diff --git a/docs/tutorials/06_examples_max_cut_and_tsp.ipynb b/docs/tutorials/06_examples_max_cut_and_tsp.ipynb
index c049119e6..abf7a1156 100644
--- a/docs/tutorials/06_examples_max_cut_and_tsp.ipynb
+++ b/docs/tutorials/06_examples_max_cut_and_tsp.ipynb
@@ -105,6 +105,16 @@
    "execution_count": 1,
    "metadata": {},
    "outputs": [],
+   "source": [
+    "%load_ext autoreload\n",
+    "%autoreload 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "# useful additional packages \n",
     "import matplotlib.pyplot as plt\n",
@@ -112,6 +122,7 @@
     "%matplotlib inline\n",
     "import numpy as np\n",
     "import networkx as nx\n",
+    "import retworkx as rx\n",
     "\n",
     "from qiskit import Aer\n",
     "from qiskit.tools.visualization import plot_histogram\n",
@@ -133,393 +144,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -552,7 +182,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "metadata": {
     "tags": []
    },
@@ -590,7 +220,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "metadata": {
     "tags": []
    },
@@ -621,388 +251,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1045,7 +294,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 8,
    "metadata": {
     "tags": []
    },
@@ -1054,6 +303,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "[1.0, 1.0, 1.0, 1.0, 1.0]\n",
       "// This file has been generated by DOcplex\n",
       "// model name is: Max-cut\n",
       "// single vars section\n",
@@ -1080,7 +330,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -1106,7 +356,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 10,
    "metadata": {
     "tags": []
    },
@@ -1144,7 +394,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 11,
    "metadata": {
     "tags": []
    },
@@ -1161,388 +411,7 @@
     },
     {
      "data": {
-      "image/png": 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ym82cPXuWkSNHEhQURE5ODq+++ipDhw4FykLZZDJJ6Arj6brOoUOHUFWV9evX07FjRxRF4emnn8bHx8fo8oRw2uLFiwkNDaV3797lWxDh1+mGpkRCtxE6deoUcXFx5XOtiqKwatUqQkNDDa5MiGtjtVp58cUX+emnn4CyQzhN9aomORzRSGRnZ7N+/XpsNhspKSmMHDmSmJgYbrzxxibztksIVVXp0KED3bt3p7S0tHzHwg8//EB6ejoAgYGBDf5Kdzkc0UgVFxezfft2VFXl+++/Z8CAAUyfPp2oqKgms31GiMtFRkby+eef0717d6xWK6dPn2b58uWcOHGC9u3bk5KSQlJSEt98802jfWcnI90GRtd1Dhw4gKqqbNiwgfDwcBRFYcSIEXIYQTQLiYmJdO7cmQMHDrB06VLatm3LuHHjaN26NaGhocybNw+Hw9Ggu5TJSLcROHnyJKqqoqoqbm5uKIrCJ598QqtWrYwuTYh61blzZwC+/vprOnXqxIMPPkhoaGj5NJqmaYwYMcLIEq+LhK6BMjMzy+dpz5w5w5gxY1i8eDFdu3aVeVrRrB09epRNmzbx5ZdfEhAQAMDu3bt54YUXKCwsZOHChQZXeO0kdOtZUVER27ZtQ1VVfvjhBwYNGsSMGTPo169fk12tFaKmIiIiyMzMJD4+nt69e/Paa6+RnJzMyJEjiY2NbdRrGjKnWw80TeOHH37AZrOxZcsWIiIiUBSF4cOH4+npaXR5QjRImzdvZs2aNezatYvBgwdz//33ExYWhqenZ4M/ElzVnK6Ebh1KSkoq30/r5eWFoiiMGTOG4OBgo0sTotHIz89vdIMTWUirRxcuXGDt2rWoqsr58+cZM2YMr776Kl26dDG6NCEaJU9PzyZ1Mk1CtxYUFhayZcsWVFXl4MGDDB06lNjYWG699dYm8xdFCCNd+nfU0KcVnCGhe400TWPPnj2oqsrWrVu56aabUBSFv/71r9JzVog6kJ+fz7p167jrrruMLuW6SOjWUGJiIqqqsnbtWvz9/YmJiSE2NpaWLVsaXZoQTVppaSlvvfUW3bt3Jzw83OhyrpmErhPS09PL52mzs7OJjo7mjTfeICwszOjShGg2fHx8mDFjBkuWLOGdd95ptNMMErqVyM/PZ/PmzdhsNhISErjtttuYO3cuvXv3lnlaIQxy9913s2bNGjZs2MDIkSONLueaSOhexuFwsHv3blRV5dtvv6VXr17cddddLF++HDc3N6PLE6LZM5vNzJ8/n2eeeYZBgwY1yvWTZh+6uq5z7NgxbDYb8fHxhISEEBMTw+OPP15+/FAI0XDcfPPN9OrVi1WrVvHII48YXU6NNdvQTUtLK7+wsaCggOjoaN555x06duxodGlCiGo89thj3Hvvvdxxxx3ld6g1Fs0qdPPy8sovbDx27BgjRozgySefJDIyUuZphWhEgoOD+Z//+R+WL1/OkiVLjC6nRpp86JaWlvLf//4XVVXZsWMHffr04Z577mHQoEFyYaMQjdh9993HhAkT2LVrF/369TO6HKfVWugm5uezJyeH73NyOFZQQImm4WGxcJOnJzd7e9Pfx4c29bQYpes6CQkJ2Gw21q1bR7t27VAUhYULF+Lr61svNQgh6parqytz585l6dKlfPrpp42m89h1NbzRdZ0NmZm8efo0h/Py0AEdcDOZMAEaUKRpWEwmdGCwnx+zQ0O5xdu7ll9GmdTU1PJ5WofDgaIoREdH065duzr5fkIIY+m6zqOPPkpUVBSTJk0yupxyddJl7FxxMQuTkvg2KwsL4GWxVLlZWdN1sh0OAP4QHMxTHTrQohb6x2ZnZ7Nhwwbi4uJISkpi5MiRKIpCjx49Gu3maSGE806cOMGDDz7I559/3mB2HNV66B7Oy+N/jhwh1+HAt5qw/S2HrpPjcNDWzY3PbryRVtcw5VBSUsLOnTux2Wzl8zkxMTEMGDAAFxeXGj+fEKJxe+2118jJyeHZZ581uhSglls7HsvPZ+KRIxRrGn7XMIdiMZnws1o5XVzMhMOH+eqmmwh0YkFL13V+/PFHVFVl/fr1hIWFER0dzTPPPIOPj0+N6xBCNB0PPfQQ48aN48iRI9x4441Gl1OlGqVmocPBQz/9RJGm4XOdUwO+FgupxcU8cfw4qyIiKh0tp6SklM/Tms1mYmJi+Oijjxrd3jwhRN1p0aIFf/zjH1myZAkrV65s0FtAaxS6y0+d4nRR0TWNcCviZ7Gww27nq4wMxgYFlX/cbrezfv16VFUlJSWFUaNGsWjRIrp16ybztEKICsXExLB69Wri4uKIiYkxupxKOZ2e6cXF/OPsWbxr8fJEk8mEh9nMouRkxvj48N8dO7DZbOzZs4eBAwcyffp0oqKiGs1WECGEccxmMwsWLGDu3LkMGzaMFi1aGF1ShZxOsy/S03HoOpZqRpqO3FzOvP8+uYcOYfX2JmjCBHwHDKj08VpRESdycxkwaxb9XVxQFIUXX3wRLy8v51+FEEIA3bt3Z8CAAaxcuZJHH33U6HIq5HTofnruHO5OzJOcXbUKk9VKlzfeoDA5mZRly3Bv3x63tm3LH1NcXIzdbsdut2M2m3Hx96f77Nn8/eabr+1VCCHERbNnz+aee+7hzjvvpEOHDkaXcxWnZptzS0tJLSrCvZpRrlZYSM6ePQSNH4/Z3R3Prl3x7t0b+44dlDocZGZm8suJEyQnJ6NpGm3btSMsLIwQX19+uriHVwghrkdAQADTp09n2bJlVLUl1ihOhe6xggJcTaZqF7GKz54FsxnXVq2Asm1eWlAQGUePcvz4cQoKCggKCiI8PJyQkBDcL+7RdTGZyCwtJbOk5DpfjhBCwD333ENqairbt283upSrOBW69tJSnPl5oRUVYb7YVDjl1CkOHzlCdlERppIS2oaGEhgYWGHTYZPJhNVkKj+xJoQQ18PFxYV58+axbNkyiouLjS7nCk7N6Tq7Scvs5oZWUABAfl4eXl5eOIAiV1fS09NxOByUOhxomobFYsFqsWCxWLBYrZS6uvLxRx/RydsbPz8//P398fPzK/+9dAQTQtRE//796dSpE5988gnTpk0zupxyToWuv5NHa11btQJNo/jsWdq2a8eZ1FQ8srPx7tqV4Muag+uUXY3jKC3F4XBQUlpKtq7jbTZz8uRJDh48SFZWVvn/MjMzsVqtVwTx5YFcUUh7e3s36A3SQoi698QTTzBlyhQURSE4ONjocgAnQzfcw4MSXUfX9Srndc3u7nj36UP6mjW0fuABLGfPkrlrF+F/+tMVjzMB1osjXSjrRNbSYuHRBx6o8Hl1XaegoIDMzMwrgvjSr6dOnbriY1lZWeTl5eHj43NVOFcV2O7u7k7+ZxNCNAahoaGMGzeOFStW8Kff5JBRnApdT4uFDu7unC0qwrOawxGtpk4l9b33ODZ7NmZPT8yKgvniwlpl8jWNYf7+lX7eZDLh6emJp6cnoaGhzpSMw+HAbrdXGNKpqakcOXLkio9lZWVhMpkqHTlXFNi+vr4ymhaigZs2bRrjx4/nwIEDREZGGl2O813GPjhzhj8nJ9f4CHB6RgbFRUWVhqWu62RrGh9ERDDQwAbjuq5TWFh4xWi5osC+/GM5OTl4X5yDrmqq4/Kw9vDwkKPMQtSz+Ph4PvzwQz766KN6GSjVSmvHrJIS+u3bh7vJhEsNitZ1nePHj9OmTRs8PT2v+ny+w4Gfiwvbb74ZcyMLI03TKhxNVxXYmqZVGMiVBbavry+WWjx6LURzpOs6Dz30EIqiMHbs2Dr/frXS2tHPxYXH2rZlWUoKfk7s2b3smxMSEsLZs2cJCwu74nOarlOk6/z5hhsaXeBC2Vlvf39//KuYGvmtwsLCCkM6MzOTY8eOXRXY2dnZtGjRwukFRD8/Pzw9PWU0LcRlTCYTCxYsYPbs2dx+++2GtoOtURPzUk3jzkOH+Ck/v8bTDCdPnsTb27s8oHRdJ9Ph4PctW/K38PBrq74Z0DSNnJycaqc6Lg/rkpKSGi0g+vr6Ntjm7zk5Odx9990kJSURGxvL448/ftWCrqZpnD59GqvVSnBwsLwzEJX6y1/+gtVqZf78+XX6fWr15ojTRUXcfegQmSUl+NYgeIuKikg+eZJOYWGYzWayHA66enryrxtvxFu6iNWq4uJip0L60q92ux0PDw+nFxD9/Pzw8vKql9F0Xl4e+/bt49NPPwXgrbfewuFwXBGs69at4+WXXyY5OZkRI0bw+uuvN9gOU8JYWVlZTJgwgbfffpvOnTvX2fep9et6ThYWcu+RI5wtLsbXYnF6aiAtLY1SXcczMJCeLVrwQbduNQpuUTc0TSM3N9fpBcSsrCyKiorw9fV1egHRz8/vug64PPnkk7i5ufHCCy/gcDgwm82YTCaOHTvGkiVLGDFiBBMnTmTOnDkEBwfz1FNPoWma7C4RV/n888/ZtGkTb7/9dp0NHGr1uh6A9u7uxPfsycvJyXyRno4O+FgsVbZ9LNV1rP7+nD9zhodcXXm2e/caLciJumM2m/Hx8cHHx4f27ds79TWXOsVVNL2RnJzM/v37r/qcq6trlQuII0aMqLSlZ1paGrfccgtQNj93aYrh22+/pWXLlgy42D7U09MTu91e/jghfmvcuHF8+eWXbNq0iREjRtT797/mYaaX1cpfOnXinuBg3jtzhg2ZmZgpO+jgctkV7KX8eiX7fa1a0SY9nb1vvYX1vfdq6SUII7i6uhIUFETQZTd+VEXXdfLy8iodOZ88eZIhQ4ZU+vVZWVkVnig6ffo03t7etGzZEgCr1Yqnp6eMckWlLBYL8+fP5/nnn2fgwIHlh6LOl5RwJC+PnwsKsJeW4moyEeLqSrcWLQj38MC1lv4+Xfd7+5u9vXnL25vzJSUcyM3lYG4uCfn5FGsaLSwWenh50d3Tk15eXnhZrWgdOzLliy9Yt24do0ePro3XIBoBk8mEl5cXXl5etL2st7IzHA4HWVlZ5cGqXzwdaTabycvLu+L5MjM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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1576,7 +445,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1588,7 +457,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 13,
    "metadata": {
     "scrolled": true,
     "tags": [
@@ -1600,397 +469,16 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "energy: -1.4999796718931908\n",
-      "time: 3.5326640605926514\n",
-      "max-cut objective: -3.9999796718931906\n",
+      "energy: -1.4997993934173715\n",
+      "time: 1.476668119430542\n",
+      "max-cut objective: -3.9997993934173715\n",
       "solution: [0. 1. 0. 1.]\n",
       "solution objective: 4.0\n"
      ]
     },
     {
      "data": {
-      "image/png": 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++2zppjRC1CWqqrJo0aLSC7ZvvPEGy5YtIyYmhv/97380bdoUcJ1piBK6LsJms/Hdd99hsVj4/vvv6dWrFxMmTKBPnz6VXqwQQk9Go5FTp04xdOhQwsLCyMvL47XXXmPgwIGAI5QNBoOErtCfpmn8/PPPKIrC2rVradWqFWazmaeeeoqAgAC9yxPCaQsWLCAyMpLu3buXTkGE39sNrkRCtx46fvw4iYmJpb1Ws9nMsmXLiIyM1LkyIa6NyWTihRde4ODBg4BjEY6rHtUkiyPqCavVytq1a7FYLBw7doyhQ4cSHx9Pp06dXOZtlxCKotCyZUs6d+5MSUlJ6YyFH3/8kaysLABCQ0Pr/JHusjiiniouLmbr1q0oisIPP/xAnz59mDx5MrGxsS4zfUaIS3Xr1o3PPvuMzp07YzKZOHHiBEuWLOHo0aO0aNGCY8eOkZqaytdff11v39nJSLeO0TSNvXv3oigK69atIzo6GrPZzJAhQ2QxgmgQUlJSaNu2LXv37mXRokU0a9aM0aNH06RJEyIjI5k9ezZ2u71O71ImI916ID09HUVRUBQFT09PzGYzH3/8MY0bN9a7NCFqVdu2bQH46quvaNOmDQ8++CCRkZGlbTRVVRkyZIieJV4XCV0dZWdnl/ZpT548yYgRI1iwYAHt27eXPq1o0A4cOMCGDRtYtWoVISEhAOzYsYPnn3+ewsJC5s2bp3OF105Ct5YVFRWxZcsWFEXhxx9/pF+/fkyZMoVevXq57NVaIaqqQ4cOZGdnk5SURPfu3Xn99ddJS0tj6NChJCQk1OtrGtLTrQWqqvLjjz9isVjYtGkTHTp0wGw2M3jwYHx8fPQuT4g6aePGjaxcuZLt27fTv39/7r//fqKiovDx8anzS4Ir6ulK6Nag1NTU0vm0fn5+mM1mRowYQXh4uN6lCVFvFBQU1LvBiVxIq0Xnzp1j9erVKIrC2bNnGTFiBK+99hrt2rXTuzQh6iUfHx+XWpkmoVsNCgsL2bRpE4qisG/fPgYOHEhCQgI333yzy3yjCKGniz9Hdb2t4AwJ3Wukqio7d+5EURQ2b97MDTfcgNls5tVXX5U9Z4WoAQUFBaxZs4Y77rhD71Kui4RuFaWkpKAoCqtXryY4OJj4+HgSEhJo1KiR3qUJ4dJKSkp455136Ny5M9HR0XqXc80kdJ2QlZVV2qe1Wq3ExcXx1ltvERUVpXdpQjQYAQEBTJkyhYULF/Lee+/V2zaDhG45CgoK2LhxIxaLheTkZG655RZmzZpF9+7dpU8rhE7uvPNOVq5cybp16xg6dKje5VwTCd1L2O12duzYgaIofPPNN8TExHDHHXewZMkSPD099S5PiAbPaDQyZ84cnn76afr161cvr580+NDVNI1Dhw5hsVhISkoiIiKC+Ph4HnvssdLlh0KIuuPGG28kJiaGZcuW8cgjj+hdTpU12NDNzMwsPbDxwoULxMXF8d5779GqVSu9SxNCVGLGjBmMHz+e22+/vfQMtfqiQYXu+fPnSw9sPHToEEOGDOGJJ56gW7du0qcVoh4JDw/nj3/8I0uWLGHhwoV6l1MlLh+6JSUlfP/99yiKwrZt2+jRowd33XUX/fr1kwMbhajH7rnnHsaOHcv27dvp1auX3uU4rfpCNyUFdu6EH36AQ4fAZgNvb7jhBrjxRujdG2rpbYCmaSQnJ2OxWFizZg3NmzfHbDYzb948AgMDa6UGIUTN8vDwYNasWSxatIhPPvmk3uw8dn0b3mgarFsHb78N+/c7/q5p4OkJBgOoKhQVgZub4/P9+8P06XDTTTXwUiAjI6O0T2u32zGbzcTFxdG8efMaeT4hhL40TePRRx8lNjaWCRMm6F1OqZrZZez0aZg3D775xhGqfn6OoC2PqoLV6vj47rvhySfB19fpF1Eeq9XKunXrSExMJDU1laFDh2I2m+nSpUu9nTwthHDe0aNHefDBB/nss8/qzIyj6g/d/fvhj3+E/HwIDKw4bK9kt0NeHjRrBp9+CtdwHI3NZuPbb7/FYrGU9nPi4+Pp06cP7u7uVX48IUT99vrrr5OXl8czzzyjdylAdW/teOgQjBsHxcUQFFT1atzcHPc7cQLGjoUvvoDQ0ErvpmkaP/30E4qisHbtWqKiooiLi+Ppp58mICCg6nUIIVzGQw89xOjRo/nll1/o1KmT3uVUqGoj3cJCGD4cTp6E6gi67Gzo2xeWLSt3tHzs2LHSPq3RaCQ+Pp64uLh6NzdPCFGz/vvf/7Jq1SqWLl2q+xTQ6hvpLlniGKFeywi3