diff --git a/.pylintdict b/.pylintdict
index 77f56f507..07e036844 100644
--- a/.pylintdict
+++ b/.pylintdict
@@ -110,6 +110,7 @@ nosignatures
 np
 num
 numpy
+october
 optimality
 optimizationresult
 optimizationresultstatus
diff --git a/docs/tutorials/01_quadratic_program.ipynb b/docs/tutorials/01_quadratic_program.ipynb
index 1678c47bf..ecaa78fbf 100644
--- a/docs/tutorials/01_quadratic_program.ipynb
+++ b/docs/tutorials/01_quadratic_program.ipynb
@@ -55,7 +55,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from qiskit_optimization import QuadraticProgram"
+    "from qiskit_optimization import QuadraticProgram\n",
+    "from qiskit_optimization.translators import from_docplex_mp"
    ]
   },
   {
@@ -72,7 +73,7 @@
     "Qiskit's optimization module supports the conversion from Docplex model. You can easily make a model of an optimization problem with Docplex.\n",
     "You can find the documentation of Docplex at https://ibmdecisionoptimization.github.io/docplex-doc/mp/index.html\n",
     "\n",
-    "You can load a Docplex model to `QuadraticProgram` by invoking `from_docplex`."
+    "You can load a Docplex model to `QuadraticProgram` by using `from_docplex_mp` function."
    ]
   },
   {
@@ -167,8 +168,7 @@
    ],
    "source": [
     "# load from a Docplex model\n",
-    "mod = QuadraticProgram()\n",
-    "mod.from_docplex(mdl)\n",
+    "mod = from_docplex_mp(mdl)\n",
     "print(mod.export_as_lp_string())"
    ]
   },
@@ -821,4 +821,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
+}
\ No newline at end of file
diff --git a/docs/tutorials/04_grover_optimizer.ipynb b/docs/tutorials/04_grover_optimizer.ipynb
index 01e3ac102..4b1ba6e60 100644
--- a/docs/tutorials/04_grover_optimizer.ipynb
+++ b/docs/tutorials/04_grover_optimizer.ipynb
@@ -69,7 +69,7 @@
     "\\min_{x \\in \\{0, 1\\}^3} -2x_1x_3 - x_2x_3 - 1x_1 + 2x_2 - 3x_3.\n",
     "\\end{eqnarray}\n",
     "\n",
-    "For our initial steps, we create a docplex model that defines the problem above, and then use the ```from_docplex()``` function to convert the model to a ```QuadraticProgram```, which can be used to represent a QUBO in Qiskit Optimization."
+    "For our initial steps, we create a docplex model that defines the problem above, and then use the ```from_docplex_mp()``` function to convert the model to a ```QuadraticProgram```, which can be used to represent a QUBO in Qiskit Optimization."
    ]
   },
   {
@@ -81,6 +81,7 @@
     "from qiskit.algorithms import NumPyMinimumEigensolver\n",
     "from qiskit_optimization.algorithms import GroverOptimizer, MinimumEigenOptimizer\n",
     "from qiskit_optimization.problems import QuadraticProgram\n",
+    "from qiskit_optimization.translators import from_docplex_mp\n",
     "from qiskit import BasicAer\n",
     "from docplex.mp.model import Model\n",
     "\n",
@@ -124,8 +125,7 @@
     "x1 = model.binary_var(name='x1')\n",
     "x2 = model.binary_var(name='x2')\n",
     "model.minimize(-x0+2*x1-3*x2-2*x0*x2-1*x1*x2)\n",
-    "qp = QuadraticProgram()\n",
-    "qp.from_docplex(model)\n",
+    "qp = from_docplex_mp(model)\n",
     "print(qp.export_as_lp_string())"
    ]
   },
@@ -312,4 +312,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 2
-}
+}
\ No newline at end of file
diff --git a/docs/tutorials/05_admm_optimizer.ipynb b/docs/tutorials/05_admm_optimizer.ipynb
index 49d588561..bea2ef349 100644
--- a/docs/tutorials/05_admm_optimizer.ipynb
+++ b/docs/tutorials/05_admm_optimizer.ipynb
@@ -94,6 +94,7 @@
     "from qiskit_optimization.algorithms import CobylaOptimizer, MinimumEigenOptimizer\n",
     "from qiskit_optimization.problems import QuadraticProgram\n",
     "from qiskit_optimization.algorithms.admm_optimizer import ADMMParameters, ADMMOptimizer\n",
+    "from qiskit_optimization.translators import from_docplex_mp\n",
     "\n",
     "# If CPLEX is installed, you can uncomment this line to import the CplexOptimizer.\n",
     "# CPLEX can be used in this tutorial to solve the convex continuous problem, \n",
@@ -199,8 +200,7 @@
     "mdl.add_constraint(v + w == 1, \"cons3\")\n",
     "\n",
     "# load quadratic program from docplex model\n",
-    "qp = QuadraticProgram()\n",
-    "qp.from_docplex(mdl)\n",
+    "qp = from_docplex_mp(mdl)\n",
     "print(qp.export_as_lp_string())"
    ]
   },
@@ -530,4 +530,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 2
-}
+}
\ No newline at end of file
diff --git a/docs/tutorials/06_examples_max_cut_and_tsp.ipynb b/docs/tutorials/06_examples_max_cut_and_tsp.ipynb
index c049119e6..dd07d8819 100644
--- a/docs/tutorials/06_examples_max_cut_and_tsp.ipynb
+++ b/docs/tutorials/06_examples_max_cut_and_tsp.ipynb
@@ -109,7 +109,6 @@
     "# useful additional packages \n",
     "import matplotlib.pyplot as plt\n",
     "import matplotlib.axes as axes\n",
-    "%matplotlib inline\n",
     "import numpy as np\n",
     "import networkx as nx\n",
     "\n",
@@ -137,395 +136,13 @@
    "metadata": {},
    "outputs": [
     {
+     "output_type": "display_data",
      "data": {
-      "image/png": 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\n"
      },
-     "metadata": {},
-     "output_type": "display_data"
+     "metadata": {}
     }
    ],
    "source": [
@@ -558,13 +175,10 @@
    },
    "outputs": [
     {
-     "name": "stdout",
      "output_type": "stream",
+     "name": "stdout",
      "text": [
-      "[[0. 1. 1. 1.]\n",
-      " [1. 0. 1. 0.]\n",
-      " [1. 1. 0. 1.]\n",
-      " [1. 0. 1. 0.]]\n"
+      "[[0. 1. 1. 1.]\n [1. 0. 1. 0.]\n [1. 1. 0. 1.]\n [1. 0. 1. 0.]]\n"
      ]
     }
    ],
@@ -596,419 +210,20 @@
    },
    "outputs": [
     {
-     "name": "stdout",
      "output_type": "stream",
+     "name": "stdout",
      "text": [
-      "case = [0, 0, 0, 0] cost = 0.0\n",
-      "case = [1, 0, 0, 0] cost = 3.0\n",
-      "case = [0, 1, 0, 0] cost = 2.0\n",
-      "case = [1, 1, 0, 0] cost = 3.0\n",
-      "case = [0, 0, 1, 0] cost = 3.0\n",
-      "case = [1, 0, 1, 0] cost = 4.0\n",
-      "case = [0, 1, 1, 0] cost = 3.0\n",
-      "case = [1, 1, 1, 0] cost = 2.0\n",
-      "case = [0, 0, 0, 1] cost = 2.0\n",
-      "case = [1, 0, 0, 1] cost = 3.0\n",
-      "case = [0, 1, 0, 1] cost = 4.0\n",
-      "case = [1, 1, 0, 1] cost = 3.0\n",
-      "case = [0, 0, 1, 1] cost = 3.0\n",
-      "case = [1, 0, 1, 1] cost = 2.0\n",
-      "case = [0, 1, 1, 1] cost = 3.0\n",
-      "case = [1, 1, 1, 1] cost = 0.0\n",
-      "\n",
-      "Best solution = [1, 0, 1, 0] cost = 4.0\n"
+      "case = [0, 0, 0, 0] cost = 0.0\ncase = [1, 0, 0, 0] cost = 3.0\ncase = [0, 1, 0, 0] cost = 2.0\ncase = [1, 1, 0, 0] cost = 3.0\ncase = [0, 0, 1, 0] cost = 3.0\ncase = [1, 0, 1, 0] cost = 4.0\ncase = [0, 1, 1, 0] cost = 3.0\ncase = [1, 1, 1, 0] cost = 2.0\ncase = [0, 0, 0, 1] cost = 2.0\ncase = [1, 0, 0, 1] cost = 3.0\ncase = [0, 1, 0, 1] cost = 4.0\ncase = [1, 1, 0, 1] cost = 3.0\ncase = [0, 0, 1, 1] cost = 3.0\ncase = [1, 0, 1, 1] cost = 2.0\ncase = [0, 1, 1, 1] cost = 3.0\ncase = [1, 1, 1, 1] cost = 0.0\n\nBest solution = [1, 0, 1, 0] cost = 4.0\n"
      ]
     },
     {
+     "output_type": "display_data",
      "data": {
-      "image/png": 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Z8BRuX7p6vZ6UlBRee+01MjMztY4jfJiqqnz44Yc3Ncot5u/vz9SpU5kzZw55lZwuIbyL25cuQLNmzejevTuvv/661lGED/v111/Jzs6mc+fOLrleu3btaN26Ne+++65Lric8g0eULjgOs/z+++/57bfftI4ifNTSpUsZOHAgOhcubhg/fjz/+c9/OHr0qMuuKdybx5Su0WgsOcyyqKhI6zjCx6SlpbF3714edvLEa2dFREQwatQoZsyYIRs9+QiPKV2A7t27Ex0dLYdZilq3bNky+vXrV2rPXFfo06cPqqryySefuPzawv14VOkWz3H84IMPSE9P1zqO8BEZGRls2bKFfv361cj1dTod06ZN46233iIjI6NG3kO4D48qXYBbbrmFgQMHMmfOHJm7K2rFmjVr6NGjBxGVbOF4M5o0acLDDz/Mq6++WmPvIdyDx5UuOA6zPHPmDFu3btU6ivBy+fn5rF27loEDB9b4e40YMYLdu3ej1X4nonZ4ZOn6+fkxbdo05s2bR25urtZxhBfbuHEjLVu2pGHDhjX+XsHBwUyZMoUZM2ZgtVpr/P2ENjyydAHatGnDvffey8KFC7WOIryU3W6/6SW/VdW5c2duu+02Pvzww1p7T1G7PLZ0AcaOHctnn33GwYMHtY4ivNC2bdswmUy0atWqVt938uTJrFixgpMnT9bq+4ra4dGlGx4ezrPPPitzHEWNWLp0KYMHD65wq8aaEBsby9///ndmzZolD4u9kEeXLjgOswwICGDt2rVaRxFeZM+ePVy6dImuXbtq8v6PP/44ZrOZL774QpP3FzXH40tXURSmTZvGO++8w6VLl7SOI7xEamoqgwYNcumS36rQ6/VMmzaNV155haysLE0yiJrh8aULkJCQwKOPPiqHWQqXOHXqFLt376Z3796a5mjRogVdunThzTff1DSHcC2vKF2AJ598kr1797Jjxw6towgPt3z5cvr27UtQUJDWURg9ejTffPMNe/fu1TqKcBGvKd3AwECmTp3KrFmzKCgo0DqO8FBms5nPPvuMpKQkraMAEBYWxrhx45gxYwY2m03rOMIFvKZ0ATp06EDTpk1ZtGiR1lGEh1q7di1du3albt26Wkcp8eCDDxIeHs7KlSu1jiJcwKtKF2DixIl89NFH/P7771pHER7GarWyevVqBg0apHWU6xRv9LR48WLOnz+vdRxxk7yudKOiohgxYoQcZimqbNOmTTRt2pSEhASto5TSoEEDkpOTmTt3rtZRxE3yutIF6N+/P3l5eXz66adaRxEewm63k5qaWqtLfqtq6NChHD9+nO3bt2sdRdwEryxdnU5HSkoKr7/+OhaLRes4wgN8//33BAYGcvfdd2sdpVz+/v5MmzaNOXPmyEZPHswrSxegadOm/PnPf5bDLIVTtFryW1X33HMPbdq0kcMsPZjXli7AqFGj2LFjB7/88ovWUYQb279/P+np6TzwwANaR3HKuHHj2LhxI0eOHNE6iqgGry7dkJAQJk6cyMyZMyksLNQ6jnBTqampDBgwAIPBoHUUp8hhlp7Nq0sXoFu3bsTGxrJs2TKtowg3lJ6ezo8//kifPn20jlIlffr0QVEU1q9fr3UUUUVeX7rFcxyXLl0qh1mKUlasWEGfPn0IDg7WOkqVFB9muWDBAjnM0sN4fekCxMXFMXjwYNmfVFwnMzOTTz/9lOTkZK2jVEtiYiK9e/eWwyw9jE+ULsDAgQM5d+4cX3/9tdZRhJtYt24d999/P9HR0VpHqbbhw4fzyy+/8NNPP2kdRTjJZ0q3+DDLl156iZycHK3jCI1ZrVZWrlzpdkt+qyooKIjJkycza9YsOczSQ/hM6QLcdddd3HfffSxYsEDrKEJjn3/+OY0bNyYxMVHrKDetc+fONGrUiCVLlmgdRTjBp0oXHIdZfvHFFxw4cEDrKEIjqqqWLIbwFpMmTWLVqlVymKUH8LnSNZlM/OMf/5A5jj5sx44d6PV62rVrp3UUl4mJieHJJ5+Uh8UewOdKF6BXr14EBQWxZs0araMIDRRvbOPuS36rKikpqWQTduG+fLJ0iw+zfPfdd7l48aLWcUQtOnToECdOnKBHjx5aR3E5vV5PSkoKr776KpmZmVrHEeXwydIFuO222+jbty8vvfSS1lFELUpNTSU5ORk/Pz+to9SI5s2b07VrV9544w2to4hy+GzpguMwywMHDvDDDz9oHUXUgvPnz/P999/Tt29fraPUqNGjR7N9+3b27NmjdRRRBp8u3YCAgJLDLPPz87WOI2rYihUr6N27N0ajUesoNSo0NJQJEyYwc+ZMioqKtI4jbuDTpQvQvn17mjdvzuLFi7WOImpQdnY2GzZs4PHHH9c6Sq3o0aMHERERrFixQuso4gY+X7oAEyZMYN26dRw/flzrKKKGfPzxx3To0IHY2Fito9SK4o2elixZwrlz57SOI64hpQtERkaWHGYpc3e9T2FhIStWrPD4Jb9VVb9+fR5//HHmzJmjdRRxDSndq/r160dBQQEbN27UOopwsS1bttCwYUPuuOMOraPUuiFDhpCWlsa2bdu0jiKuktK9qvgwyzfeeAOz2ax1HOEiqqq6/Sm/NUkOs3Q/UrrXuOOOO3jooYfkMEsv8vPPP1NUVET79u21jqKZtm3b0rZtW9555x2towikdEsZOXIkP/74I7t379Y6inCBpUuXMmjQIK9b8ltVY8eO5dNPP+Xw4cNaR/F5Uro3CA4OZtKkSXKYpRc4cuQIR44c4aGHHtI6iuYiIiIYPXq0bPTkBqR0y9C1a1duueUWli5dqnUUcROWLVtGUlIS/v7+WkdxC3/961/R6/V8/PHHWkfxaVK6ZVAUhSlTprBs2TLOnDmjdRxRDRcuXGD79u089thjWkdxG8UPixcuXMjly5e1juOzpHTLERcXx9ChQ5k9e7bsT+qBVq1axV/+8hfCwsK0juJWGjVqxF//+ldeeeUVraP4LCndCgwYMIDz58/z5Zdfah1FVEFubi7r169nwIABWkdxS0899RR79uxh586dWkfxSVK6FTAYDPz73/8mMTFRRrseZP369dxzzz3ExcVpHcUtBQUFMWXKFDnMUiNSupW4/fbbadCggc9POfIUNpvNJ5f8VlWnTp1ITEzk/fff1zqKz5HSdYJOV/5vk6qqMqfXjXz11VfExsbSvHlzraO4vUmTJrF69WrS0tK0juJTpHRv0v79+5k8ebI8mHAD3njKb02Kjo6Wwyw1IKV7k+68805at27N66+/Tl5entZxfNovv/xCTk4OnTp10jqKx0hKSiIzM5PNmzdrHcVnSOlWQ/GoYOfOnfz1r39l9+7dLFmyhKCgII2T+bbiJb8V3Q4S19Pr9Tz//PNymGUtkj+dTrLZbLzwwguYzWaKiopISUnhqaeeon379mzdupUuXbrIP9E0lJeXh5+fHw8//LDWUTxOs2bN6N69O/Pnz9c6ik8waB3AU+j1eqxWK507dyY6Opo6deqwfv16GjVqBDhKWa/Xa5zSdwUGBjJ9+nSvPeW3pj3zzDP079+fPXv20LJlS63jeDUZ6VbBjBkzyM3NJTExkTVr1tCoUSPsdjuqqkrhakxRFCncm2A0Ghk/fjzTp0+XwyxrmJRuFd04S0Gn0113W0F2cBKeqkePHkRHR7N8+XKto3g1paL7kG3btlV37dpVi3E8w7Zt29Dr9XTq1Am73V7y4Gb16tWcPXuWy5cvExkZyZgxYzRO6r3kdk7NOH36NEOHDiU1NZV69eppHcdjKYryX1VV25b1ORnpVkO7du346aefyMzMRKfT8dtvvzF06FD+85//oNPpqFOnDh999BFvvvmm1lG9lhRuzYiPj2fgwIHMmTNHHgzXEHmQVg1BQUEMHDiQsLAwDh48yPz582nRogV9+vQhKioKk8lE06ZNWblyJaqqyhJiF9m5cyeff/4569ev509/+hOPPPIILVq0ICIioqQg5Pf65g0aNIgBAwbwzTff0LVrV63jeB0Z6VZTTEwMAJ988gkhISEkJyfTuHFjTCYTWVlZLFy4kI4dO0oJuNDo0aOJiopi0aJFGAwGZs+ezcSJE/n5559RFEV+r13E39+flJQU5s6dK4dZ1gAp3ZtgNpv58ssveeaZZ4iPjwfgo48+onfv3sTExDB8+HCNE3qP06dPExgYyJAhQ7jrrruYM2cOy5cvJzExkeTkZD755BOtI3qVNm3a8Mwzz2AwyD+GXU0epN2k0aNHc+7cOZ588knefPNNdDodjz/+OMnJyeh0uusetInqKb5F889//hOz2czYsWNp0KBByRSxbdu2sXLlSt544w251+tCcmus+uRBWg2aP38+999/P2vWrKFbt24sWrSI/v37lxStFO7NK/7Gf+qpp7Db7bz88sts2rSJAwcOcPHiRX755RdOnjwphetiFRWu7K5XfTLSdREZFdQOs9nM0qVL2bp1K1FRUZw+fRq73c7cuXNlO8datG/fPsaMGUOvXr0YP3681nHcTkUjXSldFyouXrmlUDOu/YutqKiIPXv2UK9ePQICAoiIiNA4ne+ZOHEi69atY//+/bLZ0w3k9kItURSFwsJCDh06JHMcXcxms6EoCqqqYrfbMRgMtGnThpiYGCncWiK767mGlK6L6XQ6XnzxRbZs2aJ1FK9w8OBB4I/FENcusz527BjLli3TJJevkN31XE9K18X0ej0pKSm88sorZGVlaR3Hoy1cuJC77rqLXr16sWHDBsDx+1t86+bAgQPceeedWkb0etfurtezZ0+OHDnC+vXrmTZtGvDHv0CE86R0a0DLli25//77eeutt7SO4tFWr17Na6+9RnJyMvPmzaNTp04sWLAAcByzfvjwYdq0aaNxSu8nu+u5lpRuDXn22Wf5+uuv2bdvn9ZRPJKqqsyePZsOHTowaNAgPvvsM8aNG8enn35Kx44dady4MRcvXtQ6ps+Q3fVcR2Yv1KBNmzaRmprK0qVLZUTgQuvWreOJJ54gPT0do9GodRyfUd7uemvWrCE9PZ3Lly8TFRXFP/7xD42Tak9mL2ikZ8+ehIeHs2rVKq2jeJWjR48yaNAgKdxaVt7uehs2bCjZXW/t2rWyu14lZGF1DVIUhalTp/L3v/+d7t27Ex0drXUkj3Tj3rlPP/20hml8l+yu5xoy0q1hDRo0ICkpiXnz5mkdxWMtX74ci8VS8mOTyYTJZNIwke+S3fVunpRuLXjiiSc4evQo3377rdZRPM7p06dZsmSJnH/mRmR3vZsjpVsL/P39mTp1KnPmzCEvL0/rOB5l+fLl9O3bl+DgYK2jiKvCw8Np0qQJKSkpbNq0iYcffpglS5YwYsSIkil9MpuhfFK6taRdu3a0bt2ad999V+soHsNisfDZZ5+RlJSkdRRxA9ldr/rkQVotGj9+PElJSfzlL3+hcePGWsdxex999BF/+tOfiIyM1DqKuIFOp2PcuHHywKwa5K+jWhQREcGoUaOYMWOG/POrElarlVWrVjFw4ECto4gKFG9CVEz2YaiclG4t69OnD6qqyvEyldi8eTO33347jRo10jqKqERx8RYUFHDs2DGt47g9Kd1aptPpSElJ4a233iIjI0PrOG7JbreTmprKoEGDtI4inKQoCocPH2bMmDFymGUlpHQ1kJiYSK9evXj11Ve1juKWduzYgZ+fH/fcc4/WUUQVtGjRgvvuu69kBoMom5SuRoYPH87u3buRvS1KW7p0KYMHD5YHNB5ozJgxfP755yX7IIvSpHQ1EhwczJQpU5gxYwZWq1XrOG7jwIEDnDp1ih49emgdRVRDeHg4zz77rDwsroCUroY6d+7MbbfdxgcffKB1FLeRmprK448/jsEgsxk9Ve/evQkMDGTt2rVaR3FLUroamzJlCitXruTkyZNaR9Hc2bNn2bFjB48++qjWUcRNKN7o6Z133pE9j8sgpauxmJgYhg0bxqxZs3x+juOKFSt45JFHCAkJ0TqKuEkJCQn07duXl19+WesobkdK1w0kJydjNpv54osvtI6imaysLDZu3EhycrLWUYSLDBs2jP379/PDDz9oHcWtSOm6ATnMEj7++GM6depUsnWg8HyBgYE899xzzJ49m4KCAq3juA0pXTfRvHlzunTp4pO77hcWFrJy5UpZDOGFOnToQLNmzVi0aJHWUdyGlK4befbZZ/nmm2/Yu3ev1lFq1RdffMGtt95KkyZNtI4iasCECRNYt24dv//+u9ZR3IKUrhsJDQ1l3LhxzJgxA5vNpnWcWqGqasliCOGdoqKiGDFiBDNnzvT5h8Ugpet2HnzwQcLDw1m5cqXWUWrFzp07UVWV++67T+soogb169eP/Px8Nm7cqHUUzUnpupniOY6LFy/m3LlzWsepcbLk1zfodDqmTZvG/PnzMZvNWsfRlJSuG2rQoAHJyclef5jl4cOHOX78OA8++KDWUUQtaNq0KX/+8595/fXXtY6iKSldNzV06FCOHz/O9u3btY5SY1JTU0lOTpZDJ33IqFGj+PHHH/nll1+0jqIZKV035e/vT0pKCnPmzPHK/UkvXLjAd999R9++fbWOImpRSEgIEydOZObMmRQWFmodRxNSum6sbdu2tGnTxisPs1y5ciW9evUiNDRU6yiilnXr1o169eqRmpqqdRRNSOm6uXHjxrFx40aOHDmidRSXycnJYf369bLk10cpisJzzz1HamoqZ86c0TpOrZPSdXMRERE888wzXrU/6fr167nvvvuIi4vTOorQSFxcHIMHD2b27Nk+N3dXStcDPPLIIyiKwvr167WOctOKiopYvny5LPkVDBo0iHPnzvHVV19pHaVWSel6gOLDLBcsWODxh1l++eWXxMfH06xZM62jCI0ZDAZSUlJ4+eWXycnJ0TpOrZHS9RCNGzemd+/evPLKK1pHqTZZ8itu1Lp1a9q3b89bb72ldZRaI6XrQYYPH86vv/7KTz/9pHWUatm1axdWq5UOHTpoHUW4kTFjxrBlyxb279+vdZRaIaXrQYKCgpgyZQqzZs3yyMMsly5dyqBBg9Dp5I+d+IPJZGLMmDHMnDnTax4WV0T+9HuY+++/n0aNGrFkyRKto1TJsWPHOHToED179tQ6inBDDz/8MEFBQaxZs0brKDVOStcDTZ48mVWrVnnUYZbLli0jKSkJf39/raMIN6QoCikpKbz77rtcuHBB6zg1SkrXA0VHR/Pkk096zGGWly5d4ptvvuGxxx7TOopwY7feeiuPPfaY1x9mKaXroZKSkjCbzXz22WdaR6nUqlWreOihhzCZTFpHEW5u2LBhHDx40KsPs5TS9VB6vZ7nn3+eV199lczMTK3jlCs3N5d169YxYMAAraMIDxAQEMBzzz3HrFmzyM/P1zpOjZDS9WB33nkn3bp144033tA6Srk2bNhA27ZtiY+P1zqK8BDt27enefPmXnuYpZSuhxs9ejTffvste/bs0TpKKTabTZb8imqZMGECH3/8McePH9c6istJ6Xo4o9HI+PHjmTFjBkVFRVrHuc7WrVuJioqiRYsWWkcRHiYyMpIRI0Z41UZPxaR0vUCPHj2IjIxkxYoVWkcpoaoqH374oSz5FdXWr18/rFar1x1mKaXrBYr3J12yZInbHGb566+/kp2dTefOnbWOIjxU8UZPb7zxhlcdZiml6yXq16/PgAEDmDNnjtZRAMeS34EDB8qSX3FT7rjjDh566CFee+01raO4jHxHeJHBgwdz8uRJtm3bpmmOtLQ09u7dy8MPP6xpDuEdRo4cyc6dO9m9e7fWUVxCSteL+Pv7M23aNM0Ps1y2bBn9+vUjMDBQswzCewQHBzNp0iSvOcxSStfL3H333dxzzz28/fbbmrx/RkYGW7ZsoV+/fpq8v/BOXbt2JT4+nqVLl2od5aZJ6XqhsWPHsnnzZg4fPlzr771mzRp69OhBRERErb+38F6KojB58mSWLVvG6dOntY5zU6R0vVCdOnUYPXp0rc9xzM/PZ+3atQwcOLDW3lP4jri4OIYOHerxh1lK6Xqp3r17o9fr+fjjj2vtPTdu3EjLli1p2LBhrb2n8C0DBgzgwoULfPnll1pHqTYpXS+l0+l4/vnnWbhwIZcvX67x97Pb7SxbtkwWQ4gade1hltnZ2VrHqRYpXS+WkJDAI488UiuHWW7btg2TyUSrVq1q/L2Eb2vVqhUdO3b02MMspXS93FNPPcWePXvYuXNnjb5P8Sm/iqLU6PsIAY7DLL/66iuPPMxSStfLBQYGluxPWlOHWe7Zs4dLly7RtWvXGrm+EDcKCwtj7NixTJ8+HZvNpnWcKpHS9QEdO3YkMTGR999/v0aun5qaKqf8ilrXs2dPQkNDWb16tdZRqkS+S3zEpEmTWLNmDWlpaS697qlTp9i9eze9e/d26XWFqIyiKEybNo333nvPow6zlNL1EcWHWc6cOdOlcxyXL19O3759CQoKctk1hXBWw4YN6d+/P/PmzdM6itOkdH1IUlIS2dnZbN682SXXKz4YMykpySXXE6I6hg0bxuHDh/nuu++0juIUKV0fUrw/6WuvveaSwyzXrl1Lt27dqFu3rgvSCVE912705AmHWUrp+phmzZrxwAMPMH/+/Ju6jtVqZfXq1bLkV7iFe++9lxYtWvDuu+9qHaVSUro+6JlnnuG77767qcMsP/30U5o1a0ZCQoILkwlRfRMmTOCTTz7h2LFjWkepkJSuDzIajUyYMIHp06dX6zBLu91OamqqLPkVbqVu3bqMHDnS7Q+zlNL1Ud27dycmJobly5dX+bXfffcdwcHBtGnTpgaSCVF9ffv2xWazsWHDBq2jlMugdQChjeLDLIcMGUL37t2Ji4ujyG5nR2Ymv2RnszMzk3SrFbuqEmYwcJfRSBujkc7h4SWjXFnyK9xN8cPi0aNH06VLF+rUqaN1pFKUiuZstm3bVt21a1ctxhG1bfHixezev5+m48ax+Nw5smw2Cu12/HU6/BQFBbCpKvl2O3qdDmtBAfqff2bz009zu9GodXwhyvTKK69gNpt58cUXNXl/RVH+q6pq27I+J7cXfFzTPn1YcffdTD9yBKvdTpheT10/P0L1egJ1OgJ0OoL1eiL8/DDp9eRfuUJ2q1b03rePhWfOUOTG986E73r66afZtWsX7jholNL1YcvPn2fI4cOExsWRc/48fpV8fWFhIXk5OcSHhRGkKMw9dYq/HzpEnodtOCK8X3BwMJMnT2bmzJk1ttFTdUnp+qjV58/z///+O8E6HZEhIRhDQrh48WKFr8nIyCA8PBydToefTke4Xs8PFgsjDh2iUEa8ws106dKFhg0but1hllK6Puhwbi7/34kThFwtT3DszWDJzCS/oKDM19jsdiwWy3UHTiqKQrhez/eZmbx15kytZBfCWcWHWS5fvpxTp05pHaeElK6PKbLbGXvkCAD+12zFqNfriY6O5uzZs2W+7sqVKxhDQzEYrp/woigKYXo9b6SncyAnp+aCC1EN9erVc7vDLKV0fczXZjNH8/MJLWPv23CTCZ2icOXKlet+XlVVrmRkULecY9UNioJdVXnJjUYTQhQbMGAAly5dYsuWLVpHAWSers95Jz0dRVXLnWMbW68eJ44eJWf1avIPHsSWkwPh4Ri6dycgMbHc65r0er4xmzlbUEC9gICaii9ElRUfZjllyhTat29PaGiopnlkpOtDLlqt/JqdTaheX+7XBPj7Ex4WRp6fHw1SUmiycCF06ULBqlVYK3jQplMUVODLG0bJQriDli1b0rlzZ7c4zFJK14ccyM3FoCiVriSLvuUWlC5dKAwOJicvD79mzQiMiSH/xIkKX6cDdrpgy0ghasKzzz7L119/zb59+zTNIaXrQw7k5mJ14mGCoijExsZy9tw5Ll+6RLjBgPXcOQJuuaXC1wXqdPyWne2quEK4lLscZiml60OuFBY6/QTXGBKCv78/mWYzFxctwr9tW/KNRrJzcsjPz8daWFhqJye9opApCyWEG+vZsycmk4lVq1ZplkEepIlyxcfFUbh6NUV6PUF9+pCdlYXNZrvuw66q6PV69Ho9isGAXq/nxRdfxGQylfsRHh6Ov7+/1r884YMURWHq1KkMGzaMBx54gJiYmFrPIKXrQ2L9/Z3eGUxVVc4tXoyhoIDbnnsOXTklqaoqNrsdm81GbmEhJlWldevWWCwWzGYzp0+fxmKxkJmZidlsxmKxYLFY0Ov1JQVcUUEXf01YWBhhYWFef8y7WsHMEuEaDRs2JCkpiXnz5jF37lzHT2ZkwLffwn//C7/+CllZoNfDLbfAvfdCmzbQrh244M+flK4PaRoSgr+T39DnlizBeuYMDaZOLbdwwTFyMOj1GPR6cnU6ekRF8cif/lThtVVVJS8vr6SAiz/MZjOZmZmcO3eOQ4cOXVfSmZmZZGdnYzQaS8o4LCzMqdIOCgpy2yLLyMjg0Ucf5cSJE0ybNo2RI0eWKl6r1crRo0cBaNCgAUbZ3e2mPfHEEyQnJ/PzsmXcs3MnfPYZqCrYbBAY+Ee5njzpKGODASIiYPhwGDQIbmJapJSuD2kaHIwNsKsqugpKqPDSJcxbt6IYDBz5xz9Kfj72iScwdexY7usU4N6wsEpzKIpCcHAwwcHB1KtXz+n8drudzMzMUmVd/HHkyJGSgi4ucYvFgt1uJywszOmRdfHHjavvaoLBYGDGjBksWrSI48ePl/w69ddM6/vkk0+YP38+Fy5coHPnzsydOxeTyVTj2byZv6LwckICfk8/jT06Gp3J5BjZ3igw8I//zsqCGTMgNRVeew1atqzWe8t+uj5m5KFDfHnlCuEuLpSiq3vu/nT33YTVQllVRUFBQblFfeNI+9rSDgwMvK6knRlZG43Gao2qx44dS3x8PJMnT8Zms6HT6VAUhT179vDqq6/Sv39/evbsyciRI2nUqBGTJ0/Gbrd7/e2WGpGTA08+CT//zNnsbHQBAcRERzv/+uJpkXPmQJ8+ZX5JRfvputd3h6hxw+rV48srV1x+7zDTZuNvUVFuV7gAAQEBREdHE12Fbyy73U5OTk65BX3y5MkyS7ugoKDk/vONI+uwsDAGDRpU5kPEixcv0rp1a8DxL4Hi/3+2b99OfHw8rVq1AhxbFuZc3eNCCrcaCgpg2DDYtQvCw4kKDeX48eOYTCYCnb1lEBYGVitMnOi47dCrV5UiuN93iKhR94SG0iU8nG1ms8tGu/l2O0E6HePr13fJ9dyBTqcjNDSU0NBQ4uPjnX5dYWEhWVlZ192PvvZDX85qwMzMTKKiokr9fHp6OnXq1CE8PBwAPz8/QkJCZJRbXa+8UlK4KAoGg4Go6GjOnT1Lw1tvxelhiL+/4x7w5MmO2wwNGjgdQUrXxyiKwsyEBLr/9hu5NhvBFSwJdoZNVcmz25nXqBExMg0MPz8/IiIirtsCszJFRUVkZWWV3KdVVRVVVdHpdFitVoxGY8n86itXrtCoUaNS18jNzWXq1KlO3Q5x5weLNWrPHli0CEJD4Zpff3h4OGazGbPZTJ2rf7k5JSAA8vJg/HhYs8bpmQ1Suj4o2t+f926/nSEHDtxU8dpUFYvNxpCYGPpERro4pe8wGAyoqkrLqw9mrh0NJyYmcuzYsZKHevv27aNfv36lRrkGg4H+/ftfNz3v2LFjZY62bTZbqdsflT1oDAsLw8+vsrNF3NysWY7R6Y3bkwL1YmM5efIkoUZj1R6gmkzw22/www/QqZNTL5HS9VHtwsL4sGlThh08iLmoCJNeX6XRT47NhlVVGV6vHs81aOCbIycXSElJYcWKFaSlpdGhQwdeeOEFwsLCUBSFbt260adPH4YMGcK2bdvQ6/UYDAZatGhR6jr+/v7cf//9Tr2n1Wot9dCw+McZGRmcOHGi1O2RzMxMAgICSuZMXzuHuqKRdUhIiHvcBjlxAn76yXE/tgyBgYGYTCbOX7jAQlXlp9xc8ux2Ig0GhtStS5/yRsDFf+7fe8/p0pXZCz7uXEEBk48dY0dmJgoQqteXO51MvTpDIV9ViTAYeKlxYzpX5Z9johSbzUZGRgYWi4X09HTi4uLIzc0lKyuL9u3bo9Pp+PDDD1m0aBEFBQUsWLCAu+66q9Zzqqp63YPF8kr7xoUweXl5ZZZ0eQ8ai38c4OrtQd9+G2bPdsy1LYfNbuf48eMU1K1LE5MJf52OEwUFjEhL47X69WkaFFT2C+12x4yGX35x3Lqg4tkLUroCVVXZbrHwbno6O7Oy0AMFdjv6a45g1+t06IC6fn6MqFePx6KiCHXDmQrCvRQVFZWU8LVzrCsqbYvFgk6nu241orOlXe6o+oknHLcAKplHnpWVxfkLF0hISECnKKQVFDDi5EkmxcTQo6LXZmfD0qVwzz2ATBkTlVAUhS7h4XQJD+dcQQF7c3L4X04OJ/PzsQF1DAZaGY3cERzMHcHBFS6sEOJaBoOhyg8WVVUlPz+/zNWKFouF8+fPc+jQoVKlnZ2dTUhISJlLyIdu3YoB0GVmluwVUvyh0+lKZi2EhoZitlj41/HjfF1YSIGqcntgIB0rWwVYWAiHDpWUboW/J07/TgifEBsQQGxAAN2r8E0ihCspikJQUBBBQUHExsY6/Tq73U5WVlaZDw8N+fnk6/XYrNY/NmwqKsJmt6Nes2mT/uqzjeT8fP55xx3sKyjgv7m5lS+ft9nAYnEqp5SuEMIrFN+SKHOJ9L//DUFBZS71tatqqd3z6tSpg59eT+vgYDZZLKy9coXkigYiVTj0UkpXCOH9IiL+2DnsBjpFQWcw4FfOMwobcNpqrfj6/v5QxuKWsrjBXA4hhKhhrVpBfn6lX5ZRVMQXmZnk2u3YVZUd2dl8brHQLiSk4hfq9XDHHU5FkZGuEML7degAX3xR6ZcpwNorV5hx9ix2oJ6fHxNjYuhc0QnCNptj2liTJk5FkdIVQni/hx5y3Ne12crewvGqOgYD7zRsWLVrZ2Y6dhu7dhvICsjtBSGE94uKggcf/GNbRlex2x2r0oYOdfolUrpCCN8wZQr4+Tm2ZXSVzEzo2bNKG5pL6QohfEODBvD8845NzG84ybpacnIcy35ffLFKL5PSFUL4joEDoW9fMJtvrnhzchy3FRYvrnA/h7JI6QohfIdO59j45vHHHSvI8vKq9npVhStXHLcpUlOhGpsPVbjhjaIoF4G0Kl9VCCF8W0NVVctcLVFh6QohhHAtub0ghBC1SEpXCCFqkZSuEELUIildIYSoRVK6QghRi/4fzdEQ2xcQWRMAAAAASUVORK5CYII=\n"
