGjZsyYoUCg5+97VlRUqLW1NbQ1NTVFu0sAgAvU6e9ZR7K5QdRXg99yyy2h/x83bpzGjx+v0aNHq66uTjfddNMZx/v9fvn9/mh3AwCAC0bMb4oyatQoZWVlaefOnbFuCgCAC1LMv2f9+eef6/DhwxoyZEismwIAJJoEWWBmnKyPHTsWNkrevXu3GhoalJmZqczMTD3yyCOaM2eOcnNztWvXLt1///269NJLVVJSEtWOAwCQKPcGN07WH3zwgW688cbQz+Xl5ZKkBQsW6Nlnn9XHH3+s3/3udzpy5Ijy8vI0bdo0PfbYY1yXtsEK9tGrKGiv2ENX0HxiJmiZX3kJ2ihhZ7eAhR3dwSTjmBRvdwx6ciavjYIhkr3zZ+ffqdsyL1aTbKNvNk+DPX31d4t/SoBTbvxuO3XqVFnW2c/MO++8E1GHAABAOO4NDgBwL65ZAwDgbIlyzZp61gAAOBwjawCAezENDgCAszENDgAAHIFkDQBwrzjVs66pqdGIESOUkpKiwsJCbdu2rVdxL730kjwej2bPnm3UHskaAOBecUjW69atU3l5uSorK/Xhhx+qoKBAJSUlOnDgwDnj9uzZo/vuu0/XX3+9cZskawBAwmtrawvbOjs7z3rsypUrtXjxYi1cuFBXXnmlVq1apdTUVK1evfqsMYFAQLfddpseeeQRjRo1yrh/JGsAgGtFq551fn6+MjIyQltVVVWP7XV1dam+vl7FxcWhfV6vV8XFxdq8efNZ+/noo48qOztbixYtsvU8WQ0OAHCvKH11q6mpSenp6aHdZ6tncejQIQUCAeXk5ITtz8nJ0Y4dO3qM2bRpk37729+qoaHBdjdJ1gAA94pSsk5PTw9L1tFy9OhR3X777XruueeUlZVl+3FI1rBt6sWNxjH/93iecYzfe9I4JmCjupdkr9qUr09LOjmXnXN3NJBiHGOnkpiN4l5Aj7KysuTz+dTS0hK2v6WlRbm5uWccv2vXLu3Zs0ezZs0K7QsGT72G+/Xrp8bGRo0ePfq87XLNGgDgWtG6Zt1bycnJmjhxompra0P7gsGgamtrVVRUdMbxY8aM0V/+8hc1NDSEtu9973u68cYb1dDQoPz8/F61y8gaAOBecbjdaHl5uRYsWKBJkyZp8uTJqq6uVnt7uxYuXChJmj9/voYOHaqqqiqlpKRo7NixYfEDBw6UpDP2nwvJGgAAA3PnztXBgwe1fPlyNTc3a8KECVq/fn1o0dnevXvl9UZ34ppkDQBwrXjdG7ysrExlZWU9/q6uru6csWvWrDFuj2QNAHCvBKm6xQIzAAAcjpE1AMC9EmRkTbIGALiW5x9bJPFuwDQ4AAAOx8gaAOBeTIMDAOBs8frqVl8jWQMA3IuRNeLOcnaBiA4rqU/ayeh3wjimI2ivb3aKcngt8792r413iKCNpTA+m+9Ex21UvhjQr9M45u/dqcYxQRtFWgJJfbiMyOF/t3AnkjUAwN1cMjqOBMkaAOBaiXLNmq9uAQDgcIysAQDuxQIzAACcjWlwAADgCIysAQDuxTQ4AADOxjQ4AABwBEbWAAD3YhocAACHI1kDAOBsiXLNmmQN2w51pxnH+L0njWOOB5PN2/GYtyNJ3TYKWNgpsJHi7TaOaQ30N44J2OibJKX6zIty2Cmw0RxMN46xo2tgHxbyAGKAZA0AcC+mwQEAcDaPZcljo0ztV+PdgK9uAQDgcIysAQDuxTQ4AADOliirwZkGBwDA4RhZAwDci2lwAACcjWlwAADgCIysAQDuxTQ4AADOlijT4CRrAIB7MbIGzs1O0Yu+4vMEbcUF++g5JXkCxjHePnxXsVOUw2vjnNtppz3oN445mWIcYpsVdMm7P1yFZA0AcDW3TGVHgmQNAHAvyzq1RRLvAnx1CwAAhzNK1lVVVbrmmmuUlpam7OxszZ49W42NjWHHdHR0qLS0VIMGDdKAAQM0Z84ctbS0RLXTAABI/1wNHsnmBkbJeuPGjSotLdWWLVu0YcMGdXd3a9q0aWpvbw8ds3TpUv3xj3/UK6+8oo0bN2rfvn26+eabo95xAABCq8Ej2VzA6Jr1+vXrw35es2aNsrOzVV9frxtuuEGtra367W9/q7Vr1+o73/mOJOn555/XN77xDW3ZskXXXnvtGY/Z2dmpzs7O0M9tbW12ngcAABesiK5Zt7a2SpIyMzMlSfX19eru7lZxcXHomDFjxuiSSy7R5s2be3yMqqoqZWRkhLb8/PxIugQASCCeYOSbG9hO1sFgUEuWLNGUKVM0duxYSVJzc7OSk5M1cODAsGNzcnLU3Nzc4+NUVFSotbU1tDU1NdntEgAg0TANfm6lpaXavn27Nm3aFFEH/H6//H7zmxwAAJAobI2sy8rK9Oabb+rdd9/VsGHDQvtzc3PV1dWlI0eOhB3f0tKi3NzciDoKAMDXsRq8B5ZlqaysTK+99pr+/Oc/a+TIkWG/nzhxopKSklRbWxva19jYqL1796qoqCg6PQYA4LTTN0WJZHMBo2nw0tJSrV27Vm+88YbS0tJC16EzMjLUv39/ZWRkaNGiRSovL1dmZqbS09N1zz33qKioqMeV4AAARIKqWz149tlnJUlTp04N2//888/rjjvukCT94he/kNfr1Zw5c9TZ2amSkhI988wzUeksnMVOMQp5ot+PngRsFIjoS0mek8YxdouT2GHn/Nl5PQQt8xfEcTuFPFJd8o4MnIVRsrZ6MV2QkpKimpoa1dTU2O4UAAC9QolMAACcLVGmwZ09VwgAABhZAwBcLEFKZJKsAQCuxTQ4AABwBEbWAAD3YjU4AADOxjQ4AABwBEbWAAD3ClqntkjiXYBkDQBwL65ZAwDgbB5FeM06aj2JLa5ZAwDgcIysncwld9YxkeLtjncXzslOtSlvH82j+fvw3AVtjDe8NqqC9fOaV+rqsMzftiyfcQjcgjuYAQDgbHx1CwAA9KimpkYjRoxQSkqKCgsLtW3btrMe+9xzz+n666/XxRdfrIsvvljFxcXnPL4nJGsAgHtZUdgMrVu3TuXl5aqsrNSHH36ogoIClZSU6MCBAz0eX1dXp3nz5undd9/V5s2blZ+fr2nTpumLL77odZskawCAa3ksK+JNktra2sK2zs7Os7a5cuVKLV68WAsXLtSVV16pVatWKTU1VatXr+7x+BdeeEE/+tGPNGHCBI0ZM0a/+c1vFAwGVVtb2+vnSbIGACS8/Px8ZWRkhLaqqqoej+vq6lJ9fb2Ki4tD+7xer4qLi7V58+ZetXX8+HF1d3crMzOz1/1jgRkAwL2C/9giiZfU1NSk9PT00G6/39/j4YcOHVIgEFBOTk7Y/pycHO3YsaNXTT7wwAPKy8sLS/jnQ7IGALjWV6ey7cZLUnp6eliyjpWf/exneumll1RXV6eUlJRex5GsAQDopaysLPl8PrW0tITtb2lpUW5u7jlj//3f/10/+9nP9Kc//Unjx483apdr1gAA9+rj1eDJycmaOHFi2OKw04vFioqKzhq3YsUKPfbYY1q/fr0mTZpk1qgYWQMA3CwOdzArLy/XggULNGnSJE2ePFnV1dVqb2/XwoULJUnz58/X0KFDQ4vUfv7zn2v58uVau3atRowYoebmZknSgAEDNGDAgF61SbIGALhWPO5gNnfuXB08eFDLly9Xc3OzJkyYoPXr14cWne3du1de7z8nrp999ll1dXXpX//1X8Mep7KyUg8//HCv2iRZAwBgqKysTGVlZT3+rq6uLuznPXv2RNweydrJPDaKt/XhTenbTvZ+JeNpqcldMehJ9HTbqPhgpzhJh5VkHJPkMS96Yef52BW0UQTFZ2NY0xk0P3c2umafFcn3iGCMQh4AADibJ3hqiyTeDVgNDgCAwzGyBgC4F9PgAAA4nM3KWWHxLsA0OAAADsfIGgDgWtG6N7jTkawBAO6VINesmQYHAMDhGFkDANzLUmT1rN0xsCZZAwDci2vWAAA4naUIr1lHrScxxTVrAAAcjpE1+lSS96RxjJ3CDV6bH5ftFMuwE+Oz0b+AzAu72GnHLjv9s/vvZKoP65mgryXIanCSNQDAvYKSjc+J4fEuwDQ4AAAOx8gaAOBarAYHAMDpEuSaNdPgAAA4HCNrAIB7JcjImmQNAHCvBEnWTIMDAOBwjKwBAO6VIN+zJlkDAFyLr24BAOB0XLMGAABOwMjayRz+ia/+UL5xTP6wL41jjgeSjWO6bVZusBM3wNfZJ+3YiQlY9j6PdwbN3xpSfX1TLcPOc7J8ffi35PC/2wtO0JI8EZzzoDv+vUjWAAD3YhocAAA4ASNrAICLRTiy7sOa75EwGllXVVXpmmuuUVpamrKzszV79mw1NjaGHTN16lR5PJ6w7a677opqpwEAkPTPafBINhcwStYbN25UaWmptmzZog0bNqi7u1vTpk1Te3t72HGLFy/W/v37Q9uKFSui2mkAABKJ0TT4+vXrw35es2aNsrOzVV9frxtuuCG0PzU1Vbm5ub16zM7OTnV2/nM1bVtbm0mXAACJLGgpoqlsl6wGj2iBWWtrqyQpMzMzbP8LL7ygrKwsjR07VhUVFTp+/PhZH6OqqkoZGRmhLT/f/OtAAIAEZQUj31zA9gKzYDCoJUuWaMqUKRo7dmxo/6233qrhw4crLy9PH3/8sR544AE1Njbq1Vdf7fFxKioqVF5eHvq5ra2NhA0AwFfYTtalpaXavn27Nm3aFLb/zjvvDP3/uHHjNGTIEN10003atWuXRo8efcbj+P1++f1+u90AACQyvmd9dmVlZXrzzTf17rvvatiwYec8trCwUJK0c+dOO00BAHB2QSvyzQWMRtaWZemee+7Ra6+9prq6Oo0cOfK8MQ0NDZKkIUOG2OogAABnlSAja6NkXVpaqrVr1+qNN95QWlqampubJUkZGRnq37+/du3apbVr12rmzJkaNGiQPv74Yy1dulQ33HCDxo8fH5MnAADAhc4oWT/77LOSTt345Kuef/553XHHHUpOTtaf/vQnVVdXq729Xfn5+ZozZ44eeuihqHUYAIAQSxGOrKPWk5gyngY/l/z8fG3cuDGiDsE98tOOmMckmVfdSvV2Gcdc0/8z4xhJSpb51ziSPOYxGd6AcUxfOm55jGNSbFQ++uOxbxjHDE36u3FM6sg+vH+D10b1saCzXw+OliDT4BTyAADA4SjkAQBwr2BQsjEjFh7vfCRrAIB7MQ0OAACcgJE1AMC9EmRkTbIGALgXVbcAAIATMLIGALiWZQVlRVDmMpLYvkSyBgC4lxVhMQ6uWQMAEGNWhNesXZKsuWYNAIDDMbIGALhXMCjZuD9/CNesETGPeTGFvpzS2bp9tHHMNv/5a6CfoTXJOMRK6sM/QBvzU75jNoJsFNeQjeIakuQ5ad6Wnaa83eYxXRnmDQ3+wMa5s4uiHH2LaXAAAOAEjKwBAK5lBYOyIpgG56tbAADEGtPgAADACRhZAwDcK2jZXkgpyTUja5I1AMC9LEtSJF/dckeyZhocAACHY2QNAHAtK2jJimAa3HLJyJpkDQBwLyuoyKbB3fHVLabBAQCuZQWtiDc7ampqNGLECKWkpKiwsFDbtm075/GvvPKKxowZo5SUFI0bN05vv/22UXskawAADKxbt07l5eWqrKzUhx9+qIKCApWUlOjAgQM9Hv/+++9r3rx5WrRokT766CPNnj1bs2fP1vbt23vdpsdy2IR9a2urBg4cqOs0U/1kfk/oC4rD7w2+83990zjG47cx5XQh3hu8nXuDS5LHxr3Bu9PNG8r6yN69wTNeOvdoCT07qW5t0ts6cuSIMjIyYtJGW1ubMjIyIs4Vp/va1NSk9PT00H6/3y+/399jTGFhoa655hr96le/kiQFg0Hl5+frnnvu0YMPPnjG8XPnzlV7e7vefPPN0L5rr71WEyZM0KpVq3rXUcthmpqaTt+Oho2NjY3NxVtTU1PMcsWJEyes3NzcqPRzwIABZ+yrrKzssd3Ozk7L5/NZr732Wtj++fPnW9/73vd6jMnPz7d+8YtfhO1bvny5NX78+F4/X8ctMMvLy1NTU5PS0tLk+drIsq