LEFBsG2bY7Q7alTpp3Nzc1m7di2KonDs2DGGDRvG/Pnz6dixo/RphRBlio+PZ8WKFSQmJhIfH693OeVyfqSbleUYlfr6OloE1aWwELy8KN6yha3ff4/FYmHnzp307duXuLg4YmNj681UECGEvvbv38+sWbNYuXIlvtVwof5aVc9I9/PPHRfBKglcq93OiydP8n1+PkEmE9PDwhhRztxYDbigqlw4epT5ffpwvndvzGYzL7zwAn5+fk6XJoQQAJ07d6ZPnz4sXbqURx99VO9yyuR86H7yCXh5VXqzV06dwt1gYE27dhwqLGTGsWO08/Ii6pJduoqKi8nNzSU3Nxej0Uiouzt/6dwZn7///ZpehBBCXDR9+nTuuusuRo4cScuWLfUu5yrOdZvz8yEjo9LQvaCqbMjLY2pYGD5GIzE+Pgz098eSm0tJSQnnsrM5cuQIaWlpqKpK82bNiIqKIjAiAp+DB6vj9QghGriQkBAmT57M4sWLqah9qhfnQvfQIfDwqHQ+bnpxMW5Ai9/2NFA1jUhVZe+ZMxw+fJgLFy4QFhZGdHQ0jSMi8PLywgDg7u6YyZCdfX2vRgghgLvuuouMjAy2bt2qdylXcS50c3Mdy3grUaCq+P42VePYsWP8sn8/JVYrhQYDkc2aERoaWvamwwYDmEy/r1gTQojr4O7uzuzZs1m8eDHFxcV6l3MZ53q6Tk7T8jEaOa+qaMD5ggL8/PxQ7Xa8iorIysrCbrdjLylBVVXc3NxwM5lwc3PD5OaGd0kJyvLluLdpQ1BQEMHBwQQFBZV+LDuCCSGqonfv3rRp04aPP/6Y++67T+9ySjkXusHBTt2shYcHduBYcTHNmzUj4+RJTnt7083fn9bh4aW30zQNu91Oid3uCGKbDaPVit3fn5Pp6ezbt4+cnJzSP9nZ2ZhMpsuC+NJALiuk/f39dZ8gLYTQ1+OPP87EiRMxm82EX5JBenIudKOjHVs1alqFo15vo5HB/v78PSuLZ5o04YibG+uzs1l+xXHJBoMBk8n0+/zboiJo1Ij7ypnioWkaFy5cIDs7+7Igvvjf48ePX/a5nJwczp8/T0BAwFXhXFFgezkxO0MIUX9ERkYyevRo3nzzTf7yl7/oXQ7gbOj6+EDLlnDqlOPjCvy5cWNeyMhg6KFD+BuN3G800ryyEWdBAQwaVO6XDQYDPj4++Pj4EBkZ6VTJdrud3NzcMkM6IyODX3755bLP5eTkYDAYyh05lxXYgYGBMpoWoo677777GDNmDHv37qVbt256l1OFFWkffggvvVTlJcBZWVkUFRfTrLyw1DTHBbQPP3SseNOJpmkUFhZeNlouK7Av/VxeXh7+/v6VtjouDWtvb29ZyixELUtKSuLf//43H330Ua0MlKpna8ecHOjVyzFXtwrbJ6qaxuHDh2natCm+ZY2SCwocQb51K9SzUaOqqmWOpisKbFVVywzk8gI7MDAQt+pcdi1EA6RpGg899BBms5lRl+zzUlOqZxlwUBDMmAGLFzs+dnK0ZjQYiIiI4NSpU0RFRXHZvS6eLPHSS/UucAGMRiPBwcEEO3mhEaCwsLDMkM7OzubQoUNXBbbVasXX19fpC4hBQUH4+PjIaFqISxgMBubOncv06dO59dZbdd0OtmpbO5aUwMiRcPBgldoMGpCeno6/vz8hFwNK0xyLIf7wB/jb366t+gZAVVXy8vIqbXVcGtY2m61KFxADAwPr7ObveXl53HnnnaSmppKQkMBjjz2GpmmX/VJRVZUTJ05gMpkIDw+XdwaiXH/9618xmUzMmTOnRp+nek+OOHEC7rzTEZhVOOSxsKiI9LQ0otq0wWQ0OtoV7dvDf/4D/v5OP46oXHFxsVMhffG/ubm5eHt7O30BMSgoCD8/v1oZTZ8/f57du3fzySefAPDOO+9gt9svC9Y1a9bw8ssvk5aWxpAhQ3jjjTd03WFK1F05OTmMHTuWd999l7Zt29bY81T/cT3p6TB+vGM2Q2Cg062BU5mZGEpKiPDxga5dHRfP5HRe3amqSn5+vtMXEHNycigqKiIwMNDpC4hBQUHXtcDliSeewNPTk+effx673Y7RaMRgMHDo0CEWLlzIkCFDGDduHDNnziQ8PJwnn3wSVVVldom4ymeffcaGDRt49913a2zgUL3H9QC0aAFJSfDyy44tHzXNcZJERW/rSkoIM5k4efYsmQ89RMQzz1TpgpyoOUajkYCAAAICAmjRooVT9yn+bae4stobaWlp7Nmz56qveXh4VHgBcciQIeVu6ZmZmclNv50ibTAYSlsM33zzDY0aNaJPnz4A+Pj4kJubW3o7Ia40evRoVq1axYYNGxgyZEitP/+17w7u5wd//SvcdRd88IHjKHaj0XFhzN399yPYS0pKj2R3u+ce9jdtyue7dvGByYT8SNRfHh4ehIWFERYW5tTtNU3j/Pnz5Y6c09PTGTBgQLn3z8nJKXNF0YkTJ/D396dRo0YAmEwmfHx8ZJQryuXm5sacOXN47rnn6Nu37++Los6ehV9+gV9/dew34+EBERHQsaNjgVg1bUVw/Ucy3HgjvPOOo+C9e2HfPkhOdhxc6esLXbpA584QEwN+fgxVVT6aOJE1a9YwfPjw638Fol4wGAz4+fnh5+dHs2bNqnRfu91OTk5OabBqmoamaRiNRs6fP3/Z42VnZ9OuXbvLtvRTVZWPPvqodIXipSPtgIAACecG6Ka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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2023,7 +511,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 14,
    "metadata": {
     "tags": []
    },
@@ -2039,388 +527,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2477,7 +584,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 24,
    "metadata": {
     "tags": []
    },
@@ -2487,453 +594,14 @@
      "output_type": "stream",
      "text": [
       "distance\n",
-      " [[ 0. 48. 91.]\n",
-      " [48.  0. 63.]\n",
-      " [91. 63.  0.]]\n"
+      " [[0. 1. 1.]\n",
+      " [1. 0. 1.]\n",
+      " [1. 1. 0.]]\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2947,12 +615,13 @@
     "n = 3\n",
     "num_qubits = n ** 2\n",
     "tsp = Tsp.create_random_instance(n, seed=123)\n",
-    "adj_matrix = nx.to_numpy_matrix(tsp.graph)\n",
+    "adj_matrix = rx.adjacency_matrix(tsp.graph)\n",
+    "graph = nx.from_numpy_array(adj_matrix)\n",
     "print('distance\\n', adj_matrix)\n",
     "\n",
-    "colors = ['r' for node in tsp.graph.nodes]\n",
-    "pos = [tsp.graph.nodes[node]['pos']  for node in tsp.graph.nodes]\n",
-    "draw_graph(tsp.graph, colors, pos)"
+    "colors = ['r' for node in graph.nodes]\n",
+    "pos = [node['pos'] for node in tsp.graph.nodes()]\n",
+    "draw_graph(graph, colors, pos)"
    ]
   },
   {
@@ -2964,7 +633,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 27,
    "metadata": {
     "tags": []
    },
@@ -2973,471 +642,13 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "order = (0, 1, 2) Distance = 202.0\n",
-      "Best order from brute force = (0, 1, 2) with total distance = 202.0\n"
+      "order = (0, 1, 2) Distance = 3.0\n",
+      "Best order from brute force = (0, 1, 2) with total distance = 3.0\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -3481,7 +692,7 @@
     "    edge_labels = nx.get_edge_attributes(G2, 'weight')\n",
     "    nx.draw_networkx_edge_labels(G2, pos, font_color='b', edge_labels=edge_labels)\n",
     "    \n",
-    "draw_tsp_solution(tsp.graph, best_order, colors, pos)"
+    "draw_tsp_solution(graph, best_order, colors, pos)"
    ]
   },
   {
@@ -3493,7 +704,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [
     {