      },
-     "metadata": {},
-     "output_type": "display_data"
+     "metadata": {}
     }
    ],
    "source": [
@@ -1051,31 +266,17 @@
    },
    "outputs": [
     {
-     "name": "stdout",
      "output_type": "stream",
+     "name": "stdout",
      "text": [
-      "// This file has been generated by DOcplex\n",
-      "// model name is: Max-cut\n",
-      "// single vars section\n",
-      "dvar bool x_0;\n",
-      "dvar bool x_1;\n",
-      "dvar bool x_2;\n",
-      "dvar bool x_3;\n",
-      "\n",
-      "maximize\n",
-      " 3 x_0 + 2 x_1 + 3 x_2 + 2 x_3 [ - 2 x_0*x_1 - 2 x_0*x_2 - 2 x_0*x_3\n",
-      " - 2 x_1*x_2 - 2 x_2*x_3 ];\n",
-      " \n",
-      "subject to {\n",
-      "\n",
-      "}\n"
+      "\\ This file has been generated by DOcplex\n\\ ENCODING=ISO-8859-1\n\\Problem name: Max-cut\n\nMaximize\n obj: 3 x_0 + 2 x_1 + 3 x_2 + 2 x_3 + [ - 4 x_0*x_1 - 4 x_0*x_2 - 4 x_0*x_3\n      - 4 x_1*x_2 - 4 x_2*x_3 ]/2\nSubject To\n\nBounds\n 0 <= x_0 <= 1\n 0 <= x_1 <= 1\n 0 <= x_2 <= 1\n 0 <= x_3 <= 1\n\nBinaries\n x_0 x_1 x_2 x_3\nEnd\n\n"
      ]
     }
    ],
    "source": [
     "max_cut = Maxcut(w)\n",
     "qp = max_cut.to_quadratic_program()\n",
-    "qp.to_docplex().prettyprint()"
+    "print(qp.export_as_lp_string())"
    ]
   },
   {
@@ -1084,16 +285,10 @@
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
      "output_type": "stream",
+     "name": "stdout",
      "text": [
-      "Offset: -2.5\n",
-      "Ising Hamiltonian:\n",
-      "0.5 * ZZII\n",
-      "+ 0.5 * IZZI\n",
-      "+ 0.5 * ZIIZ\n",
-      "+ 0.5 * IZIZ\n",
-      "+ 0.5 * IIZZ\n"
+      "Offset: -2.5\nIsing Hamiltonian:\n0.5 * ZZII\n+ 0.5 * IZZI\n+ 0.5 * ZIIZ\n+ 0.5 * IZIZ\n+ 0.5 * IIZZ\n"
      ]
     }
    ],
@@ -1112,12 +307,10 @@
    },
    "outputs": [
     {
-     "name": "stdout",
      "output_type": "stream",
+     "name": "stdout",
      "text": [
-      "optimal function value: 4.0\n",
-      "optimal value: [1. 0. 1. 0.]\n",
-      "status: SUCCESS\n"
+      "optimal function value: 4.0\noptimal value: [1. 0. 1. 0.]\nstatus: SUCCESS\n"
      ]
     }
    ],
@@ -1150,405 +343,20 @@
    },
    "outputs": [
     {
-     "name": "stdout",
      "output_type": "stream",
+     "name": "stdout",
      "text": [
-      "energy: -1.5\n",
-      "max-cut objective: -4.0\n",
-      "solution: [1 0 1 0]\n",
-      "solution objective: 4.0\n"
+      "energy: -1.5\nmax-cut objective: -4.0\nsolution: [1 0 1 0]\nsolution objective: 4.0\n"
      ]
     },
     {
+     "output_type": "display_data",
      "data": {
-      "image/png": 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\n"
      },
-     "metadata": {},
-     "output_type": "display_data"
+     "metadata": {}
     }
    ],
    "source": [
@@ -1597,406 +405,20 @@
    },
    "outputs": [
     {
-     "name": "stdout",
      "output_type": "stream",
+     "name": "stdout",
      "text": [
-      "energy: -1.4999796718931908\n",
-      "time: 3.5326640605926514\n",
-      "max-cut objective: -3.9999796718931906\n",
-      "solution: [0. 1. 0. 1.]\n",
-      "solution objective: 4.0\n"
+      "energy: -1.4997993934173706\ntime: 2.370492458343506\nmax-cut objective: -3.9997993934173706\nsolution: [0. 1. 0. 1.]\nsolution objective: 4.0\n"
      ]
     },
     {
+     "output_type": "display_data",
      "data": {
-      "image/png": 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\n"
      },
-     "metadata": {},
-     "output_type": "display_data"
+     "metadata": {}
     }
    ],
    "source": [
@@ -2029,404 +451,20 @@
    },
    "outputs": [
     {
-     "name": "stdout",
      "output_type": "stream",
+     "name": "stdout",
      "text": [
-      "optimal function value: 4.0\n",
-      "optimal value: [1. 0. 1. 0.]\n",
-      "status: SUCCESS\n"
+      "optimal function value: 4.0\noptimal value: [1. 0. 1. 0.]\nstatus: SUCCESS\n"
      ]
     },
     {
+     "output_type": "display_data",
      "data": {
-      "image/png": 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\n"
      },
-     "metadata": {},
-     "output_type": "display_data"
+     "metadata": {}
     }
    ],
    "source": [
@@ -2483,463 +521,20 @@
    },
    "outputs": [
     {
-     "name": "stdout",
      "output_type": "stream",
+     "name": "stdout",
      "text": [
-      "distance\n",
-      " [[ 0. 48. 91.]\n",
-      " [48.  0. 63.]\n",
-      " [91. 63.  0.]]\n"
+      "distance\n [[ 0. 48. 91.]\n [48.  0. 63.]\n [91. 63.  0.]]\n"
      ]
     },
     {
+     "output_type": "display_data",
      "data": {
-      "image/png": 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\n"
      },
-     "metadata": {},
-     "output_type": "display_data"
+     "metadata": {}
     }
    ],
    "source": [
@@ -2970,480 +565,20 @@
    },
    "outputs": [
     {
-     "name": "stdout",
      "output_type": "stream",
+     "name": "stdout",
      "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 = 202.0\nBest order from brute force = (0, 1, 2) with total distance = 202.0\n"
      ]
     },
     {
+     "output_type": "display_data",
      "data": {
-      "image/png": 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\n"
      },
-     "metadata": {},
-     "output_type": "display_data"
+     "metadata": {}
     }
    ],
    "source": [
@@ -3497,50 +632,16 @@
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
      "output_type": "stream",
+     "name": "stdout",
      "text": [
-      "// This file has been generated by DOcplex\n",
-      "// model name is: TSP\n",
-      "// single vars section\n",
-      "dvar bool x_0_0;\n",
-      "dvar bool x_0_1;\n",
-      "dvar bool x_0_2;\n",
-      "dvar bool x_1_0;\n",
-      "dvar bool x_1_1;\n",
-      "dvar bool x_1_2;\n",
-      "dvar bool x_2_0;\n",
-      "dvar bool x_2_1;\n",
-      "dvar bool x_2_2;\n",
-      "\n",
-      "minimize\n",
-      " [ 48 x_0_0*x_1_1 + 48 x_0_0*x_1_2 + 91 x_0_0*x_2_1 + 91 x_0_0*x_2_2\n",
-      " + 48 x_0_1*x_1_0 + 48 x_0_1*x_1_2 + 91 x_0_1*x_2_0 + 91 x_0_1*x_2_2\n",
-      " + 48 x_0_2*x_1_0 + 48 x_0_2*x_1_1 + 91 x_0_2*x_2_0 + 91 x_0_2*x_2_1\n",
-      " + 63 x_1_0*x_2_1 + 63 x_1_0*x_2_2 + 63 x_1_1*x_2_0 + 63 x_1_1*x_2_2\n",
-      " + 63 x_1_2*x_2_0 + 63 x_1_2*x_2_1 ];\n",
-      " \n",
-      "subject to {\n",
-      " c0:\n",
-      "  x_0_0 + x_0_1 + x_0_2 == 1;\n",
-      " c1:\n",
-      "  x_1_0 + x_1_1 + x_1_2 == 1;\n",
-      " c2:\n",
-      "  x_2_0 + x_2_1 + x_2_2 == 1;\n",
-      " c3:\n",
-      "  x_0_0 + x_1_0 + x_2_0 == 1;\n",
-      " c4:\n",
-      "  x_0_1 + x_1_1 + x_2_1 == 1;\n",
-      " c5:\n",
-      "  x_0_2 + x_1_2 + x_2_2 == 1;\n",
-      "\n",
-      "}\n"
+      "\\ This file has been generated by DOcplex\n\\ ENCODING=ISO-8859-1\n\\Problem name: TSP\n\nMinimize\n obj: [ 96 x_0_0*x_1_1 + 96 x_0_0*x_1_2 + 182 x_0_0*x_2_1 + 182 x_0_0*x_2_2\n      + 96 x_0_1*x_1_0 + 96 x_0_1*x_1_2 + 182 x_0_1*x_2_0 + 182 x_0_1*x_2_2\n      + 96 x_0_2*x_1_0 + 96 x_0_2*x_1_1 + 182 x_0_2*x_2_0 + 182 x_0_2*x_2_1\n      + 126 x_1_0*x_2_1 + 126 x_1_0*x_2_2 + 126 x_1_1*x_2_0 + 126 x_1_1*x_2_2\n      + 126 x_1_2*x_2_0 + 126 x_1_2*x_2_1 ]/2\nSubject To\n c0: x_0_0 + x_0_1 + x_0_2 = 1\n c1: x_1_0 + x_1_1 + x_1_2 = 1\n c2: x_2_0 + x_2_1 + x_2_2 = 1\n c3: x_0_0 + x_1_0 + x_2_0 = 1\n c4: x_0_1 + x_1_1 + x_2_1 = 1\n c5: x_0_2 + x_1_2 + x_2_2 = 1\n\nBounds\n 0 <= x_0_0 <= 1\n 0 <= x_0_1 <= 1\n 0 <= x_0_2 <= 1\n 0 <= x_1_0 <= 1\n 0 <= x_1_1 <= 1\n 0 <= x_1_2 <= 1\n 0 <= x_2_0 <= 1\n 0 <= x_2_1 <= 1\n 0 <= x_2_2 <= 1\n\nBinaries\n x_0_0 x_0_1 x_0_2 x_1_0 x_1_1 x_1_2 x_2_0 x_2_1 x_2_2\nEnd\n\n"
      ]
     }
    ],
    "source": [
     "qp = tsp.to_quadratic_program()\n",
-    "qp.to_docplex().prettyprint()"
+    "print(qp.export_as_lp_string())"
    ]
   },
   {
@@ -3552,56 +653,10 @@
    },
    "outputs": [
     {
-     "name": "stdout",
      "output_type": "stream",
+     "name": "stdout",
      "text": [
-      "Offset: 7581.0\n",
-      "Ising Hamiltonian:\n",
-      "-1290.0 * ZIIIIIIII\n",
-      "- 1290.0 * IZIIIIIII\n",
-      "+ 606.5 * ZZIIIIIII\n",
-      "- 1290.0 * IIZIIIIII\n",
-      "+ 606.5 * ZIZIIIIII\n",
-      "+ 606.5 * IZZIIIIII\n",
-      "- 1268.5 * IIIZIIIII\n",
-      "+ 606.5 * ZIIZIIIII\n",
-      "+ 15.75 * IZIZIIIII\n",
-      "+ 15.75 * IIZZIIIII\n",
-      "- 1268.5 * IIIIZIIII\n",
-      "+ 15.75 * ZIIIZIIII\n",
-      "+ 606.5 * IZIIZIIII\n",
-      "+ 15.75 * IIZIZIIII\n",
-      "+ 606.5 * IIIZZIIII\n",
-      "- 1268.5 * IIIIIZIII\n",
-      "+ 15.75 * ZIIIIZIII\n",
-      "+ 15.75 * IZIIIZIII\n",
-      "+ 606.5 * IIZIIZIII\n",
-      "+ 606.5 * IIIZIZIII\n",
-      "+ 606.5 * IIIIZZIII\n",
-      "- 1282.5 * IIIIIIZII\n",
-      "+ 606.5 * ZIIIIIZII\n",
-      "+ 22.75 * IZIIIIZII\n",