2tTfn5+Wd8Ako0nIdTOA+ncB5O4Tyc4oTzYFmWjh49qry8vJi1kZKSot27d6urqyvix7Is64x8c7ZR9aFDhxQIBJSTkxO2PycnRzt27Ogxprm5ucfjm5ube91HxyVrr9erYcOGnfOY9PT0hP5jPI3zcArn4RTOwymch1PifR5iNf39VSkpKUpJSYl5O07AAjMAAHopKytLPp9PLS0tYftbWlqUm5vbY0xubq7R8T0hWQMA0EvJycmaOHGiamtrQ/uCwaBqa2tVVFTUY0xRUVHY8ZK0YcOGsx7fE8dNg5+L3+9XZWXlWa8lJArOwymch1M4D6dwHk7hPMReeXm5FixYoEmTJmny5Mmqrq5We3u7Fi5cKEmaP3++hg4dqqqqKknSvffeq29/+9t66qmn9N3vflcvvfSSPvjgA/3617/udZuO++oWAABO96tf/UpPPvmkmpubNWHCBD399NMqLCyUJE2dOlUjRozQmjVrQse/8soreuihh7Rnzx5ddtllWrFihWbOnNnr9kjWAAA4HNesAQBwOJI1AAAOR7IGAMDhSNYAADica5K1aTmyC9HDDz8sj8cTto0ZMybe3Yq59957T7NmzVJeXp48Ho9ef/31sN9blqXly5dryJAh6t+/v4qLi/Xpp5/Gp7MxdL7zcMcdd5zx+pg+fXp8OhsjVVVVuuaaa5SWlqbs7GzNnj1bjY2NYcd0dHSotLRUgwYN0oABAzRnzpwzbkjhdr05D1OnTj3j9XDXXXfFqceIlCuStWk5sgvZVVddpf3794e2TZs2xbtLMdfe3q6CggLV1NT0+PsVK1bo6aef1qpVq7R161ZddNFFKikpUUdHRx/3NLbOdx4kafr06WGvjxdffLEPexh7GzduVGlpqbZs2aINGzaou7tb06ZNU3t7e+iYpUuX6o9//KNeeeUVbdy4Ufv27dPNN98cx15HX2/OgyQtXrw47PWwYsWKOPUYEet1yY84mjx5slVaWhr6ORAIWHl5eVZVVVUce9X3KisrrYKCgnh3I64khVW7CQaDVm5urvXkk0+G9h05csTy+/3Wiy++GIce9o2vnwfLsqwFCxZY3//+9+PSn3g5cOCAJcnauHGjZVmn/u2TkpKsV155JXTMX//6V0uStXnz5nh1M+a+fh4sy7K+/e1vW/fee2/8OoWocvzIuqurS/X19SouLg7t83q9Ki4u1ubNm+PYs/j49NNPlZeXp1GjRum2227T3r17492luNq9e7eam5vDXh8ZGRkqLCxMyNdHXV2dsrOzdcUVV+juu+/W4cOH492lmGptbZUkZWZmSpLq6+vV3d0d9noYM2aMLrnkkgv69fD183DaCy+8oKysLI0dO1YVFRU6fvx4PLqHKHD87UbtlCO7UBUWFmrNmjW64oortH//fj3yyCO6/vrrtX37dqWlpcW7e3FxusRcpOXnLgTTp0/XzTffrJEjR2rXrl36yU9+ohkzZmjz5s3y+Xzx7l7UBYNBLVmyRFOmTNHYsWMlnXo9JCcna+DAgWHHXsivh57OgyTdeuutGj58uPLy8vTxxx/rgQceUGNjo1599dU49hZ2OT5Z459mzJgR+v/x48ersLBQw4cP18svv6xFixbFsWdwgltuuSX0/+PGjdP48eM1evRo1dXV6aabbopjz2KjtLRU27dvT4h1G+dytvNw5513hv5/3LhxGjJkiG666Sbt2rVLo0eP7utuIkKOnwa3U44sUQwcOFCXX365du7cGe+uxM3p1wCvjzONGjVKWVlZF+Tro6ysTG+++abeffddDRs2LLQ/NzdXXV1dOnLkSNjxF+rr4WznoSen71t9Ib4eEoHjk7WdcmSJ4tixY9q1a5eGDBkS767EzciRI5Wbmxv2+mhra9PWrVsT/vXx+eef6/DhwxfU68OyLJWVlem1117Tn//8Z40cOTLs9xMnTlRSUlLY66GxsVF79+69oF4P5zsPPWloaJCkC+r1kEhcMQ1+vnJkieK+++7TrFmzNHz4cO3bt0+VlZXy+XyaN29evLsWU8eOHQsbDezevVsNDQ3KzMzUJZdcoiVLlujxxx/XZZddppEjR2rZsmXKy8vT7Nmz49fpGDjXecjMzNQjjzyiOXPmKDc3V7t27dL999+vSy+9VCUlJXHsdXSVlpZq7dq1euONN5SWlha6Dp2RkaH+/fsrIyNDixYtUnl5uTIzM5Wenq577rlHRUVFuvbaa+Pc++g533nYtWuX1q5dq5kzZ2rQoEH6+OOPtXTpUt1www0aP358nHsPW+K9HL23fvnLX1qXXHKJlZycbE2ePNnasmVLvLvU5+bOnWsNGTLESk5OtoYOHWrNnTvX2rlzZ7y7FXPvvvuuJemMbcGCBZZlnfr61rJly6ycnBzL7/dbN910k9XY2BjfTsfAuc7D8ePHrWnTplmDBw+2kpKSrOHDh1uLFy+2mpub493tqOrp+Uuynn/++dAxJ06csH70ox9ZF198sZWammr94Ac/sPbv3x+/TsfA+c7D3r17rRtuuMHKzMy0/H6/demll1o//vGPrdbW1vh2HLZRIhMAAIdz/DVrAAASHckaAACHI1kDAOBwJGsAAByOZA0AgMORrAEAcDiSNQAADkeyBgDA4UjWAAA4HMkaAACHI1kDAOBw/z9irU6oHrWghwAAAABJRU5ErkJggg==",
       "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
+       "<Figure size 640x480 with 2 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -316,7 +301,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {
     "id": "0_EvK2kJPKDk"
    },
@@ -345,13 +330,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
     },