@@ -3545,7 +756,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 29,
    "metadata": {
     "scrolled": true,
     "tags": []
@@ -3618,7 +829,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 31,
    "metadata": {
     "tags": []
    },
@@ -3647,7 +858,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 33,
    "metadata": {
     "tags": []
    },
@@ -3660,470 +871,12 @@
       "tsp objective: 202.0\n",
       "feasible: True\n",
       "solution: [0, 1, 2]\n",
-      "solution objective: 202.0\n"
+      "solution objective: 3.0\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -4144,7 +897,7 @@
     "z = tsp.interpret(x)\n",
     "print('solution:', z)\n",
     "print('solution objective:', tsp.tsp_value(z, adj_matrix))\n",
-    "draw_tsp_solution(tsp.graph, z, colors, pos)"
+    "draw_tsp_solution(graph, z, colors, pos)"
    ]
   },
   {
@@ -4157,7 +910,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -4169,7 +922,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 37,
    "metadata": {
     "tags": []
    },
@@ -4178,474 +931,16 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "energy: -7204.6830268662625\n",
-      "time: 8.034800052642822\n",
+      "energy: -7323.265478527832\n",
+      "time: 3.109360933303833\n",
       "feasible: True\n",
-      "solution: [1, 2, 0]\n",
-      "solution objective: 202.0\n"
+      "solution: [2, 0, 1]\n",
+      "solution objective: 3.0\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -4668,12 +963,12 @@
     "z = tsp.interpret(x)\n",
     "print('solution:', z)\n",
     "print('solution objective:', tsp.tsp_value(z, adj_matrix))\n",
-    "draw_tsp_solution(tsp.graph, z, colors, pos)"
+    "draw_tsp_solution(graph, z, colors, pos)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -4685,7 +980,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 40,
    "metadata": {
     "tags": []
    },
@@ -4695,473 +990,15 @@
      "output_type": "stream",
      "text": [
       "optimal function value: 202.0\n",
-      "optimal value: [0. 0. 1. 0. 1. 0. 1. 0. 0.]\n",
+      "optimal value: [1. 0. 0. 0. 0. 1. 0. 1. 0.]\n",
       "status: SUCCESS\n",
-      "solution: [1, 2, 0]\n",
-      "solution objective: 202.0\n"
+      "solution: [2, 0, 1]\n",
+      "solution objective: 3.0\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -5183,12 +1020,12 @@
     "z = tsp.interpret(x)\n",
     "print('solution:', z)\n",
     "print('solution objective:', tsp.tsp_value(z, adj_matrix))\n",
-    "draw_tsp_solution(tsp.graph, z, colors, pos)"
+    "draw_tsp_solution(graph, z, colors, pos)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 41,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2019-08-22T01:58:30.581695Z",
@@ -5196,19 +1033,11 @@
     }
    },
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/atsushi/github/aqua-public/qiskit/aqua/operators/operator_globals.py:49: DeprecationWarning: `from_label` is deprecated and will be removed no earlier than 3 months after the release date. Use Pauli(label) instead.\n",
-      "  X = make_immutable(PauliOp(Pauli.from_label('X')))\n"
-     ]
-    },
     {
      "data": {
       "text/html": [
-       "<h3>Version Information</h3><table><tr><th>Qiskit Software</th><th>Version</th></tr><tr><td>Qiskit</td><td>None</td></tr><tr><td>Terra</td><td>0.17.0.dev0+89d15f3</td></tr><tr><td>Aer</td><td>0.6.1</td></tr><tr><td>Ignis</td><td>0.4.0</td></tr><tr><td>Aqua</td><td>0.9.0.dev0+84ce2d6</td></tr><tr><td>IBM Q Provider</td><td>0.11.0</td></tr><tr><th>System information</th></tr><tr><td>Python</td><td>3.8.6 (default, Mar 10 2021, 14:41:09) \n",
-       "[Clang 12.0.0 (clang-1200.0.32.29)]</td></tr><tr><td>OS</td><td>Darwin</td></tr><tr><td>CPUs</td><td>8</td></tr><tr><td>Memory (Gb)</td><td>32.0</td></tr><tr><td colspan='2'>Thu Mar 11 18:37:49 2021 JST</td></tr></table>"
+       "<h3>Version Information</h3><table><tr><th>Qiskit Software</th><th>Version</th></tr><tr><td>Qiskit</td><td>None</td></tr><tr><td>Terra</td><td>0.18.0.dev0+9229c5c</td></tr><tr><td>Aer</td><td>0.8.2</td></tr><tr><td>Ignis</td><td>0.6.0</td></tr><tr><td>Aqua</td><td>0.10.0.dev0+fcc73ed</td></tr><tr><td>IBM Q Provider</td><td>0.13.1</td></tr><tr><th>System information</th></tr><tr><td>Python</td><td>3.8.10 (default, May  4 2021, 03:06:52) \n",
+       "[Clang 12.0.0 (clang-1200.0.32.29)]</td></tr><tr><td>OS</td><td>Darwin</td></tr><tr><td>CPUs</td><td>4</td></tr><tr><td>Memory (Gb)</td><td>16.0</td></tr><tr><td colspan='2'>Fri May 28 19:39:13 2021 JST</td></tr></table>"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -5262,7 +1091,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.5"
+   "version": "3.8.10"
   },
   "varInspector": {
    "cols": {
diff --git a/qiskit_optimization/applications/clique.py b/qiskit_optimization/applications/clique.py
index da7f730c6..d32137709 100644
--- a/qiskit_optimization/applications/clique.py
+++ b/qiskit_optimization/applications/clique.py
@@ -11,14 +11,17 @@
 # that they have been altered from the originals.
 