-      "+ 22.75 * IIZIIIZII\n",
-      "+ 606.5 * IIIZIIZII\n",
-      "+ 12.0 * IIIIZIZII\n",
-      "+ 12.0 * IIIIIZZII\n",
-      "- 1282.5 * IIIIIIIZI\n",
-      "+ 22.75 * ZIIIIIIZI\n",
-      "+ 606.5 * IZIIIIIZI\n",
-      "+ 22.75 * IIZIIIIZI\n",
-      "+ 12.0 * IIIZIIIZI\n",
-      "+ 606.5 * IIIIZIIZI\n",
-      "+ 12.0 * IIIIIZIZI\n",
-      "+ 606.5 * IIIIIIZZI\n",
-      "- 1282.5 * IIIIIIIIZ\n",
-      "+ 22.75 * ZIIIIIIIZ\n",
-      "+ 22.75 * IZIIIIIIZ\n",
-      "+ 606.5 * IIZIIIIIZ\n",
-      "+ 12.0 * IIIZIIIIZ\n",
-      "+ 12.0 * IIIIZIIIZ\n",
-      "+ 606.5 * IIIIIZIIZ\n",
-      "+ 606.5 * IIIIIIZIZ\n",
-      "+ 606.5 * IIIIIIIZZ\n"
+      "Offset: 7581.0\nIsing Hamiltonian:\n-1290.0 * ZIIIIIIII\n- 1290.0 * IZIIIIIII\n+ 606.5 * ZZIIIIIII\n- 1290.0 * IIZIIIIII\n+ 606.5 * ZIZIIIIII\n+ 606.5 * IZZIIIIII\n- 1268.5 * IIIZIIIII\n+ 606.5 * ZIIZIIIII\n+ 15.75 * IZIZIIIII\n+ 15.75 * IIZZIIIII\n- 1268.5 * IIIIZIIII\n+ 15.75 * ZIIIZIIII\n+ 606.5 * IZIIZIIII\n+ 15.75 * IIZIZIIII\n+ 606.5 * IIIZZIIII\n- 1268.5 * IIIIIZIII\n+ 15.75 * ZIIIIZIII\n+ 15.75 * IZIIIZIII\n+ 606.5 * IIZIIZIII\n+ 606.5 * IIIZIZIII\n+ 606.5 * IIIIZZIII\n- 1282.5 * IIIIIIZII\n+ 606.5 * ZIIIIIZII\n+ 22.75 * IZIIIIZII\n+ 22.75 * IIZIIIZII\n+ 606.5 * IIIZIIZII\n+ 12.0 * IIIIZIZII\n+ 12.0 * IIIIIZZII\n- 1282.5 * IIIIIIIZI\n+ 22.75 * ZIIIIIIZI\n+ 606.5 * IZIIIIIZI\n+ 22.75 * IIZIIIIZI\n+ 12.0 * IIIZIIIZI\n+ 606.5 * IIIIZIIZI\n+ 12.0 * IIIIIZIZI\n+ 606.5 * IIIIIIZZI\n- 1282.5 * IIIIIIIIZ\n+ 22.75 * ZIIIIIIIZ\n+ 22.75 * IZIIIIIIZ\n+ 606.5 * IIZIIIIIZ\n+ 12.0 * IIIZIIIIZ\n+ 12.0 * IIIIZIIIZ\n+ 606.5 * IIIIIZIIZ\n+ 606.5 * IIIIIIZIZ\n+ 606.5 * IIIIIIIZZ\n"
      ]
     }
    ],
@@ -3624,12 +679,10 @@
    },
    "outputs": [
     {
-     "name": "stdout",
      "output_type": "stream",
+     "name": "stdout",
      "text": [
-      "optimal function value: 202.0\n",
-      "optimal value: [1. 0. 0. 0. 1. 0. 0. 0. 1.]\n",
-      "status: SUCCESS\n"
+      "optimal function value: 202.0\noptimal value: [1. 0. 0. 0. 1. 0. 0. 0. 1.]\nstatus: SUCCESS\n"
      ]
     }
    ],
@@ -3653,483 +706,20 @@
    },
    "outputs": [
     {
-     "name": "stdout",
      "output_type": "stream",
+     "name": "stdout",
      "text": [
-      "energy: -7379.0\n",
-      "tsp objective: 202.0\n",
-      "feasible: True\n",
-      "solution: [0, 1, 2]\n",
-      "solution objective: 202.0\n"
+      "energy: -7379.0\ntsp objective: 202.0\nfeasible: True\nsolution: [0, 1, 2]\nsolution objective: 202.0\n"
      ]
     },
     {
+     "output_type": "display_data",
      "data": {
-      "image/png": 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\n"
      },
-     "metadata": {},
-     "output_type": "display_data"
+     "metadata": {}
     }
    ],
    "source": [
@@ -4175,483 +765,20 @@
    },
    "outputs": [
     {
-     "name": "stdout",
      "output_type": "stream",
+     "name": "stdout",
      "text": [
-      "energy: -7204.6830268662625\n",
-      "time: 8.034800052642822\n",
-      "feasible: True\n",
-      "solution: [1, 2, 0]\n",
-      "solution objective: 202.0\n"
+      "energy: -7341.272944851356\ntime: 4.881815195083618\nfeasible: True\nsolution: [2, 1, 0]\nsolution objective: 202.0\n"
      ]
     },
     {
+     "output_type": "display_data",
      "data": {
-      "image/png": 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\n"
      },
-     "metadata": {},
-     "output_type": "display_data"
+     "metadata": {}
     }
    ],
    "source": [
@@ -4691,483 +818,20 @@
    },
    "outputs": [
     {
-     "name": "stdout",
      "output_type": "stream",
+     "name": "stdout",
      "text": [
-      "optimal function value: 202.0\n",
-      "optimal value: [0. 0. 1. 0. 1. 0. 1. 0. 0.]\n",
-      "status: SUCCESS\n",
-      "solution: [1, 2, 0]\n",
-      "solution objective: 202.0\n"
+      "optimal function value: 202.0\noptimal value: [0. 0. 1. 0. 1. 0. 1. 0. 0.]\nstatus: SUCCESS\nsolution: [2, 1, 0]\nsolution objective: 202.0\n"
      ]
     },
     {
+     "output_type": "display_data",
      "data": {
-      "image/png": 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\n"
      },
-     "metadata": {},
-     "output_type": "display_data"
+     "metadata": {}
     }
    ],
    "source": [
@@ -5197,37 +861,20 @@
    },
    "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"
-     ]
-    },
-    {
+     "output_type": "display_data",
      "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>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
+      "text/plain": "<IPython.core.display.HTML object>",
+      "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>0.18.0</td></tr><tr><td>Aer</td><td>0.8.2</td></tr><tr><td>Ignis</td><td>None</td></tr><tr><td>Aqua</td><td>None</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>3.8.5 (default, May 27 2021, 13:30:53) \n[GCC 9.3.0]</td></tr><tr><td>OS</td><td>Linux</td></tr><tr><td>CPUs</td><td>4</td></tr><tr><td>Memory (Gb)</td><td>14.650382995605469</td></tr><tr><td colspan='2'>Sun Jun 13 22:29:17 2021 JST</td></tr></table>"
      },
-     "metadata": {},
-     "output_type": "display_data"
+     "metadata": {}
     },
     {
+     "output_type": "display_data",
      "data": {
-      "text/html": [
-       "<div style='width: 100%; background-color:#d5d9e0;padding-left: 10px; padding-bottom: 10px; padding-right: 10px; padding-top: 5px'><h3>This code is a part of Qiskit</h3><p>&copy; Copyright IBM 2017, 2021.</p><p>This code is licensed under the Apache License, Version 2.0. You may<br>obtain a copy of this license in the LICENSE.txt file in the root directory<br> of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.<p>Any modifications or derivative works of this code must retain this<br>copyright notice, and modified files need to carry a notice indicating<br>that they have been altered from the originals.</p></div>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
+      "text/plain": "<IPython.core.display.HTML object>",
+      "text/html": "<div style='width: 100%; background-color:#d5d9e0;padding-left: 10px; padding-bottom: 10px; padding-right: 10px; padding-top: 5px'><h3>This code is a part of Qiskit</h3><p>&copy; Copyright IBM 2017, 2021.</p><p>This code is licensed under the Apache License, Version 2.0. You may<br>obtain a copy of this license in the LICENSE.txt file in the root directory<br> of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.<p>Any modifications or derivative works of this code must retain this<br>copyright notice, and modified files need to carry a notice indicating<br>that they have been altered from the originals.</p></div>"
      },
-     "metadata": {},
-     "output_type": "display_data"
+     "metadata": {}
     }
    ],
    "source": [
@@ -5248,9 +895,8 @@
   "anaconda-cloud": {},
   "celltoolbar": "Tags",
   "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
+   "name": "python3",
+   "display_name": "Python 3.8.5 64-bit ('dev38': venv)"
   },
   "language_info": {
    "codemirror_mode": {
@@ -5292,8 +938,11 @@
     "_Feature"
    ],
    "window_display": false
+  },
+  "interpreter": {
+   "hash": "b4fe86ebdff5cc3f22ee67b3ac6e6e0ad6d0d521745d3575c06ff4d245962764"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
+}
\ No newline at end of file
diff --git a/docs/tutorials/08_cvar_optimization.ipynb b/docs/tutorials/08_cvar_optimization.ipynb
index 2776619d1..e0da103ee 100644
--- a/docs/tutorials/08_cvar_optimization.ipynb
+++ b/docs/tutorials/08_cvar_optimization.ipynb
@@ -35,6 +35,7 @@
     "from qiskit_optimization import QuadraticProgram\n",
     "from qiskit_optimization.converters import LinearEqualityToPenalty\n",
     "from qiskit_optimization.algorithms import MinimumEigenOptimizer\n",
+    "from qiskit_optimization.translators import from_docplex_mp\n",
     "from qiskit import execute, Aer\n",
     "from qiskit.utils import algorithm_globals\n",
     "\n",
@@ -109,8 +110,7 @@
     "mdl.add_constraint(mdl.sum(x[i] for i in range(n)) == budget)\n",
     "\n",
     "# case to \n",
-    "qp = QuadraticProgram()\n",
-    "qp.from_docplex(mdl)"
+    "qp = from_docplex_mp(mdl)"
    ]
   },
   {
@@ -384,4 +384,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 2
-}
+}
\ No newline at end of file
diff --git a/docs/tutorials/10_warm_start_qaoa.ipynb b/docs/tutorials/10_warm_start_qaoa.ipynb
index 711a457e6..845af7ed7 100644
--- a/docs/tutorials/10_warm_start_qaoa.ipynb
+++ b/docs/tutorials/10_warm_start_qaoa.ipynb
@@ -51,7 +51,8 @@
     "from qiskit_optimization.algorithms import MinimumEigenOptimizer, CplexOptimizer\n",
     "from qiskit_optimization import QuadraticProgram\n",
     "from qiskit_optimization.problems.variable import VarType\n",
-    "from qiskit_optimization.converters.quadratic_program_to_qubo import QuadraticProgramToQubo"
+    "from qiskit_optimization.converters.quadratic_program_to_qubo import QuadraticProgramToQubo\n",
+    "from qiskit_optimization.translators import from_docplex_mp"
    ]
   },
   {
@@ -83,9 +84,7 @@
     "    cost = mdl.sum(x)\n",
     "    mdl.add_constraint(cost == total)\n",
     "    \n",
-    "    qp = QuadraticProgram()\n",
-    "    qp.from_docplex(mdl)\n",
-    "    \n",
+    "    qp = from_docplex_mp(mdl)\n",
     "    return qp\n",
     "\n",
     "def relax_problem(problem) -> QuadraticProgram:\n",
@@ -758,4 +757,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 5
-}
+}
\ No newline at end of file
diff --git a/qiskit_optimization/algorithms/cplex_optimizer.py b/qiskit_optimization/algorithms/cplex_optimizer.py
index 22e51aa3e..9bb8126b5 100644
--- a/qiskit_optimization/algorithms/cplex_optimizer.py
+++ b/qiskit_optimization/algorithms/cplex_optimizer.py
@@ -18,12 +18,13 @@
 