     "id": "0WhtP5RRkYSI",
-    "outputId": "cfbfdd7b-7a5f-46fb-b998-4f9d79835b0b"
+    "outputId": "252f57c2-9c4b-4d90-e34a-b16e0917f079"
    },
    "outputs": [
     {
@@ -379,7 +364,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {
     "id": "EMxlW2kZDtvn"
    },
@@ -393,13 +378,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
     },
     "id": "P7vqUjDMGF2S",
-    "outputId": "e4bae463-23a6-43fd-c12e-28ba30e616bf"
+    "outputId": "0a87104c-8b4f-49b7-f9c9-05121b18ea4b"
    },
    "outputs": [
     {
@@ -424,7 +409,7 @@
    "source": [
     "## 2. Relabeling and computing PQK features\n",
     "\n",
-    "You will now prepare a \"stilted\" quantum dataset by incorporating quantum components and re-labeling the truncated fashion-MNIST dataset you've created above. In order to get the most seperation between quantum and classical methods, you will first prepare the PQK features and then relabel outputs based on their values. "
+    "You will now prepare a \"stilted\" quantum dataset by incorporating quantum components and re-labeling the truncated fashion-MNIST dataset you've created above. In order to get the most seperation between quantum and classical methods, you will first prepare the PQK features and then relabel outputs based on their values."
    ]
   },
   {
@@ -434,7 +419,7 @@
    },
    "source": [
     "### 2.1 Quantum encoding and PQK features\n",
-    "You will create a new set of features, based on `x_train`, `y_train`, `x_test` and `y_test` that is defined to be the 1-RDM on all qubits of: \n",
+    "You will create a new set of features, based on `x_train`, `y_train`, `x_test` and `y_test` that is defined to be the 1-RDM on all qubits of:\n",
     "\n",
     "$V(x_{\\text{train}} / n_{\\text{trotter}}) ^ {n_{\\text{trotter}}} U_{\\text{1qb}} | 0 \\rangle$\n",
     "\n",
@@ -445,7 +430,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "metadata": {
     "id": "hVTlHdGvEuaT"
    },
@@ -472,28 +457,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
-     "height": 238
+     "height": 499
     },
     "id": "tfJkWj88Fqwl",
-    "outputId": "b1f802ea-2220-46ed-9bb5-5975290756b0"
+    "outputId": "4180a8c3-3071-4993-a44b-0ae3ca7e994c"
    },
-   "outputs": [
-    {
-     "data": {
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-      "text/plain": [
-       "<cirq.contrib.svg.svg.SVGCircuit at 0x7f6db4231940>"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "SVGCircuit(single_qubit_wall(\n",
     "    cirq.GridQubit.rect(1,4), np.random.uniform(size=(4, 3))))"
@@ -510,7 +483,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "metadata": {
     "id": "4w2em6c0HOIO"
    },
@@ -538,35 +511,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
-     "height": 138
+     "height": 573
     },
     "id": "r7YIeOrzJDlT",
-    "outputId": "b2c5a762-558f-4974-9661-598ef20179e5"
+    "outputId": "f621fafe-91e6-443c-8b9b-26e7a1190b34"
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Symbols found in circuit:[ref_0]\n"
-     ]
-    },
-    {