 """An application class for the clique."""
-from typing import Optional, Union, List, Dict
+from typing import Dict, List, Optional, Union
 
 import networkx as nx
 import numpy as np
+import retworkx as rx
 from docplex.mp.model import Model
+from retworkx.visualization import mpl_draw
 
 from qiskit_optimization.algorithms import OptimizationResult
 from qiskit_optimization.problems.quadratic_program import QuadraticProgram
+
 from .graph_optimization_application import GraphOptimizationApplication
 
 
@@ -31,7 +34,7 @@ class Clique(GraphOptimizationApplication):
     """
 
     def __init__(
-        self, graph: Union[nx.Graph, np.ndarray, List], size: Optional[int] = None
+        self, graph: Union[rx.PyGraph, nx.Graph, np.ndarray, List], size: Optional[int] = None
     ) -> None:
         """
         Args:
@@ -53,12 +56,11 @@ def to_quadratic_program(self) -> QuadraticProgram:
             The :class:`~qiskit_optimization.problems.QuadraticProgram` created
             from the clique problem instance.
         """
-        complement_g = nx.complement(self._graph)
-
+        complement_g = rx.complement(self._graph)
         mdl = Model(name="Clique")
-        n = self._graph.number_of_nodes()
+        n = self._graph.num_nodes()
         x = {i: mdl.binary_var(name="x_{0}".format(i)) for i in range(n)}
-        for w, v in complement_g.edges:
+        for w, v in complement_g.edge_list():
             mdl.add_constraint(x[w] + x[v] <= 1)
         if self.size is None:
             mdl.maximize(mdl.sum(x[i] for i in x))
@@ -96,13 +98,13 @@ def _draw_result(
             pos: The positions of nodes
         """
         x = self._result_to_x(result)
-        nx.draw(self._graph, node_color=self._node_colors(x), pos=pos, with_labels=True)
+        mpl_draw(self._graph, node_color=self._node_colors(x), pos=pos, with_labels=True)
 
     def _node_colors(self, x: np.ndarray) -> List[str]:
         # Return a list of strings for draw.
         # Color a node with red when the corresponding variable is 1.
         # Otherwise color it with dark gray.
-        return ["r" if x[node] else "darkgrey" for node in self._graph.nodes]
+        return ["r" if x[node] else "darkgrey" for node in self._graph.nodes()]
 
     @property
     def size(self) -> int:
diff --git a/qiskit_optimization/applications/graph_optimization_application.py b/qiskit_optimization/applications/graph_optimization_application.py
index 00838d7fa..389fbd626 100644
--- a/qiskit_optimization/applications/graph_optimization_application.py
+++ b/qiskit_optimization/applications/graph_optimization_application.py
@@ -13,13 +13,16 @@
 """An abstract class for graph optimization application classes."""
 
 from abc import abstractmethod
-from typing import Union, Optional, Dict, List
+from typing import Dict, Optional, Union, List
 
 import networkx as nx
 import numpy as np
-from qiskit.exceptions import MissingOptionalLibraryError
+import retworkx as rx
+from retworkx.visualization import mpl_draw
 
+from qiskit.exceptions import MissingOptionalLibraryError
 from qiskit_optimization.algorithms import OptimizationResult
+
 from .optimization_application import OptimizationApplication
 
 try:
@@ -35,14 +38,21 @@ class GraphOptimizationApplication(OptimizationApplication):
     An abstract class for graph optimization applications.
     """
 
-    def __init__(self, graph: Union[nx.Graph, np.ndarray, List]) -> None:
+    def __init__(self, graph: Union[rx.PyGraph, nx.Graph, np.ndarray, List]) -> None:
         """
         Args:
             graph: A graph representing a problem. It can be specified directly as a
             NetworkX Graph, or as an array or list if format suitable to build out a NetworkX graph.
         """
         # The view of the graph is stored which means the graph can not be changed.
-        self._graph = nx.Graph(graph).copy(as_view=True)
+        if isinstance(graph, rx.PyGraph):
+            self._graph = graph.copy()
+        elif isinstance(graph, nx.Graph):
+            self._graph = rx.networkx_converter(graph)
+        elif isinstance(graph, (np.ndarray, List)):
+            self._graph = rx.PyGraph.from_adjacency_matrix(np.asarray(graph))
+        else:
+            raise TypeError("graph should be rx.PyGraph or nx.Graph")
 
     def draw(
         self,
@@ -66,7 +76,7 @@ def draw(
             )
 
         if result is None:
-            nx.draw(self._graph, pos=pos, with_labels=True)
+            mpl_draw(self._graph, pos=pos, with_labels=True)
         else:
             self._draw_result(result, pos)
 
@@ -85,7 +95,7 @@ def _draw_result(
         pass
 
     @property
-    def graph(self) -> nx.Graph:
+    def graph(self) -> rx.PyGraph:
         """Getter of the graph
 
         Returns:
@@ -94,7 +104,7 @@ def graph(self) -> nx.Graph:
         return self._graph
 