 from qiskit.exceptions import MissingOptionalLibraryError
 
+from qiskit_optimization.problems.quadratic_program import QuadraticProgram
+from qiskit_optimization.translators import to_docplex_mp
 from .optimization_algorithm import (
     OptimizationAlgorithm,
     OptimizationResult,
     OptimizationResultStatus,
 )
-from ..problems.quadratic_program import QuadraticProgram
 
 logger = logging.getLogger(__name__)
 
@@ -139,7 +140,7 @@ def solve(self, problem: QuadraticProgram) -> OptimizationResult:
             QiskitOptimizationError: If the problem is incompatible with the optimizer.
         """
 
-        mod = problem.to_docplex()
+        mod = to_docplex_mp(problem)
         sol = mod.solve(log_output=self._disp, cplex_parameters=self._cplex_parameters)
         if sol is None:
             # no solution is found
diff --git a/qiskit_optimization/algorithms/gurobi_optimizer.py b/qiskit_optimization/algorithms/gurobi_optimizer.py
index fa1b9fdea..7cc42bb15 100644
--- a/qiskit_optimization/algorithms/gurobi_optimizer.py
+++ b/qiskit_optimization/algorithms/gurobi_optimizer.py
@@ -15,9 +15,11 @@
 import logging
 
 from qiskit.exceptions import MissingOptionalLibraryError
-from .optimization_algorithm import OptimizationAlgorithm, OptimizationResult
+
 from ..exceptions import QiskitOptimizationError
 from ..problems.quadratic_program import QuadraticProgram
+from ..translators.gurobipy import to_gurobipy
+from .optimization_algorithm import OptimizationAlgorithm, OptimizationResult
 
 logger = logging.getLogger(__name__)
 
@@ -32,7 +34,7 @@
 class GurobiOptimizer(OptimizationAlgorithm):
     """The Gurobi optimizer wrapped as an Qiskit :class:`OptimizationAlgorithm`.
 
-    This class provides a wrapper for ``gurobipy`` (https://pypi.gurobi.com)
+    This class provides a wrapper for ``gurobipy``
     to be used within the optimization module.
 
     Examples:
@@ -63,7 +65,7 @@ def __init__(self, disp: bool = False) -> None:
             raise MissingOptionalLibraryError(
                 libname="GUROBI",
                 name="GurobiOptimizer",
-                pip_install="pip install -i https://pypi.gurobi.com gurobipy",
+                pip_install="pip install qiskit-optimization[gurobi]",
             )
 
         self._disp = disp
@@ -122,7 +124,7 @@ def solve(self, problem: QuadraticProgram) -> OptimizationResult:
         """
 
         # convert to Gurobi problem
-        model = problem.to_gurobipy()
+        model = to_gurobipy(problem)
 
         # Enable non-convex
         model.Params.NonConvex = 2
diff --git a/qiskit_optimization/applications/clique.py b/qiskit_optimization/applications/clique.py
index da7f730c6..527d5d292 100644
--- a/qiskit_optimization/applications/clique.py
+++ b/qiskit_optimization/applications/clique.py
@@ -19,6 +19,7 @@
 
 from qiskit_optimization.algorithms import OptimizationResult
 from qiskit_optimization.problems.quadratic_program import QuadraticProgram
+from qiskit_optimization.translators import from_docplex_mp
 from .graph_optimization_application import GraphOptimizationApplication
 
 
@@ -64,8 +65,7 @@ def to_quadratic_program(self) -> QuadraticProgram:
             mdl.maximize(mdl.sum(x[i] for i in x))
         else:
             mdl.add_constraint(mdl.sum(x[i] for i in x) == self.size)
-        op = QuadraticProgram()
-        op.from_docplex(mdl)
+        op = from_docplex_mp(mdl)
         return op
 
     def interpret(self, result: Union[OptimizationResult, np.ndarray]) -> List[int]:
diff --git a/qiskit_optimization/applications/exact_cover.py b/qiskit_optimization/applications/exact_cover.py
index 125d18093..ed4f821a7 100644
--- a/qiskit_optimization/applications/exact_cover.py
+++ b/qiskit_optimization/applications/exact_cover.py
@@ -18,6 +18,7 @@
 
 from qiskit_optimization.algorithms import OptimizationResult
 from qiskit_optimization.problems.quadratic_program import QuadraticProgram
+from qiskit_optimization.translators import from_docplex_mp
 from .optimization_application import OptimizationApplication
 
 
@@ -55,8 +56,7 @@ def to_quadratic_program(self) -> QuadraticProgram:
             mdl.add_constraint(
                 mdl.sum(x[i] for i, sub in enumerate(self._subsets) if element in sub) == 1
             )
-        op = QuadraticProgram()
-        op.from_docplex(mdl)
+        op = from_docplex_mp(mdl)
         return op
 
     def interpret(self, result: Union[OptimizationResult, np.ndarray]) -> List[List[int]]:
diff --git a/qiskit_optimization/applications/graph_partition.py b/qiskit_optimization/applications/graph_partition.py
index 9d3370e90..42ccf28a2 100644
--- a/qiskit_optimization/applications/graph_partition.py
+++ b/qiskit_optimization/applications/graph_partition.py
@@ -20,6 +20,7 @@
 
 from qiskit_optimization.algorithms import OptimizationResult
 from qiskit_optimization.problems.quadratic_program import QuadraticProgram
+from qiskit_optimization.translators import from_docplex_mp
 from .graph_optimization_application import GraphOptimizationApplication
 
 
@@ -49,8 +50,7 @@ def to_quadratic_program(self) -> QuadraticProgram:
         )
         mdl.minimize(objective)
         mdl.add_constraint(mdl.sum([x[i] for i in x]) == n // 2)
-        op = QuadraticProgram()
-        op.from_docplex(mdl)
+        op = from_docplex_mp(mdl)
         return op
 
     def interpret(self, result: Union[OptimizationResult, np.ndarray]) -> List[List[int]]:
diff --git a/qiskit_optimization/applications/knapsack.py b/qiskit_optimization/applications/knapsack.py
index 329c9d0ad..7e824bb27 100644
--- a/qiskit_optimization/applications/knapsack.py
+++ b/qiskit_optimization/applications/knapsack.py
@@ -18,6 +18,7 @@
 
 from qiskit_optimization.algorithms import OptimizationResult
 from qiskit_optimization.problems.quadratic_program import QuadraticProgram
+from qiskit_optimization.translators import from_docplex_mp
 from .optimization_application import OptimizationApplication
 
 
@@ -52,8 +53,7 @@ def to_quadratic_program(self) -> QuadraticProgram:
         x = {i: mdl.binary_var(name="x_{0}".format(i)) for i in range(len(self._values))}
         mdl.maximize(mdl.sum(self._values[i] * x[i] for i in x))
         mdl.add_constraint(mdl.sum(self._weights[i] * x[i] for i in x) <= self._max_weight)
-        op = QuadraticProgram()
-        op.from_docplex(mdl)
+        op = from_docplex_mp(mdl)
         return op
 
     def interpret(self, result: Union[OptimizationResult, np.ndarray]) -> List[int]:
diff --git a/qiskit_optimization/applications/max_cut.py b/qiskit_optimization/applications/max_cut.py
index 3510773cc..e64974a1d 100644
--- a/qiskit_optimization/applications/max_cut.py
+++ b/qiskit_optimization/applications/max_cut.py
@@ -20,6 +20,7 @@
 
 from qiskit_optimization.algorithms import OptimizationResult
 from qiskit_optimization.problems.quadratic_program import QuadraticProgram
+from qiskit_optimization.translators import from_docplex_mp
 from .graph_optimization_application import GraphOptimizationApplication
 
 
@@ -51,8 +52,7 @@ def to_quadratic_program(self) -> QuadraticProgram:
             for i, j in self._graph.edges
         )
         mdl.maximize(objective)
-        op = QuadraticProgram()
-        op.from_docplex(mdl)
+        op = from_docplex_mp(mdl)
         return op
 
     def _draw_result(
diff --git a/qiskit_optimization/applications/number_partition.py b/qiskit_optimization/applications/number_partition.py
index 4c72e058d..4ba98cd98 100644
--- a/qiskit_optimization/applications/number_partition.py
+++ b/qiskit_optimization/applications/number_partition.py
@@ -19,6 +19,7 @@
 
 from qiskit_optimization.algorithms import OptimizationResult
 from qiskit_optimization.problems.quadratic_program import QuadraticProgram
+from qiskit_optimization.translators import from_docplex_mp
 from .optimization_application import OptimizationApplication
 
 
@@ -50,8 +51,7 @@ def to_quadratic_program(self) -> QuadraticProgram:
         mdl.add_constraint(
             mdl.sum(num * (-2 * x[i] + 1) for i, num in enumerate(self._number_set)) == 0
         )
-        op = QuadraticProgram()
-        op.from_docplex(mdl)
+        op = from_docplex_mp(mdl)
         return op
 
     def interpret(self, result: Union[OptimizationResult, np.ndarray]) -> List[List[int]]:
diff --git a/qiskit_optimization/applications/set_packing.py b/qiskit_optimization/applications/set_packing.py
index 3341ff455..2507d5e7b 100644
--- a/qiskit_optimization/applications/set_packing.py
+++ b/qiskit_optimization/applications/set_packing.py
@@ -19,6 +19,7 @@
 
 from qiskit_optimization.algorithms import OptimizationResult
 from qiskit_optimization.problems.quadratic_program import QuadraticProgram
+from qiskit_optimization.translators import from_docplex_mp
 from .optimization_application import OptimizationApplication
 
 
@@ -56,8 +57,7 @@ def to_quadratic_program(self) -> QuadraticProgram:
             mdl.add_constraint(
                 mdl.sum(x[i] for i, sub in enumerate(self._subsets) if element in sub) <= 1
             )
-        op = QuadraticProgram()
-        op.from_docplex(mdl)
+        op = from_docplex_mp(mdl)
         return op
 
     def interpret(self, result: Union[OptimizationResult, np.ndarray]) -> List[List[int]]:
diff --git a/qiskit_optimization/applications/stable_set.py b/qiskit_optimization/applications/stable_set.py
index ae2fb8e07..72a4c037b 100644
--- a/qiskit_optimization/applications/stable_set.py
+++ b/qiskit_optimization/applications/stable_set.py
@@ -20,6 +20,7 @@
 
 from qiskit_optimization.algorithms import OptimizationResult
 from qiskit_optimization.problems.quadratic_program import QuadraticProgram
+from qiskit_optimization.translators import from_docplex_mp
 from .graph_optimization_application import GraphOptimizationApplication
 