-     "data": {
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-      "text/plain": [
-       "<cirq.contrib.svg.svg.SVGCircuit at 0x7f6db41ccbe0>"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "test_circuit, test_symbols = v_theta(cirq.GridQubit.rect(1, 2))\n",
     "print(f'Symbols found in circuit:{test_symbols}')\n",
@@ -584,7 +538,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
    "metadata": {
     "id": "LReAUF6CSwn5"
    },
@@ -602,7 +556,7 @@
     "  # Prepare parametrized V\n",
     "  V_circuit, symbols = v_theta(qubits)\n",
     "  exp_circuit = cirq.Circuit(V_circuit for t in range(n_trotter))\n",
-    "  \n",
+    "\n",
     "  # Convert to `tf.Tensor`\n",
     "  initial_U_tensor = tfq.convert_to_tensor([initial_U])\n",
     "  initial_U_splat = tf.tile(initial_U_tensor, [n_points])\n",
@@ -626,7 +580,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "metadata": {
     "id": "5F47SaRERKx_"
    },
@@ -648,7 +602,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": null,
    "metadata": {
     "id": "cEGko5t-SZ14"
    },
@@ -667,15 +621,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": null,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
     },
     "id": "xZOEdNMzS8hW",
-    "outputId": "5d8f40b0-af85-4afe-dc25-599cd3966385"
+    "outputId": "4be54ac7-e999-4ba3-9be9-3823cb054590"
    },
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.10/dist-packages/keras/src/initializers/initializers.py:120: UserWarning: The initializer RandomUniform is unseeded and being called multiple times, which will return identical values each time (even if the initializer is unseeded). Please update your code to provide a seed to the initializer, or avoid using the same initializer instance more than once.\n",
+      "  warnings.warn(\n"
+     ]
+    },
     {
      "name": "stdout",
      "output_type": "stream",
@@ -713,7 +675,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": null,
    "metadata": {
     "id": "BLyGksxvGINl"
    },
@@ -735,13 +697,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": null,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
     },
     "id": "a4AxcKa4RRJr",
-    "outputId": "049fc8ce-0ff7-442c-8b7f-861bea0fb658"
+    "outputId": "a9f4af24-0506-4058-da23-02f4b79c3f5b"
    },
    "outputs": [
     {
@@ -749,47 +711,104 @@
      "output_type": "stream",
      "text": [
       "Eigenvectors of pqk kernel matrix: tf.Tensor(\n",
-      "[[-2.09569391e-02  1.05973557e-02  2.16634180e-02 ...  2.80352887e-02\n",
-      "   1.55521873e-02  2.82677952e-02]\n",
-      " [-2.29303762e-02  4.66355234e-02  7.91163836e-03 ... -6.14174758e-04\n",
-      "  -7.07804322e-01  2.85902526e-02]\n",
-      " [-1.77853629e-02 -3.00758495e-03 -2.55225878e-02 ... -2.40783971e-02\n",
-      "   2.11018627e-03  2.69009806e-02]\n",
+      "[[-0.02572668 -0.02500617 -0.00210256 ...  0.01292474 -0.00216366\n",
+      "   0.03198409]\n",
+      " [-0.04505223 -0.00725259 -0.01396801 ... -0.00145716 -0.7412964\n",
+      "   0.03257377]\n",