     @staticmethod
-    def random_graph(num_nodes: int, num_edges: int, seed: Optional[int] = None) -> nx.Graph:
+    def random_graph(num_nodes: int, num_edges: int, seed: Optional[int] = None) -> rx.PyGraph:
         """
 
         Args:
@@ -105,5 +115,5 @@ def random_graph(num_nodes: int, num_edges: int, seed: Optional[int] = None) ->
         Returns:
             A random graph of NetworkX
         """
-        graph = nx.gnm_random_graph(num_nodes, num_edges, seed)
+        graph = rx.undirected_gnm_random_graph(num_nodes, num_edges, seed)
         return graph
diff --git a/qiskit_optimization/applications/graph_partition.py b/qiskit_optimization/applications/graph_partition.py
index 9d3370e90..55d503df4 100644
--- a/qiskit_optimization/applications/graph_partition.py
+++ b/qiskit_optimization/applications/graph_partition.py
@@ -14,7 +14,8 @@
 
 from typing import Dict, List, Optional, Union
 
-import networkx as nx
+import retworkx as rx
+from retworkx.visualization import mpl_draw
 import numpy as np
 from docplex.mp.model import Model
 
@@ -39,13 +40,13 @@ def to_quadratic_program(self) -> QuadraticProgram:
             from the graph partition instance.
         """
         mdl = Model(name="Graph partition")
-        n = self._graph.number_of_nodes()
+        n = self._graph.num_nodes()
         x = {i: mdl.binary_var(name="x_{0}".format(i)) for i in range(n)}
-        for w, v in self._graph.edges:
-            self._graph.edges[w, v].setdefault("weight", 1)
+        for i, j in self._graph.edge_list():
+            self.graph.update_edge(i, j, 1)
         objective = mdl.sum(
-            self._graph.edges[i, j]["weight"] * (x[i] + x[j] - 2 * x[i] * x[j])
-            for i, j in self._graph.edges
+            self._graph.get_edge_data(i, j) * (x[i] + x[j] - 2 * x[i] * x[j])
+            for i, j in self._graph.edge_list()
         )
         mdl.minimize(objective)
         mdl.add_constraint(mdl.sum([x[i] for i in x]) == n // 2)
@@ -83,10 +84,10 @@ def _draw_result(
             pos: The positions of nodes
         """
         x = self._result_to_x(result)
-        nx.draw(self._graph, node_color=self._node_colors(x), pos=pos, with_labels=True)
+        mpl_draw(self._graph, node_color=self._node_colors(x), pos=pos, with_labels=True)
 
     def _node_colors(self, x: np.ndarray) -> List[str]:
         # Return a list of strings for draw.
         # Color a node with red when the corresponding variable is 1.
         # Otherwise color it with blue.
-        return ["r" if x[node] else "b" for node in self._graph.nodes]
+        return ["r" if x[node] else "b" for node in self._graph.nodes()]
diff --git a/qiskit_optimization/applications/max_cut.py b/qiskit_optimization/applications/max_cut.py
index 3510773cc..bc4ae0085 100644
--- a/qiskit_optimization/applications/max_cut.py
+++ b/qiskit_optimization/applications/max_cut.py
@@ -14,7 +14,7 @@
 """An application class for the Max-cut."""
 
 from typing import List, Dict, Optional, Union
-import networkx as nx
+from retworkx.visualization import mpl_draw
 import numpy as np
 from docplex.mp.model import Model
 
@@ -40,15 +40,11 @@ def to_quadratic_program(self) -> QuadraticProgram:
             from the Max-cut problem instance.
         """
         mdl = Model(name="Max-cut")
-        x = {
-            i: mdl.binary_var(name="x_{0}".format(i)) for i in range(self._graph.number_of_nodes())
-        }
-        for w, v in self._graph.edges:
-            self._graph.edges[w, v].setdefault("weight", 1)
+        x = {i: mdl.binary_var(name="x_{0}".format(i)) for i in range(self._graph.num_nodes())}
         objective = mdl.sum(
-            self._graph.edges[i, j]["weight"] * x[i] * (1 - x[j])
-            + self._graph.edges[i, j]["weight"] * x[j] * (1 - x[i])
-            for i, j in self._graph.edges
+            self._graph.get_edge_data(i, j) * x[i] * (1 - x[j])
+            + self._graph.get_edge_data(i, j) * x[j] * (1 - x[i])
+            for i, j in self._graph.edge_list()
         )
         mdl.maximize(objective)
         op = QuadraticProgram()
@@ -67,7 +63,7 @@ def _draw_result(
             pos: The positions of nodes
         """
         x = self._result_to_x(result)
-        nx.draw(self._graph, node_color=self._node_color(x), pos=pos, with_labels=True)
+        mpl_draw(self._graph, node_color=self._node_color(x), pos=pos, with_labels=True)
 
     def interpret(self, result: Union[OptimizationResult, np.ndarray]) -> List[List[int]]:
         """Interpret a result as two lists of node indices
diff --git a/qiskit_optimization/applications/stable_set.py b/qiskit_optimization/applications/stable_set.py
index ae2fb8e07..9154a4367 100644
--- a/qiskit_optimization/applications/stable_set.py
+++ b/qiskit_optimization/applications/stable_set.py
@@ -14,7 +14,7 @@
 
 from typing import Dict, List, Optional, Union
 
-import networkx as nx
+from retworkx.visualization import mpl_draw
 import numpy as np
 from docplex.mp.model import Model
 
@@ -40,12 +40,10 @@ def to_quadratic_program(self) -> QuadraticProgram:
             from the stable set instance.
         """
         mdl = Model(name="Stable set")
-        n = self._graph.number_of_nodes()
+        n = self._graph.num_nodes()
         x = {i: mdl.binary_var(name="x_{0}".format(i)) for i in range(n)}
-        for w, v in self._graph.edges:
-            self._graph.edges[w, v].setdefault("weight", 1)
         objective = mdl.sum(x[i] for i in x)
-        for w, v in self._graph.edges:
+        for w, v in self._graph.edge_list():
             mdl.add_constraint(x[w] + x[v] <= 1)
         mdl.maximize(objective)
         op = QuadraticProgram()
@@ -80,10 +78,10 @@ def _draw_result(
             pos: The positions of nodes
         """
         x = self._result_to_x(result)
-        nx.draw(self._graph, node_color=self._node_colors(x), pos=pos, with_labels=True)
+        mpl_draw(self._graph, node_color=self._node_colors(x), pos=pos, with_labels=True)
 
     def _node_colors(self, x: np.ndarray):
         # Return a list of strings for draw.
         # Color a node with red when the corresponding variable is 1.
         # Otherwise color it with dark gray.
-        return ["r" if x[node] == 1 else "darkgrey" for node in self._graph.nodes]
+        return ["r" if x[node] == 1 else "darkgrey" for node in self._graph.nodes()]
diff --git a/qiskit_optimization/applications/tsp.py b/qiskit_optimization/applications/tsp.py
index 822f8a601..a911676bc 100644
--- a/qiskit_optimization/applications/tsp.py
+++ b/qiskit_optimization/applications/tsp.py
@@ -13,11 +13,13 @@
 """An application class for Traveling salesman problem (TSP)."""
 from typing import Dict, List, Optional, Union
 