 
@@ -48,8 +49,7 @@ def to_quadratic_program(self) -> QuadraticProgram:
         for w, v in self._graph.edges:
             mdl.add_constraint(x[w] + x[v] <= 1)
         mdl.maximize(objective)
-        op = QuadraticProgram()
-        op.from_docplex(mdl)
+        op = from_docplex_mp(mdl)
         return op
 
     def interpret(self, result: Union[OptimizationResult, np.ndarray]) -> List[int]:
diff --git a/qiskit_optimization/applications/tsp.py b/qiskit_optimization/applications/tsp.py
index 822f8a601..0d4e07ebf 100644
--- a/qiskit_optimization/applications/tsp.py
+++ b/qiskit_optimization/applications/tsp.py
@@ -18,9 +18,10 @@
 from docplex.mp.model import Model
 
 from qiskit.utils import algorithm_globals
-from ..algorithms import OptimizationResult
-from ..exceptions import QiskitOptimizationError
-from ..problems.quadratic_program import QuadraticProgram
+from qiskit_optimization.algorithms import OptimizationResult
+from qiskit_optimization.exceptions import QiskitOptimizationError
+from qiskit_optimization.problems import QuadraticProgram
+from qiskit_optimization.translators import from_docplex_mp
 from .graph_optimization_application import GraphOptimizationApplication
 
 
@@ -60,8 +61,7 @@ def to_quadratic_program(self) -> QuadraticProgram:
             mdl.add_constraint(mdl.sum(x[(i, k)] for k in range(n)) == 1)
         for k in range(n):
             mdl.add_constraint(mdl.sum(x[(i, k)] for i in range(n)) == 1)
-        op = QuadraticProgram()
-        op.from_docplex(mdl)
+        op = from_docplex_mp(mdl)
         return op
 
     def interpret(
diff --git a/qiskit_optimization/applications/vehicle_routing.py b/qiskit_optimization/applications/vehicle_routing.py
index 5be280d1d..3bb6bf3aa 100644
--- a/qiskit_optimization/applications/vehicle_routing.py
+++ b/qiskit_optimization/applications/vehicle_routing.py
@@ -22,6 +22,7 @@
 
 from qiskit_optimization.algorithms import OptimizationResult
 from qiskit_optimization.problems.quadratic_program import QuadraticProgram
+from qiskit_optimization.translators import from_docplex_mp
 from .graph_optimization_application import GraphOptimizationApplication
 
 
@@ -99,8 +100,7 @@ def to_quadratic_program(self) -> QuadraticProgram:
             mdl.add_constraint(
                 mdl.sum(x[(i, j)] for i in clique for j in clique if i != j) <= len(clique) - 1
             )
-        op = QuadraticProgram()
-        op.from_docplex(mdl)
+        op = from_docplex_mp(mdl)
         return op
 
     def interpret(self, result: Union[OptimizationResult, np.ndarray]) -> List[List[List[int]]]:
diff --git a/qiskit_optimization/applications/vertex_cover.py b/qiskit_optimization/applications/vertex_cover.py
index a645cd95b..d7c239eaa 100644
--- a/qiskit_optimization/applications/vertex_cover.py
+++ b/qiskit_optimization/applications/vertex_cover.py
@@ -20,6 +20,7 @@
 
 from qiskit_optimization.algorithms import OptimizationResult
 from qiskit_optimization.problems.quadratic_program import QuadraticProgram
+from qiskit_optimization.translators import from_docplex_mp
 from .graph_optimization_application import GraphOptimizationApplication
 
 
@@ -45,8 +46,7 @@ def to_quadratic_program(self) -> QuadraticProgram:
         for w, v in self._graph.edges:
             mdl.add_constraint(x[w] + x[v] >= 1)
         mdl.minimize(objective)
-        op = QuadraticProgram()
-        op.from_docplex(mdl)
+        op = from_docplex_mp(mdl)
         return op
 
     def interpret(self, result: Union[OptimizationResult, np.ndarray]) -> List[int]:
diff --git a/qiskit_optimization/problems/quadratic_program.py b/qiskit_optimization/problems/quadratic_program.py
index ca8f56978..3d1e1f972 100644
--- a/qiskit_optimization/problems/quadratic_program.py
+++ b/qiskit_optimization/problems/quadratic_program.py
@@ -21,37 +21,8 @@
 from typing import Dict, List, Optional, Tuple, Union, cast
 
 import numpy as np
-from docplex.mp.constr import LinearConstraint as DocplexLinearConstraint
-from docplex.mp.constr import NotEqualConstraint
-from docplex.mp.constr import QuadraticConstraint as DocplexQuadraticConstraint
-
-try:
-    # new location since docplex 2.16.196
-    from docplex.mp.dvar import Var
-except ImportError:
-    # old location until docplex 2.15.194
-    from docplex.mp.linear import Var
 from docplex.mp.model import Model
 from docplex.mp.model_reader import ModelReader
-from docplex.mp.quad import QuadExpr
-from docplex.mp.vartype import BinaryVarType, ContinuousVarType, IntegerVarType
-
-try:
-    import gurobipy as gp
-    from gurobipy import Model as GurobiModel
-
-    _HAS_GUROBI = True
-except ImportError:
-    _HAS_GUROBI = False
-
-    class GurobiModel:  # type: ignore
-        """Empty GurobiModel class
-        Replacement if gurobipy.Model is not present.
-        """
-
-        pass
-
-
 from numpy import ndarray, zeros
 from scipy.sparse import spmatrix
 
@@ -108,7 +79,7 @@ def __init__(self, name: str = "") -> None:
         self._objective = QuadraticObjective(self)
 
     def __repr__(self) -> str:
-        return self.to_docplex().export_as_lp_string()
+        return self.export_as_lp_string()
 
     def clear(self) -> None:
         """Clears the quadratic program, i.e., deletes all variables, constraints, the
@@ -886,147 +857,8 @@ def maximize(
             self, constant, linear, quadratic, QuadraticObjective.Sense.MAXIMIZE
         )
 
-    def from_gurobipy(self, model: GurobiModel) -> None:
-        """Loads this quadratic program from a gurobipy model.
-
-        Note that this supports only basic functions of gurobipy as follows:
-        - quadratic objective function
-        - linear / quadratic constraints
-        - binary / integer / continuous variables
-
-        Args:
-            model: The gurobipy model to be loaded.
-
-        Raises:
-            MissingOptionalLibraryError: gurobipy not installed
-            QiskitOptimizationError: if the model contains unsupported elements.
-        """
-        if not _HAS_GUROBI:
-            raise MissingOptionalLibraryError(
-                libname="GUROBI",
-                name="GurobiOptimizer",
-                pip_install="pip install -i https://pypi.gurobi.com gurobipy",
-            )
-
-        # clear current problem
-        self.clear()
-
-        # Update the model to make sure everything works as expected
-        model.update()
-
-        # get name
-        self.name = model.ModelName
-
-        # get variables
-        # keep track of names separately, since gurobipy allows to have None names.
-        var_names = {}
-        for x in model.getVars():
-            if x.vtype == gp.GRB.CONTINUOUS:
-                x_new = self.continuous_var(x.lb, x.ub, x.VarName)
-            elif x.vtype == gp.GRB.BINARY:
-                x_new = self.binary_var(x.VarName)
-            elif x.vtype == gp.GRB.INTEGER:
-                x_new = self.integer_var(x.lb, x.ub, x.VarName)
-            else:
-                raise QiskitOptimizationError(
-                    "Unsupported variable type: {} {}".format(x.VarName, x.vtype)
-                )
-            var_names[x] = x_new.name
-
-        # objective sense
-        minimize = model.ModelSense == gp.GRB.MINIMIZE
-
-        # Retrieve the objective
-        objective = model.getObjective()
-        has_quadratic_objective = False
-
-        # Retrieve the linear part in case it is a quadratic objective
-        if isinstance(objective, gp.QuadExpr):
-            linear_part = objective.getLinExpr()
-            has_quadratic_objective = True
-        else:
-            linear_part = objective
-
-        # Get the constant
-        constant = linear_part.getConstant()
-
-        # get linear part of objective
-        linear = {}
-        for i in range(linear_part.size()):
-            linear[var_names[linear_part.getVar(i)]] = linear_part.getCoeff(i)
-
-        # get quadratic part of objective
-        quadratic = {}
-        if has_quadratic_objective:
-            for i in range(objective.size()):
-                x = var_names[objective.getVar1(i)]
-                y = var_names[objective.getVar2(i)]
-                v = objective.getCoeff(i)
-                quadratic[x, y] = v
-
-        # set objective
-        if minimize:
-            self.minimize(constant, linear, quadratic)
-        else:
-            self.maximize(constant, linear, quadratic)
-
-        # check whether there are any general constraints
-        if model.NumSOS > 0 or model.NumGenConstrs > 0:
-            raise QiskitOptimizationError("Unsupported constraint: SOS or General Constraint")
-
-        # get linear constraints
-        for constraint in model.getConstrs():
-            name = constraint.ConstrName
-            sense = constraint.Sense
-
-            left_expr = model.getRow(constraint)
-            rhs = constraint.RHS
-
-            lhs = {}
-            for i in range(left_expr.size()):
-                lhs[var_names[left_expr.getVar(i)]] = left_expr.getCoeff(i)
-
-            if sense == gp.GRB.EQUAL:
-                self.linear_constraint(lhs, "==", rhs, name)
-            elif sense == gp.GRB.GREATER_EQUAL:
-                self.linear_constraint(lhs, ">=", rhs, name)
-            elif sense == gp.GRB.LESS_EQUAL:
-                self.linear_constraint(lhs, "<=", rhs, name)
-            else:
-                raise QiskitOptimizationError("Unsupported constraint sense: {}".format(constraint))
-
-        # get quadratic constraints
-        for constraint in model.getQConstrs():
-            name = constraint.QCName
-            sense = constraint.QCSense
-
-            left_expr = model.getQCRow(constraint)
-            rhs = constraint.QCRHS
-
-            linear = {}
-            quadratic = {}
-
-            linear_part = left_expr.getLinExpr()
-            for i in range(linear_part.size()):
-                linear[var_names[linear_part.getVar(i)]] = linear_part.getCoeff(i)
-
-            for i in range(left_expr.size()):
-                x = var_names[left_expr.getVar1(i)]
-                y = var_names[left_expr.getVar2(i)]
-                v = left_expr.getCoeff(i)
-                quadratic[x, y] = v
-
-            if sense == gp.GRB.EQUAL:
-                self.quadratic_constraint(linear, quadratic, "==", rhs, name)
-            elif sense == gp.GRB.GREATER_EQUAL:
-                self.quadratic_constraint(linear, quadratic, ">=", rhs, name)
-            elif sense == gp.GRB.LESS_EQUAL:
-                self.quadratic_constraint(linear, quadratic, "<=", rhs, name)
-            else:
-                raise QiskitOptimizationError("Unsupported constraint sense: {}".format(constraint))
-
     def from_docplex(self, model: Model) -> None:
-        """Loads this quadratic program from a docplex model.
+        """DEPRECATED Loads this quadratic program from a docplex model.
 
         Note that this supports only basic functions of docplex as follows:
         - quadratic objective function
@@ -1039,246 +871,22 @@ def from_docplex(self, model: Model) -> None:
         Raises:
             QiskitOptimizationError: if the model contains unsupported elements.
         """
+        warnings.warn(
+            "The from_docplex method is deprecated as of version 0.2.0 and will be "
+            "removed no sooner than 3 months after the release. Instead use the "
+            "qiskit_optimization.translators.from_docplex_mp function.",
+            DeprecationWarning,
+            stacklevel=2,
+        )
 