+      " [ 0.00566714  0.02528062 -0.00147861 ... -0.03087673 -0.00935522\n",
+      "   0.03051045]\n",
       " ...\n",
-      " [ 6.05797209e-02  1.32483775e-02  2.69536003e-02 ... -1.38843581e-02\n",
-      "   3.05043962e-02  3.85345481e-02]\n",
-      " [ 6.33309558e-02 -3.04112374e-03  9.77444276e-03 ...  7.48321265e-02\n",
-      "   3.42793856e-03  3.67484428e-02]\n",
-      " [ 5.86028099e-02  5.84433973e-03  2.64811981e-03 ...  2.82612257e-02\n",
-      "  -3.80136147e-02  3.29943895e-02]], shape=(1200, 1200), dtype=float32)\n",
+      " [-0.00952418 -0.01820112 -0.0299136  ... -0.00394092 -0.01149889\n",
+      "   0.0245917 ]\n",
+      " [ 0.03070638 -0.03498115 -0.02656119 ...  0.0215356   0.01124762\n",
+      "   0.02580499]\n",
+      " [ 0.00698054  0.02246808 -0.00997438 ... -0.01162745 -0.0414562\n",
+      "   0.0266857 ]], shape=(1000, 1000), dtype=float32)\n",
       "Eigenvectors of original kernel matrix: tf.Tensor(\n",
-      "[[ 0.03835681  0.0283473  -0.01169789 ...  0.02343717  0.0211248\n",
-      "   0.03206972]\n",
-      " [-0.04018159  0.00888097 -0.01388255 ...  0.00582427  0.717551\n",
-      "   0.02881948]\n",
-      " [-0.0166719   0.01350376 -0.03663862 ...  0.02467175 -0.00415936\n",
-      "   0.02195409]\n",
+      "[[ 4.2462684e-02  3.1354256e-02  1.2604258e-02 ... -2.0111818e-02\n",
+      "   3.0930713e-04  3.5385568e-02]\n",
+      " [-4.3868035e-02  1.0300528e-02  1.4831377e-02 ...  5.4228324e-03\n",
+      "   6.7345721e-01  3.1489469e-02]\n",
+      " [-1.8083273e-02  1.5215975e-02  3.9878976e-02 ... -3.9398521e-03\n",
+      "  -5.4628002e-03  2.4036596e-02]\n",
       " ...\n",
-      " [-0.03015648 -0.01671632 -0.01603392 ...  0.00100583 -0.00261221\n",
-      "   0.02365689]\n",
-      " [ 0.0039777  -0.04998879 -0.00528336 ...  0.01560401 -0.04330755\n",
-      "   0.02782002]\n",
-      " [-0.01665728 -0.00818616 -0.0432341  ...  0.00088256  0.00927396\n",
-      "   0.01875088]], shape=(1200, 1200), dtype=float32)\n"
+      " [ 6.6030736e-04 -3.2971684e-02  3.1675640e-02 ...  3.9464641e-02\n",
+      "  -1.8377423e-02  2.3110418e-02]\n",
+      " [ 2.9626949e-04 -3.9277028e-02  1.2305132e-02 ...  2.4492849e-02\n",
+      "   3.2030831e-03  2.4250248e-02]\n",
+      " [-1.9339835e-02 -3.1469479e-02  2.1497982e-02 ...  4.3812353e-02\n",
+      "   4.8911185e-03  2.4445757e-02]], shape=(1000, 1000), dtype=float32)\n"
      ]
     }
    ],
    "source": [
     "S_pqk, V_pqk = get_spectrum(\n",
-    "    tf.reshape(tf.concat([x_train_pqk, x_test_pqk], 0), [-1, len(qubits) * 3]))\n",
+    "    tf.reshape(x_train_pqk, [-1, len(qubits) * 3]))\n",
     "\n",
     "S_original, V_original = get_spectrum(\n",
-    "    tf.cast(tf.concat([x_train, x_test], 0), tf.float32), gamma=0.005)\n",
+    "    tf.cast(x_train, tf.float32), gamma=0.005)\n",
     "\n",
     "print('Eigenvectors of pqk kernel matrix:', V_pqk)\n",
     "print('Eigenvectors of original kernel matrix:', V_original)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "EFiv3KcmK9Ma",