-import networkx as nx
 import numpy as np
+import retworkx as rx
 from docplex.mp.model import Model
+from retworkx.visualization import mpl_draw
 
 from qiskit.utils import algorithm_globals
+
 from ..algorithms import OptimizationResult
 from ..exceptions import QiskitOptimizationError
 from ..problems.quadratic_program import QuadraticProgram
@@ -42,14 +44,14 @@ def to_quadratic_program(self) -> QuadraticProgram:
             from the traveling salesman problem instance.
         """
         mdl = Model(name="TSP")
-        n = self._graph.number_of_nodes()
+        n = self._graph.num_nodes()
         x = {
             (i, k): mdl.binary_var(name="x_{0}_{1}".format(i, k))
             for i in range(n)
             for k in range(n)
         }
         tsp_func = mdl.sum(
-            self._graph.edges[i, j]["weight"] * x[(i, k)] * x[(j, (k + 1) % n)]
+            self._graph.get_edge_data(i, j) * x[(i, k)] * x[(j, (k + 1) % n)]
             for i in range(n)
             for j in range(n)
             for k in range(n)
@@ -101,11 +103,11 @@ def _draw_result(
             pos: The positions of nodes
         """
         x = self._result_to_x(result)
-        nx.draw(self._graph, with_labels=True, pos=pos)
-        nx.draw_networkx_edges(
+        mpl_draw(self._graph, with_labels=True, pos=pos)
+        mpl_draw(
             self._graph,
             pos,
-            edgelist=self._edgelist(x),
+            edge_list=self._edgelist(x),
             width=8,
             alpha=0.5,
             edge_color="tab:red",
@@ -132,12 +134,15 @@ def create_random_instance(n: int, low: int = 0, high: int = 100, seed: int = No
         """
         if seed:
             algorithm_globals.random_seed = seed
-        coord = algorithm_globals.random.uniform(low, high, (n, 2))
-        pos = {i: (coord_[0], coord_[1]) for i, coord_ in enumerate(coord)}
-        graph = nx.random_geometric_graph(n, np.hypot(high - low, high - low) + 1, pos=pos)
-        for w, v in graph.edges:
-            delta = [graph.nodes[w]["pos"][i] - graph.nodes[v]["pos"][i] for i in range(2)]
-            graph.edges[w, v]["weight"] = np.rint(np.hypot(delta[0], delta[1]))
+        dim = 2
+        pos = algorithm_globals.random.uniform(low, high, (n, dim))
+        graph = rx.random_geometric_graph(n, np.hypot(high - low, high - low) + 1, pos=pos)
+        for i, j in graph.edge_list():
+            delta = [
+                graph.get_node_data(i)["pos"][d] - graph.get_node_data(j)["pos"][d]
+                for d in range(dim)
+            ]
+            graph.update_edge(i, j, np.rint(np.hypot(delta[0], delta[1])))
         return Tsp(graph)
 
     @staticmethod
@@ -192,12 +197,13 @@ def parse_tsplib_format(filename: str) -> "Tsp":
         y_max = max(coord_[1] for coord_ in coord)
         y_min = min(coord_[1] for coord_ in coord)
 
-        graph = nx.random_geometric_graph(
+        # TODO: fix and add test
+        graph = rx.random_geometric_graph(
             len(coord), np.hypot(x_max - x_min, y_max - y_min) + 1, pos=coord
         )
         for w, v in graph.edges:
             delta = [graph.nodes[w]["pos"][i] - graph.nodes[v]["pos"][i] for i in range(2)]
-            graph.edges[w, v]["weight"] = np.rint(np.hypot(delta[0], delta[1]))
+            graph.update_edge(w, v, np.rint(np.hypot(delta[0], delta[1])))
         return Tsp(graph)
 
     @staticmethod
diff --git a/qiskit_optimization/applications/vehicle_routing.py b/qiskit_optimization/applications/vehicle_routing.py
index 5be280d1d..40df656a2 100644
--- a/qiskit_optimization/applications/vehicle_routing.py
+++ b/qiskit_optimization/applications/vehicle_routing.py
@@ -13,13 +13,15 @@
 """An application class for the vehicle routing problem."""
 
 import itertools
-import random
 from typing import List, Dict, Union, Optional
 
 import networkx as nx
+import retworkx as rx
+from retworkx.visualization import mpl_draw
 import numpy as np
 from docplex.mp.model import Model
 
+from qiskit.utils import algorithm_globals
 from qiskit_optimization.algorithms import OptimizationResult
 from qiskit_optimization.problems.quadratic_program import QuadraticProgram
 from .graph_optimization_application import GraphOptimizationApplication
@@ -34,7 +36,7 @@ class VehicleRouting(GraphOptimizationApplication):
 
     def __init__(
         self,
-        graph: Union[nx.Graph, np.ndarray, List],
+        graph: Union[rx.PyGraph, nx.Graph, np.ndarray, List],
         num_vehicles: int = 2,
         depot: int = 0,
     ) -> None:
@@ -59,7 +61,7 @@ def to_quadratic_program(self) -> QuadraticProgram:
             from the vehicle routing problem instance.
         """
         mdl = Model(name="Vehicle routing")
-        n = self._graph.number_of_nodes()
+        n = self._graph.num_nodes()
         x = {}
         for i in range(n):
             for j in range(n):
@@ -67,7 +69,7 @@ def to_quadratic_program(self) -> QuadraticProgram:
                     x[(i, j)] = mdl.binary_var(name="x_{0}_{1}".format(i, j))
         mdl.minimize(
             mdl.sum(
-                self._graph.edges[i, j]["weight"] * x[(i, j)]
+                self._graph.get_edge_data(i, j) * x[(i, j)]
                 for i in range(n)
                 for j in range(n)
                 if i != j
@@ -113,7 +115,7 @@ def interpret(self, result: Union[OptimizationResult, np.ndarray]) -> List[List[
             A list of the routes for each vehicle
         """
         x = self._result_to_x(result)
-        n = self._graph.number_of_nodes()
+        n = self._graph.num_nodes()
         idx = 0
         edge_list = []
         for i in range(n):
@@ -158,11 +160,11 @@ def _draw_result(
         import matplotlib.pyplot as plt
 
         route_list = self.interpret(result)
-        nx.draw(self._graph, with_labels=True, pos=pos)
-        nx.draw_networkx_edges(
+        mpl_draw(self._graph, with_labels=True, pos=pos)
+        mpl_draw(
             self._graph,
             pos,
-            edgelist=self._edgelist(route_list),
+            edge_list=self._edgelist(route_list),
             width=8,
             alpha=0.5,
             edge_color=self._edge_color(route_list),
@@ -238,10 +240,15 @@ def create_random_instance(
         Returns:
             A VehicleRouting instance created from the input information
         """
-        random.seed(seed)
-        pos = {i: (random.randint(low, high), random.randint(low, high)) for i in range(n)}
-        graph = nx.random_geometric_graph(n, np.hypot(high - low, high - low) + 1, pos=pos)
-        for w, v in graph.edges:
-            delta = [graph.nodes[w]["pos"][i] - graph.nodes[v]["pos"][i] for i in range(2)]
-            graph.edges[w, v]["weight"] = np.rint(np.hypot(delta[0], delta[1]))
+        if seed:
+            algorithm_globals.random_seed = seed
+        dim = 2
+        pos = algorithm_globals.random.uniform(low, high, (n, dim))
+        graph = rx.random_geometric_graph(n, np.hypot(high - low, high - low) + 1, pos=pos)
+        for i, j in graph.edge_list():
+            delta = [
+                graph.get_node_data(i)["pos"][d] - graph.get_node_data(j)["pos"][d]
+                for d in range(dim)
+            ]
+            graph.update_edge(i, j, np.rint(np.hypot(delta[0], delta[1])))
         return VehicleRouting(graph, num_vehicle, depot)
diff --git a/qiskit_optimization/applications/vertex_cover.py b/qiskit_optimization/applications/vertex_cover.py
index a645cd95b..896509591 100644
--- a/qiskit_optimization/applications/vertex_cover.py
+++ b/qiskit_optimization/applications/vertex_cover.py
@@ -14,7 +14,8 @@
 
 from typing import Dict, List, Optional, Union
 
-import networkx as nx
+import retworkx as rx
+from retworkx.visualization import mpl_draw
 import numpy as np
 from docplex.mp.model import Model
 