-        # clear current problem
-        self.clear()
-
-        # get name
-        self.name = model.name
-
-        # get variables
-        # keep track of names separately, since docplex allows to have None names.
-        var_names = {}
-        for x in model.iter_variables():
-            if isinstance(x.vartype, ContinuousVarType):
-                x_new = self.continuous_var(x.lb, x.ub, x.name)
-            elif isinstance(x.vartype, BinaryVarType):
-                x_new = self.binary_var(x.name)
-            elif isinstance(x.vartype, IntegerVarType):
-                x_new = self.integer_var(x.lb, x.ub, x.name)
-            else:
-                raise QiskitOptimizationError(
-                    "Unsupported variable type: {} {}".format(x.name, x.vartype)
-                )
-            var_names[x] = x_new.name
-
-        # objective sense
-        minimize = model.objective_sense.is_minimize()
-
-        # make sure objective expression is linear or quadratic and not a variable
-        if isinstance(model.objective_expr, Var):
-            model.objective_expr = model.objective_expr + 0
-
-        # get objective offset
-        constant = model.objective_expr.constant
-
-        # get linear part of objective
-        linear = {}
-        linear_part = model.objective_expr.get_linear_part()
-        for x in linear_part.iter_variables():
-            linear[var_names[x]] = linear_part.get_coef(x)
-
-        # get quadratic part of objective
-        quadratic = {}
-        if isinstance(model.objective_expr, QuadExpr):
-            for quad_triplet in model.objective_expr.iter_quad_triplets():
-                i = var_names[quad_triplet[0]]
-                j = var_names[quad_triplet[1]]
-                v = quad_triplet[2]
-                quadratic[i, j] = v
-
-        # set objective
-        if minimize:
-            self.minimize(constant, linear, quadratic)
-        else:
-            self.maximize(constant, linear, quadratic)
-
-        # get linear constraints
-        for constraint in model.iter_constraints():
-            if isinstance(constraint, DocplexQuadraticConstraint):
-                # ignore quadratic constraints here and process them later
-                continue
-            if not isinstance(constraint, DocplexLinearConstraint) or isinstance(
-                constraint, NotEqualConstraint
-            ):
-                # If any constraint is not linear/quadratic constraints, it raises an error.
-                # Notice that NotEqualConstraint is a subclass of Docplex's LinearConstraint,
-                # but it cannot be handled by optimization.
-                raise QiskitOptimizationError("Unsupported constraint: {}".format(constraint))
-            name = constraint.name
-            sense = constraint.sense
-
-            left_expr = constraint.get_left_expr()
-            right_expr = constraint.get_right_expr()
-            # for linear constraints we may get an instance of Var instead of expression,
-            # e.g. x + y = z
-            if isinstance(left_expr, Var):
-                left_expr = left_expr + 0
-            if isinstance(right_expr, Var):
-                right_expr = right_expr + 0
-
-            rhs = right_expr.constant - left_expr.constant
-
-            lhs = {}
-            for x in left_expr.iter_variables():
-                lhs[var_names[x]] = left_expr.get_coef(x)
-            for x in right_expr.iter_variables():
-                lhs[var_names[x]] = lhs.get(var_names[x], 0.0) - right_expr.get_coef(x)
-
-            if sense == sense.EQ:
-                self.linear_constraint(lhs, "==", rhs, name)
-            elif sense == sense.GE:
-                self.linear_constraint(lhs, ">=", rhs, name)
-            elif sense == sense.LE:
-                self.linear_constraint(lhs, "<=", rhs, name)
-            else:
-                raise QiskitOptimizationError("Unsupported constraint sense: {}".format(constraint))
-
-        # get quadratic constraints
-        for constraint in model.iter_quadratic_constraints():
-            name = constraint.name
-            sense = constraint.sense
-
-            left_expr = constraint.get_left_expr()
-            right_expr = constraint.get_right_expr()
-
-            rhs = right_expr.constant - left_expr.constant
-            linear = {}
-            quadratic = {}
-
-            if left_expr.is_quad_expr():
-                for x in left_expr.linear_part.iter_variables():
-                    linear[var_names[x]] = left_expr.linear_part.get_coef(x)
-                for quad_triplet in left_expr.iter_quad_triplets():
-                    i = var_names[quad_triplet[0]]
-                    j = var_names[quad_triplet[1]]
-                    v = quad_triplet[2]
-                    quadratic[i, j] = v
-            else:
-                for x in left_expr.iter_variables():
-                    linear[var_names[x]] = left_expr.get_coef(x)
-
-            if right_expr.is_quad_expr():
-                for x in right_expr.linear_part.iter_variables():
-                    linear[var_names[x]] = linear.get(
-                        var_names[x], 0.0
-                    ) - right_expr.linear_part.get_coef(x)
-                for quad_triplet in right_expr.iter_quad_triplets():
-                    i = var_names[quad_triplet[0]]
-                    j = var_names[quad_triplet[1]]
-                    v = quad_triplet[2]
-                    quadratic[i, j] = quadratic.get((i, j), 0.0) - v
-            else:
-                for x in right_expr.iter_variables():
-                    linear[var_names[x]] = linear.get(var_names[x], 0.0) - right_expr.get_coef(x)
-
-            if sense == sense.EQ:
-                self.quadratic_constraint(linear, quadratic, "==", rhs, name)
-            elif sense == sense.GE:
-                self.quadratic_constraint(linear, quadratic, ">=", rhs, name)
-            elif sense == sense.LE:
-                self.quadratic_constraint(linear, quadratic, "<=", rhs, name)
-            else:
-                raise QiskitOptimizationError("Unsupported constraint sense: {}".format(constraint))
-
-    def to_gurobipy(self) -> GurobiModel:
-        """Returns a gurobipy model corresponding to this quadratic program
-
-        Returns:
-            The gurobipy model corresponding to this quadratic program.
-
-        Raises:
-            MissingOptionalLibraryError: gurobipy not installed
-            QiskitOptimizationError: if non-supported elements (should never happen).
-        """
-        if not _HAS_GUROBI:
-            raise MissingOptionalLibraryError(
-                libname="GUROBI",
-                name="GurobiOptimizer",
-                pip_install="pip install -i https://pypi.gurobi.com gurobipy",
-            )
-
-        # initialize model
-        mdl = gp.Model(self.name)
-
-        # add variables
-        var = {}
-        for idx, x in enumerate(self.variables):
-            if x.vartype == Variable.Type.CONTINUOUS:
-                var[idx] = mdl.addVar(
-                    vtype=gp.GRB.CONTINUOUS, lb=x.lowerbound, ub=x.upperbound, name=x.name
-                )
-            elif x.vartype == Variable.Type.BINARY:
-                var[idx] = mdl.addVar(vtype=gp.GRB.BINARY, name=x.name)
-            elif x.vartype == Variable.Type.INTEGER:
-                var[idx] = mdl.addVar(
-                    vtype=gp.GRB.INTEGER, lb=x.lowerbound, ub=x.upperbound, name=x.name
-                )
-            else:
-                # should never happen
-                raise QiskitOptimizationError("Unsupported variable type: {}".format(x.vartype))
-
-        # add objective
-        objective = self.objective.constant
-        for i, v in self.objective.linear.to_dict().items():
-            objective += v * var[cast(int, i)]
-        for (i, j), v in self.objective.quadratic.to_dict().items():
-            objective += v * var[cast(int, i)] * var[cast(int, j)]
-        if self.objective.sense == QuadraticObjective.Sense.MINIMIZE:
-            mdl.setObjective(objective, sense=gp.GRB.MINIMIZE)
-        else:
-            mdl.setObjective(objective, sense=gp.GRB.MAXIMIZE)
-
-        # add linear constraints
-        for i, l_constraint in enumerate(self.linear_constraints):
-            name = l_constraint.name
-            rhs = l_constraint.rhs
-            if rhs == 0 and l_constraint.linear.coefficients.nnz == 0:
-                continue
-            linear_expr = 0
-            for j, v in l_constraint.linear.to_dict().items():
-                linear_expr += v * var[cast(int, j)]
-            sense = l_constraint.sense
-            if sense == Constraint.Sense.EQ:
-                mdl.addConstr(linear_expr == rhs, name=name)
-            elif sense == Constraint.Sense.GE:
-                mdl.addConstr(linear_expr >= rhs, name=name)
-            elif sense == Constraint.Sense.LE:
-                mdl.addConstr(linear_expr <= rhs, name=name)
-            else:
-                # should never happen
-                raise QiskitOptimizationError("Unsupported constraint sense: {}".format(sense))
-
-        # add quadratic constraints
-        for i, q_constraint in enumerate(self.quadratic_constraints):
-            name = q_constraint.name
-            rhs = q_constraint.rhs
-            if (
-                rhs == 0
-                and q_constraint.linear.coefficients.nnz == 0
-                and q_constraint.quadratic.coefficients.nnz == 0
-            ):
-                continue
-            quadratic_expr = 0
-            for j, v in q_constraint.linear.to_dict().items():
-                quadratic_expr += v * var[cast(int, j)]
-            for (j, k), v in q_constraint.quadratic.to_dict().items():
-                quadratic_expr += v * var[cast(int, j)] * var[cast(int, k)]
-            sense = q_constraint.sense
-            if sense == Constraint.Sense.EQ:
-                mdl.addConstr(quadratic_expr == rhs, name=name)
-            elif sense == Constraint.Sense.GE:
-                mdl.addConstr(quadratic_expr >= rhs, name=name)
-            elif sense == Constraint.Sense.LE:
-                mdl.addConstr(quadratic_expr <= rhs, name=name)
-            else:
-                # should never happen
-                raise QiskitOptimizationError("Unsupported constraint sense: {}".format(sense))
+        from ..translators.docplex_mp import from_docplex_mp
 
-        return mdl
+        other = from_docplex_mp(model)
+        for attr, val in vars(other).items():
+            setattr(self, attr, val)
 
     def to_docplex(self) -> Model:
-        """Returns a docplex model corresponding to this quadratic program.
+        """DEPRECATED Returns a docplex model corresponding to this quadratic program.
 
         Returns:
             The docplex model corresponding to this quadratic program.
@@ -1286,81 +894,17 @@ def to_docplex(self) -> Model:
         Raises:
             QiskitOptimizationError: if non-supported elements (should never happen).
         """
+        warnings.warn(
+            "The to_docplex method is deprecated as of version 0.2.0 and will be "
+            "removed no sooner than 3 months after the release. Instead use the "
+            "qiskit_optimization.translators.to_docplex_mp function.",
+            DeprecationWarning,
+            stacklevel=2,
+        )
 
-        # initialize model
-        mdl = Model(self.name)
-
-        # add variables
-        var = {}
-        for idx, x in enumerate(self.variables):
-            if x.vartype == Variable.Type.CONTINUOUS:
-                var[idx] = mdl.continuous_var(lb=x.lowerbound, ub=x.upperbound, name=x.name)
-            elif x.vartype == Variable.Type.BINARY:
-                var[idx] = mdl.binary_var(name=x.name)
-            elif x.vartype == Variable.Type.INTEGER:
-                var[idx] = mdl.integer_var(lb=x.lowerbound, ub=x.upperbound, name=x.name)
-            else:
-                # should never happen
-                raise QiskitOptimizationError("Unsupported variable type: {}".format(x.vartype))
-
-        # add objective
-        objective = self.objective.constant
-        for i, v in self.objective.linear.to_dict().items():
-            objective += v * var[cast(int, i)]
-        for (i, j), v in self.objective.quadratic.to_dict().items():
-            objective += v * var[cast(int, i)] * var[cast(int, j)]
-        if self.objective.sense == QuadraticObjective.Sense.MINIMIZE:
-            mdl.minimize(objective)
-        else:
-            mdl.maximize(objective)
-
-        # add linear constraints
-        for i, l_constraint in enumerate(self.linear_constraints):
-            name = l_constraint.name
-            rhs = l_constraint.rhs
-            if rhs == 0 and l_constraint.linear.coefficients.nnz == 0:
-                continue
-            linear_expr = 0
-            for j, v in l_constraint.linear.to_dict().items():
-                linear_expr += v * var[cast(int, j)]
-            sense = l_constraint.sense
-            if sense == Constraint.Sense.EQ:
-                mdl.add_constraint(linear_expr == rhs, ctname=name)
-            elif sense == Constraint.Sense.GE:
-                mdl.add_constraint(linear_expr >= rhs, ctname=name)
-            elif sense == Constraint.Sense.LE:
-                mdl.add_constraint(linear_expr <= rhs, ctname=name)
-            else:
-                # should never happen
-                raise QiskitOptimizationError("Unsupported constraint sense: {}".format(sense))
-
-        # add quadratic constraints
-        for i, q_constraint in enumerate(self.quadratic_constraints):
-            name = q_constraint.name
-            rhs = q_constraint.rhs
-            if (
-                rhs == 0
-                and q_constraint.linear.coefficients.nnz == 0
-                and q_constraint.quadratic.coefficients.nnz == 0
-            ):
-                continue
-            quadratic_expr = 0
-            for j, v in q_constraint.linear.to_dict().items():
-                quadratic_expr += v * var[cast(int, j)]
-            for (j, k), v in q_constraint.quadratic.to_dict().items():
-                quadratic_expr += v * var[cast(int, j)] * var[cast(int, k)]
-            sense = q_constraint.sense
-            if sense == Constraint.Sense.EQ:
-                mdl.add_constraint(quadratic_expr == rhs, ctname=name)
-            elif sense == Constraint.Sense.GE:
-                mdl.add_constraint(quadratic_expr >= rhs, ctname=name)
-            elif sense == Constraint.Sense.LE:
-                mdl.add_constraint(quadratic_expr <= rhs, ctname=name)
-            else:
-                # should never happen
-                raise QiskitOptimizationError("Unsupported constraint sense: {}".format(sense))
+        from ..translators.docplex_mp import to_docplex_mp
 
-        return mdl
+        return to_docplex_mp(self)
 
     def export_as_lp_string(self) -> str:
         """Returns the quadratic program as a string of LP format.
@@ -1368,37 +912,9 @@ def export_as_lp_string(self) -> str:
         Returns:
             A string representing the quadratic program.
         """
-        return self.to_docplex().export_as_lp_string()
+        from ..translators.docplex_mp import to_docplex_mp
 
-    def pprint_as_string(self) -> str:
-        """DEPRECATED Returns the quadratic program as a string in Docplex's pretty print format.
-        Returns:
-            A string representing the quadratic program.
-        """
-        warnings.warn(
-            "The pprint_as_string method is deprecated and will be "
-            "removed in a future release. Instead use the"
-            "to_docplex() method and run pprint_as_string() on that "
-            "output",
-            DeprecationWarning,
-        )
-        return self.to_docplex().pprint_as_string()
-
-    def prettyprint(self, out: Optional[str] = None) -> None:
-        """DEPRECATED Pretty prints the quadratic program to a given output stream (None = default).
-
-        Args:
-            out: The output stream or file name to print to.
-              if you specify a file name, the output file name is has '.mod' as suffix.
-        """
-        warnings.warn(
-            "The prettyprint method is deprecated and will be "
-            "removed in a future release. Instead use the"
-            "to_docplex() method and run prettyprint() on that "
-            "output",
-            DeprecationWarning,
-        )
-        self.to_docplex().prettyprint(out)
+        return to_docplex_mp(self).export_as_lp_string()
 
     def read_from_lp_file(self, filename: str) -> None:
         """Loads the quadratic program from a LP file.
@@ -1436,9 +952,12 @@ def _parse_problem_name(filename: str) -> str:
                         break
             return model_name
 
-        model_reader = ModelReader()
-        model = model_reader.read(filename, model_name=_parse_problem_name(filename))
-        self.from_docplex(model)
+        from ..translators.docplex_mp import from_docplex_mp
+
+        model = ModelReader().read(filename, model_name=_parse_problem_name(filename))
+        other = from_docplex_mp(model)
+        for attr, val in vars(other).items():
+            setattr(self, attr, val)
 
     def write_to_lp_file(self, filename: str) -> None:
         """Writes the quadratic program to an LP file.
@@ -1452,7 +971,10 @@ def write_to_lp_file(self, filename: str) -> None:
             OSError: If this cannot open a file.
             DOcplexException: If filename is an empty string
         """
-        self.to_docplex().export_as_lp(filename)
+        from ..translators.docplex_mp import to_docplex_mp
+
+        mdl = to_docplex_mp(self)
+        mdl.export_as_lp(filename)
 