+    "outputId": "03cb3dbb-4e87-48ed-fd09-bc273529cf40"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Eigenvectors of pqk kernel matrix for test: tf.Tensor(\n",
+      "[[ 0.02638157  0.02806157  0.03566442 ...  0.04386202 -0.0405141\n",
+      "   0.0662972 ]\n",
+      " [ 0.05066229  0.01891651  0.06229709 ...  0.00191754  0.6073843\n",
+      "   0.07107678]\n",
+      " [-0.00779327 -0.09399084 -0.06509633 ...  0.06940294 -0.00730488\n",
+      "   0.0816892 ]\n",
+      " ...\n",
+      " [ 0.02878537 -0.06277579 -0.0869303  ...  0.06611525  0.04124057\n",
+      "   0.08266883]\n",
+      " [-0.08475214  0.01517385 -0.0412915  ... -0.04286676  0.00467837\n",
+      "   0.07634711]\n",
+      " [ 0.03031691 -0.04953269  0.09389514 ... -0.00080099  0.04431828\n",
+      "   0.06373685]], shape=(200, 200), dtype=float32)\n",
+      "Eigenvectors of original kernel matrix for test: tf.Tensor(\n",
+      "[[-0.06053653 -0.10981707 -0.06392459 ...  0.09634899 -0.0199185\n",
+      "   0.0762083 ]\n",
+      " [ 0.08907834  0.03093441  0.05701356 ... -0.02077192  0.67301965\n",
+      "   0.06606765]\n",
+      " [ 0.07030783 -0.0973983   0.03607463 ...  0.01863695 -0.01661182\n",
+      "   0.07912693]\n",
+      " ...\n",
+      " [-0.07468248 -0.04320016  0.03100761 ...  0.01004651 -0.03957008\n",
+      "   0.05961308]\n",
+      " [ 0.00730421 -0.12527922  0.01711777 ... -0.02228218  0.03887707\n",
+      "   0.06976941]\n",
+      " [-0.04144859 -0.02311671  0.09413407 ... -0.03321036  0.04489374\n",
+      "   0.04668954]], shape=(200, 200), dtype=float32)\n"
+     ]
+    }
+   ],
+   "source": [
+    "S_pqk_test, V_pqk_test = get_spectrum(\n",
+    "    tf.reshape(x_test_pqk, [-1, len(qubits) * 3]))\n",
+    "\n",
+    "S_test_original, V_test_original = get_spectrum(\n",
+    "    tf.cast(x_test, tf.float32), gamma=0.005)\n",
+    "\n",
+    "print('Eigenvectors of pqk kernel matrix for test:', V_pqk_test)\n",
+    "print('Eigenvectors of original kernel matrix for test:', V_test_original)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {
@@ -798,21 +817,23 @@
    "source": [
     "Now you have everything you need to re-label the dataset! Now you can consult with the flowchart to better understand how to maximize performance seperation when re-labeling the dataset:\n",
     "\n",
-    "<img src=\"./images/quantum_data_1.png\">\n",
+    "<img src=\"https://github.com/tensorflow/quantum/blob/master/docs/tutorials/images/quantum_data_1.png?raw=1\">\n",
     "\n",
     "In order to maximize the seperation between quantum and classical models, you will attempt to maximize the geometric difference between the original dataset and the PQK features kernel matrices $g(K_1 || K_2) = \\sqrt{ || \\sqrt{K_2} K_1^{-1} \\sqrt{K_2} || _\\infty}$ using `S_pqk, V_pqk` and `S_original, V_original`. A large value of $g$ ensures that you initially move to the right in the flowchart down towards a prediction advantage in the quantum case."