@@ -39,10 +40,10 @@ def to_quadratic_program(self) -> QuadraticProgram:
             from the vertex cover instance.
         """
         mdl = Model(name="Vertex cover")
-        n = self._graph.number_of_nodes()
+        n = self._graph.num_nodes()
         x = {i: mdl.binary_var(name="x_{0}".format(i)) for i in range(n)}
         objective = mdl.sum(x[i] for i in x)
-        for w, v in self._graph.edges:
+        for w, v in self._graph.edge_list():
             mdl.add_constraint(x[w] + x[v] >= 1)
         mdl.minimize(objective)
         op = QuadraticProgram()
@@ -77,10 +78,10 @@ def _draw_result(
             pos: The positions of nodes
         """
         x = self._result_to_x(result)
-        nx.draw(self._graph, node_color=self._node_colors(x), pos=pos, with_labels=True)
+        mpl_draw(self._graph, node_color=self._node_colors(x), pos=pos, with_labels=True)
 
     def _node_colors(self, x: np.ndarray) -> List[str]:
         # Return a list of strings for draw.
         # Color a node with red when the corresponding variable is 1.
         # Otherwise color it with dark gray.
-        return ["r" if x[node] else "darkgrey" for node in self._graph.nodes]
+        return ["r" if x[node] else "darkgrey" for node in self._graph.nodes()]
diff --git a/requirements.txt b/requirements.txt
index 3c7f538c9..826211319 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -5,4 +5,5 @@ docplex; sys_platform != 'darwin'
 docplex==2.15.194; sys_platform == 'darwin'
 setuptools>=40.1.0
 networkx>=2.2
+retworkx>=0.9.0
 dataclasses; python_version < '3.7'
diff --git a/test/applications/test_clique.py b/test/applications/test_clique.py
index c219bfcb7..c76f03432 100644
--- a/test/applications/test_clique.py
+++ b/test/applications/test_clique.py
@@ -13,7 +13,8 @@
 """ Test Clique class"""
 
 from test.optimization_test_case import QiskitOptimizationTestCase
-import networkx as nx
+
+import retworkx as rx
 
 from qiskit_optimization import QuadraticProgram
 from qiskit_optimization.algorithms import OptimizationResult, OptimizationResultStatus
@@ -27,7 +28,7 @@ class TestClique(QiskitOptimizationTestCase):
     def setUp(self):
         """Set up for the tests"""
         super().setUp()
-        self.graph = nx.gnm_random_graph(5, 8, 123)
+        self.graph = rx.undirected_gnm_random_graph(5, 8, 123)
         op = QuadraticProgram()
         for _ in range(5):
             op.binary_var()
diff --git a/test/applications/test_graph_partition.py b/test/applications/test_graph_partition.py
index fb0ef1e60..6e823325c 100644
--- a/test/applications/test_graph_partition.py
+++ b/test/applications/test_graph_partition.py
@@ -12,7 +12,8 @@
 
 """ Test GraphPartinioning class"""
 from test.optimization_test_case import QiskitOptimizationTestCase
-import networkx as nx
+
+import retworkx as rx
 
 from qiskit_optimization import QuadraticProgram
 from qiskit_optimization.algorithms import OptimizationResult, OptimizationResultStatus
@@ -26,7 +27,7 @@ class TestGraphPartition(QiskitOptimizationTestCase):
     def setUp(self):
         """Set up for the tests"""
         super().setUp()
-        self.graph = nx.gnm_random_graph(4, 4, 123)
+        self.graph = rx.undirected_gnm_random_graph(4, 4, 123)
         op = QuadraticProgram()
         for _ in range(4):
             op.binary_var()
diff --git a/test/applications/test_tsp.py b/test/applications/test_tsp.py
index b8552a321..fcebd165b 100644
--- a/test/applications/test_tsp.py
+++ b/test/applications/test_tsp.py
@@ -29,16 +29,10 @@ class TestTsp(QiskitOptimizationTestCase):
 
     def setUp(self):
         super().setUp()
-        random.seed(123)
+        seed = 123
         low = 0
         high = 100
-        pos = {i: (random.randint(low, high), random.randint(low, high)) for i in range(4)}
-        self.graph = nx.random_geometric_graph(4, np.hypot(high - low, high - low) + 1, pos=pos)
-        for w, v in self.graph.edges:
-            delta = [
-                self.graph.nodes[w]["pos"][i] - self.graph.nodes[v]["pos"][i] for i in range(2)
-            ]
-            self.graph.edges[w, v]["weight"] = np.rint(np.hypot(delta[0], delta[1]))
+        self.graph = Tsp.create_random_instance(4, low, high, seed).graph
 
         op = QuadraticProgram()
         for i in range(16):
@@ -66,7 +60,7 @@ def test_to_quadratic_program(self):
         self.assertEqual(obj.constant, 0)
         self.assertDictEqual(obj.linear.to_dict(), {})
         for edge, val in obj.quadratic.to_dict().items():
-            self.assertEqual(val, self.graph.edges[edge[0] // 4, edge[1] // 4]["weight"])
+            self.assertEqual(val, self.graph.get_edge_data(edge[0] // 4, edge[1] // 4))
 
         # Test constraint
         lin = op.linear_constraints
@@ -96,7 +90,5 @@ def test_edgelist(self):
     def test_create_random_instance(self):
         """Test create_random_instance"""
         tsp = Tsp.create_random_instance(n=3, seed=123)
-        graph = tsp.graph
-        edge_weight = [graph.edges[edge]["weight"] for edge in graph.edges]
         expected_weight = [48, 91, 63]
-        self.assertEqual(edge_weight, expected_weight)
+        self.assertEqual(tsp.graph.edges(), expected_weight)
diff --git a/test/applications/test_vehicle_routing.py b/test/applications/test_vehicle_routing.py
index bbdb2006e..47190fe38 100644
--- a/test/applications/test_vehicle_routing.py
+++ b/test/applications/test_vehicle_routing.py
@@ -28,16 +28,10 @@ class TestVehicleRouting(QiskitOptimizationTestCase):
 
     def setUp(self):
         super().setUp()
-        random.seed(600)
+        seed = 600
         low = 0
         high = 100
-        pos = {i: (random.randint(low, high), random.randint(low, high)) for i in range(4)}
-        self.graph = nx.random_geometric_graph(4, np.hypot(high - low, high - low) + 1, pos=pos)
-        for w, v in self.graph.edges:
-            delta = [
-                self.graph.nodes[w]["pos"][i] - self.graph.nodes[v]["pos"][i] for i in range(2)
-            ]
-            self.graph.edges[w, v]["weight"] = np.rint(np.hypot(delta[0], delta[1]))
+        self.graph = VehicleRouting.create_random_instance(4, low, high, seed).graph
         op = QuadraticProgram()
         for i in range(12):
             op.binary_var()
@@ -344,15 +338,6 @@ def test_edge_color_nv3(self):
             [0.0, 0.0, 1 / 3, 1 / 3, 2 / 3, 2 / 3],
         )
 
-    def test_create_random_instance(self):
-        """Test create_random_instance"""
-        vehicle_routing = VehicleRouting.create_random_instance(n=4, seed=600)
-        graph = vehicle_routing.graph
-        for node in graph.nodes:
-            self.assertEqual(graph.nodes[node]["pos"], self.graph.nodes[node]["pos"])
-        for edge in graph.edges:
-            self.assertEqual(graph.edges[edge]["weight"], self.graph.edges[edge]["weight"])
-
     def test_num_vehicles(self):
         """Test num_vehicles"""
         vehicle_routing = VehicleRouting(self.graph, num_vehicles=2)