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "id": "cjhhgD2zCxpV"
+   },
    "source": [
     "Note: Computing quantities for $s$ and $d$ are also very useful when looking to better understand performance seperations. In this case ensuring a large $g$ value is enough to see performance seperation."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": null,
    "metadata": {
     "id": "g-D_939PZoOH"
    },
@@ -838,14 +859,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": null,
    "metadata": {
     "id": "3IkuiFmZRUby"
    },
    "outputs": [],
    "source": [
-    "y_relabel = get_stilted_dataset(S_pqk, V_pqk, S_original, V_original)\n",
-    "y_train_new, y_test_new = y_relabel[:N_TRAIN], y_relabel[N_TRAIN:]"
+    "y_train_new = get_stilted_dataset(S_pqk, V_pqk, S_original, V_original)\n",
+    "y_test_new = get_stilted_dataset(S_pqk_test, V_pqk_test, S_test_original, V_test_original)"
    ]
   },
   {
@@ -870,32 +891,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": null,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
     },
     "id": "eK94tGyf--q2",
-    "outputId": "36ee9f7f-3532-440d-de23-ebcba8c76976"
+    "outputId": "72ef5081-0a71-4b39-fea8-952e1552e74f"
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Model: \"sequential\"\n",
+      "Model: \"sequential_3\"\n",
       "_________________________________________________________________\n",
-      "Layer (type)                 Output Shape              Param #   \n",
+      " Layer (type)                Output Shape              Param #   \n",
       "=================================================================\n",
-      "dense (Dense)                (None, 32)                1088      \n",
-      "_________________________________________________________________\n",
-      "dense_1 (Dense)              (None, 16)                528       \n",
-      "_________________________________________________________________\n",
-      "dense_2 (Dense)              (None, 1)                 17        \n",
+      " dense_9 (Dense)             (None, 32)                1088      \n",
+      "                                                                 \n",
+      " dense_10 (Dense)            (None, 16)                528       \n",
+      "                                                                 \n",
+      " dense_11 (Dense)            (None, 1)                 17        \n",
+      "                                                                 \n",
       "=================================================================\n",
-      "Total params: 1,633\n",
-      "Trainable params: 1,633\n",
-      "Non-trainable params: 0\n",
+      "Total params: 1633 (6.38 KB)\n",
+      "Trainable params: 1633 (6.38 KB)\n",
+      "Non-trainable params: 0 (0.00 Byte)\n",
       "_________________________________________________________________\n"
      ]
     }
@@ -919,7 +941,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": null,
    "metadata": {
     "id": "QUL8ygMn_zOB"
    },
@@ -946,32 +968,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": null,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
     },
     "id": "uHhUYWVh9kGE",
-    "outputId": "f586fd89-1157-4a7e-b382-71157a894519"
+    "outputId": "26f6e1e6-21c9-42fe-beeb-2059892a4285"
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Model: \"sequential_1\"\n",
+      "Model: \"sequential_4\"\n",
       "_________________________________________________________________\n",
-      "Layer (type)                 Output Shape              Param #   \n",
+      " Layer (type)                Output Shape              Param #   \n",
       "=================================================================\n",
-      "dense_3 (Dense)              (None, 32)                352       \n",
-      "_________________________________________________________________\n",
-      "dense_4 (Dense)              (None, 16)                528       \n",
-      "_________________________________________________________________\n",
-      "dense_5 (Dense)              (None, 1)                 17        \n",
+      " dense_12 (Dense)            (None, 32)                352       \n",
+      "                                                                 \n",
+      " dense_13 (Dense)            (None, 16)                528       \n",
+      "                                                                 \n",
+      " dense_14 (Dense)            (None, 1)                 17        \n",
+      "                                                                 \n",
       "=================================================================\n",
-      "Total params: 897\n",
-      "Trainable params: 897\n",
-      "Non-trainable params: 0\n",
+      "Total params: 897 (3.50 KB)\n",
+      "Trainable params: 897 (3.50 KB)\n",
+      "Non-trainable params: 0 (0.00 Byte)\n",
       "_________________________________________________________________\n"
      ]
     }
@@ -995,7 +1018,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": null,
    "metadata": {
     "id": "8N54jMau-1L5"
    },
@@ -1022,35 +1045,34 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": null,
    "metadata": {
     "colab": {
-     "base_uri": "https://localhost:8080/"
+     "base_uri": "https://localhost:8080/",
+     "height": 482
     },
     "id": "t9CDiHTmAEu-",
-    "outputId": "18d3ba86-969c-4f65-a0b1-aa86efc6212a"
+    "outputId": "5c4724d8-20e5-4cb5-e0c6-72629f53253f"
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x7f6d846ecee0>"
+       "<matplotlib.legend.Legend at 0x787ba42ddb70>"
       ]
      },
-     "execution_count": 28,
+     "execution_count": 90,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
-       "<Figure size 720x360 with 1 Axes>"
+       "<Figure size 1000x500 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -1077,7 +1099,9 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "id": "UbdYo8UICxpX"
+   },
    "source": [
     "## 4. Important conclusions\n",
     "\n",
@@ -1093,14 +1117,11 @@
  ],
  "metadata": {
   "colab": {
-   "collapsed_sections": [],
-   "name": "quantum_data.ipynb",
    "provenance": [],
    "toc_visible": true
   },
   "kernelspec": {
    "display_name": "Python 3",
-   "language": "python",
    "name": "python3"
   },
   "language_info": {
@@ -1117,5 +1138,5 @@
   }
  },
  "nbformat": 4,
- "nbformat_minor": 1
+ "nbformat_minor": 0
 }