     def substitute_variables(
         self,
diff --git a/qiskit_optimization/translators/__init__.py b/qiskit_optimization/translators/__init__.py
new file mode 100644
index 000000000..4f8655f0d
--- /dev/null
+++ b/qiskit_optimization/translators/__init__.py
@@ -0,0 +1,37 @@
+# This code is part of Qiskit.
+#
+# (C) Copyright IBM 2021.
+#
+# This code is licensed under the Apache License, Version 2.0. You may
+# obtain a copy of this license in the LICENSE.txt file in the root directory
+# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
+#
+# Any modifications or derivative works of this code must retain this
+# copyright notice, and modified files need to carry a notice indicating
+# that they have been altered from the originals.
+
+"""
+Quadratic program translators (:mod:`qiskit_optimization.translators`)
+======================================================================
+
+.. currentmodule:: qiskit_optimization.translators
+
+Translators between :class:`~qiskit_optimization.problems.QuadraticProgram` and
+other optimization models.
+
+Translators
+======================
+.. autosummary::
+   :toctree: ../stubs/
+   :nosignatures:
+
+   from_docplex_mp
+   to_docplex_mp
+   from_gurobipy
+   to_gurobipy
+"""
+
+from .docplex_mp import from_docplex_mp, to_docplex_mp
+from .gurobipy import from_gurobipy, to_gurobipy
+
+_all = ["from_docplex_mp", "to_docplex_mp", "from_gurobipy", "to_gurobipy"]
diff --git a/qiskit_optimization/translators/docplex_mp.py b/qiskit_optimization/translators/docplex_mp.py
new file mode 100644
index 000000000..e708bb6de
--- /dev/null
+++ b/qiskit_optimization/translators/docplex_mp.py
@@ -0,0 +1,292 @@
+# This code is part of Qiskit.
+#
+# (C) Copyright IBM 2021.
+#
+# This code is licensed under the Apache License, Version 2.0. You may
+# obtain a copy of this license in the LICENSE.txt file in the root directory
+# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
+#
+# Any modifications or derivative works of this code must retain this
+# copyright notice, and modified files need to carry a notice indicating
+# that they have been altered from the originals.
+
+"""Translator between a docplex.mp model and a quadratic program"""
+
+from typing import TYPE_CHECKING, cast
+
+from docplex.mp.constr import LinearConstraint as DocplexLinearConstraint
+from docplex.mp.constr import NotEqualConstraint
+from docplex.mp.constr import QuadraticConstraint as DocplexQuadraticConstraint
+from docplex.mp.dvar import Var
+from docplex.mp.model import Model
+from docplex.mp.quad import QuadExpr
+from docplex.mp.vartype import BinaryVarType, ContinuousVarType, IntegerVarType
+
+from qiskit_optimization.exceptions import QiskitOptimizationError
+from qiskit_optimization.problems.constraint import Constraint
+from qiskit_optimization.problems.quadratic_objective import QuadraticObjective
+from qiskit_optimization.problems.variable import Variable
+
+if TYPE_CHECKING:
+    # pylint: disable=cyclic-import
+    from qiskit_optimization.problems.quadratic_program import QuadraticProgram
+
+
+def to_docplex_mp(quadratic_program: "QuadraticProgram") -> Model:
+    """Returns a docplex.mp model corresponding to a quadratic program.
+
+    Args:
+        quadratic_program: The quadratic program to be translated.
+
+    Returns:
+        The docplex.mp model corresponding to a quadratic program.
+
+    Raises:
+        QiskitOptimizationError: if the model contains non-supported elements (should never happen).
+    """
+    # initialize model
+    mdl = Model(quadratic_program.name)
+
+    # add variables
+    var = {}
+    for idx, x in enumerate(quadratic_program.variables):
+        if x.vartype == Variable.Type.CONTINUOUS:
+            var[idx] = mdl.continuous_var(lb=x.lowerbound, ub=x.upperbound, name=x.name)
+        elif x.vartype == Variable.Type.BINARY:
+            var[idx] = mdl.binary_var(name=x.name)
+        elif x.vartype == Variable.Type.INTEGER:
+            var[idx] = mdl.integer_var(lb=x.lowerbound, ub=x.upperbound, name=x.name)
+        else:
+            # should never happen
+            raise QiskitOptimizationError(
+                "Internal error: unsupported variable type: {}".format(x.vartype)
+            )
+
+    # add objective
+    objective = quadratic_program.objective.constant
+    for i, v in quadratic_program.objective.linear.to_dict().items():
+        objective += v * var[cast(int, i)]
+    for (i, j), v in quadratic_program.objective.quadratic.to_dict().items():
+        objective += v * var[cast(int, i)] * var[cast(int, j)]
+    if quadratic_program.objective.sense == QuadraticObjective.Sense.MINIMIZE:
+        mdl.minimize(objective)
+    else:
+        mdl.maximize(objective)
+
+    # add linear constraints
+    for i, l_constraint in enumerate(quadratic_program.linear_constraints):
+        name = l_constraint.name
+        rhs = l_constraint.rhs
+        if rhs == 0 and l_constraint.linear.coefficients.nnz == 0:
+            continue
+        linear_expr = 0
+        for j, v in l_constraint.linear.to_dict().items():
+            linear_expr += v * var[cast(int, j)]
+        sense = l_constraint.sense
+        if sense == Constraint.Sense.EQ:
+            mdl.add_constraint(linear_expr == rhs, ctname=name)
+        elif sense == Constraint.Sense.GE:
+            mdl.add_constraint(linear_expr >= rhs, ctname=name)
+        elif sense == Constraint.Sense.LE:
+            mdl.add_constraint(linear_expr <= rhs, ctname=name)
+        else:
+            # should never happen
+            raise QiskitOptimizationError(
+                "Internal error: unsupported constraint sense: {}".format(sense)
+            )
+
+    # add quadratic constraints
+    for i, q_constraint in enumerate(quadratic_program.quadratic_constraints):
+        name = q_constraint.name
+        rhs = q_constraint.rhs
+        if (
+            rhs == 0
+            and q_constraint.linear.coefficients.nnz == 0
+            and q_constraint.quadratic.coefficients.nnz == 0
+        ):
+            continue
+        quadratic_expr = 0
+        for j, v in q_constraint.linear.to_dict().items():
+            quadratic_expr += v * var[cast(int, j)]
+        for (j, k), v in q_constraint.quadratic.to_dict().items():
+            quadratic_expr += v * var[cast(int, j)] * var[cast(int, k)]
+        sense = q_constraint.sense
+        if sense == Constraint.Sense.EQ:
+            mdl.add_constraint(quadratic_expr == rhs, ctname=name)
+        elif sense == Constraint.Sense.GE:
+            mdl.add_constraint(quadratic_expr >= rhs, ctname=name)
+        elif sense == Constraint.Sense.LE:
+            mdl.add_constraint(quadratic_expr <= rhs, ctname=name)
+        else:
+            # should never happen
+            raise QiskitOptimizationError(
+                "Internal error: unsupported constraint sense: {}".format(sense)
+            )
+
+    return mdl
+
+
+def from_docplex_mp(model: Model) -> "QuadraticProgram":
+    """Translate a docplex.mp model into a quadratic program.
+
+    Note that this supports only basic functions of docplex as follows:
+    - quadratic objective function
+    - linear / quadratic constraints
+    - binary / integer / continuous variables
+
+    Args:
+        model: The docplex.mp model to be loaded.
+
+    Returns:
+        The quadratic program corresponding to the model.
+
+    Raises:
+        QiskitOptimizationError: if the model contains unsupported elements.
+    """
+    if not isinstance(model, Model):
+        raise QiskitOptimizationError(f"The model is not compatible: {model}")
+
+    # pylint: disable=cyclic-import
+    from qiskit_optimization.problems.quadratic_program import QuadraticProgram
+
+    quadratic_program = QuadraticProgram()
+
+    # get name
+    quadratic_program.name = model.name
+
+    # get variables
+    # keep track of names separately, since docplex allows to have None names.
+    var_names = {}
+    for x in model.iter_variables():
+        if isinstance(x.vartype, ContinuousVarType):
+            x_new = quadratic_program.continuous_var(x.lb, x.ub, x.name)
+        elif isinstance(x.vartype, BinaryVarType):
+            x_new = quadratic_program.binary_var(x.name)
+        elif isinstance(x.vartype, IntegerVarType):
+            x_new = quadratic_program.integer_var(x.lb, x.ub, x.name)
+        else:
+            raise QiskitOptimizationError(
+                "Unsupported variable type: {} {}".format(x.name, x.vartype)
+            )
+        var_names[x] = x_new.name
+
+    # objective sense
+    minimize = model.objective_sense.is_minimize()
+
+    # make sure objective expression is linear or quadratic and not a variable
+    if isinstance(model.objective_expr, Var):
+        model.objective_expr = model.objective_expr + 0
+
+    # get objective offset
+    constant = model.objective_expr.constant
+
+    # get linear part of objective
+    linear = {}
+    linear_part = model.objective_expr.get_linear_part()
+    for x in linear_part.iter_variables():
+        linear[var_names[x]] = linear_part.get_coef(x)
+
+    # get quadratic part of objective
+    quadratic = {}
+    if isinstance(model.objective_expr, QuadExpr):
+        for quad_triplet in model.objective_expr.iter_quad_triplets():
+            i = var_names[quad_triplet[0]]
+            j = var_names[quad_triplet[1]]
+            v = quad_triplet[2]
+            quadratic[i, j] = v
+
+    # set objective
+    if minimize:
+        quadratic_program.minimize(constant, linear, quadratic)
+    else:
+        quadratic_program.maximize(constant, linear, quadratic)
+
+    # get linear constraints
+    for constraint in model.iter_constraints():
+        if isinstance(constraint, DocplexQuadraticConstraint):
+            # ignore quadratic constraints here and process them later
+            continue
+        if not isinstance(constraint, DocplexLinearConstraint) or isinstance(
+            constraint, NotEqualConstraint
+        ):
+            # If any constraint is not linear/quadratic constraints, it raises an error.
+            # Notice that NotEqualConstraint is a subclass of Docplex's LinearConstraint,
+            # but it cannot be handled by optimization.
+            raise QiskitOptimizationError("Unsupported constraint: {}".format(constraint))
+        name = constraint.name
+        sense = constraint.sense
+
+        left_expr = constraint.get_left_expr()
+        right_expr = constraint.get_right_expr()
+        # for linear constraints we may get an instance of Var instead of expression,
+        # e.g. x + y = z
+        if isinstance(left_expr, Var):
+            left_expr = left_expr + 0
+        if isinstance(right_expr, Var):
+            right_expr = right_expr + 0
+
+        rhs = right_expr.constant - left_expr.constant
+
+        lhs = {}
+        for x in left_expr.iter_variables():
+            lhs[var_names[x]] = left_expr.get_coef(x)
+        for x in right_expr.iter_variables():
+            lhs[var_names[x]] = lhs.get(var_names[x], 0.0) - right_expr.get_coef(x)
+
+        if sense == sense.EQ:
+            quadratic_program.linear_constraint(lhs, "==", rhs, name)
+        elif sense == sense.GE:
+            quadratic_program.linear_constraint(lhs, ">=", rhs, name)
+        elif sense == sense.LE:
+            quadratic_program.linear_constraint(lhs, "<=", rhs, name)
+        else:
+            raise QiskitOptimizationError("Unsupported constraint sense: {}".format(constraint))
+
+    # get quadratic constraints
+    for constraint in model.iter_quadratic_constraints():
+        name = constraint.name
+        sense = constraint.sense
+
+        left_expr = constraint.get_left_expr()
+        right_expr = constraint.get_right_expr()
+
+        rhs = right_expr.constant - left_expr.constant
+        linear = {}
+        quadratic = {}
+
+        if left_expr.is_quad_expr():
+            for x in left_expr.linear_part.iter_variables():
+                linear[var_names[x]] = left_expr.linear_part.get_coef(x)
+            for quad_triplet in left_expr.iter_quad_triplets():
+                i = var_names[quad_triplet[0]]
+                j = var_names[quad_triplet[1]]
+                v = quad_triplet[2]
+                quadratic[i, j] = v
+        else:
+            for x in left_expr.iter_variables():
+                linear[var_names[x]] = left_expr.get_coef(x)
+
+        if right_expr.is_quad_expr():
+            for x in right_expr.linear_part.iter_variables():
+                linear[var_names[x]] = linear.get(
+                    var_names[x], 0.0
+                ) - right_expr.linear_part.get_coef(x)
+            for quad_triplet in right_expr.iter_quad_triplets():
+                i = var_names[quad_triplet[0]]
+                j = var_names[quad_triplet[1]]
+                v = quad_triplet[2]
+                quadratic[i, j] = quadratic.get((i, j), 0.0) - v
+        else:
+            for x in right_expr.iter_variables():
+                linear[var_names[x]] = linear.get(var_names[x], 0.0) - right_expr.get_coef(x)
+
+        if sense == sense.EQ:
+            quadratic_program.quadratic_constraint(linear, quadratic, "==", rhs, name)
+        elif sense == sense.GE:
+            quadratic_program.quadratic_constraint(linear, quadratic, ">=", rhs, name)
+        elif sense == sense.LE:
+            quadratic_program.quadratic_constraint(linear, quadratic, "<=", rhs, name)
+        else:
+            raise QiskitOptimizationError("Unsupported constraint sense: {}".format(constraint))
+
+    return quadratic_program
diff --git a/qiskit_optimization/translators/gurobipy.py b/qiskit_optimization/translators/gurobipy.py
new file mode 100644
index 000000000..c019a383c
--- /dev/null
+++ b/qiskit_optimization/translators/gurobipy.py
@@ -0,0 +1,292 @@
+# This code is part of Qiskit.
+#
+# (C) Copyright IBM 2021.
+#
+# This code is licensed under the Apache License, Version 2.0. You may
+# obtain a copy of this license in the LICENSE.txt file in the root directory
+# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
+#
+# Any modifications or derivative works of this code must retain this
+# copyright notice, and modified files need to carry a notice indicating
+# that they have been altered from the originals.
+
+"""Translator between a gurobipy model and a quadratic program"""
+
+from typing import TYPE_CHECKING, cast
+
+try:
+    import gurobipy as gp
+    from gurobipy import Model
+
+    _HAS_GUROBI = True
+except ImportError:
+    _HAS_GUROBI = False
+
+    class Model:  # type: ignore
+        """Empty Model class
+        Replacement if gurobipy.Model is not present.
+        """
+
+        pass
+
+
+from qiskit.exceptions import MissingOptionalLibraryError
+from qiskit_optimization.exceptions import QiskitOptimizationError
+from qiskit_optimization.problems.constraint import Constraint
+from qiskit_optimization.problems.quadratic_objective import QuadraticObjective
+from qiskit_optimization.problems.variable import Variable
+
+if TYPE_CHECKING:
+    # pylint: disable=cyclic-import
+    from qiskit_optimization.problems.quadratic_program import QuadraticProgram
+
+
+def _check_gurobipy_is_installed(name: str):
+    if not _HAS_GUROBI:
+        raise MissingOptionalLibraryError(
+            libname="GUROBI",
+            name=name,
+            pip_install="pip install qiskit-optimization[gurobi]",
+        )
+
+
+def to_gurobipy(quadratic_program: "QuadraticProgram") -> Model:
+    """Returns a gurobipy model corresponding to a quadratic program.
+
+    Args:
+        quadratic_program: The quadratic program to be translated.
+
+    Returns:
+        The gurobipy model corresponding to a quadratic program.
+
+    Raises:
+        QiskitOptimizationError: if non-supported elements (should never happen).
+        MissingOptionalLibraryError: if gurobipy is not installed.
+    """
+
+    _check_gurobipy_is_installed("to_gurobipy")
+
+    # initialize model
+    mdl = gp.Model(quadratic_program.name)
+
+    # add variables
+    var = {}
+    for idx, x in enumerate(quadratic_program.variables):
+        if x.vartype == Variable.Type.CONTINUOUS:
+            var[idx] = mdl.addVar(
+                vtype=gp.GRB.CONTINUOUS, lb=x.lowerbound, ub=x.upperbound, name=x.name
+            )
+        elif x.vartype == Variable.Type.BINARY:
+            var[idx] = mdl.addVar(vtype=gp.GRB.BINARY, name=x.name)
+        elif x.vartype == Variable.Type.INTEGER:
+            var[idx] = mdl.addVar(
+                vtype=gp.GRB.INTEGER, lb=x.lowerbound, ub=x.upperbound, name=x.name
+            )
+        else:
+            # should never happen
+            raise QiskitOptimizationError("Unsupported variable type: {}".format(x.vartype))
+
+    # add objective
+    objective = quadratic_program.objective.constant
+    for i, v in quadratic_program.objective.linear.to_dict().items():
+        objective += v * var[cast(int, i)]
+    for (i, j), v in quadratic_program.objective.quadratic.to_dict().items():
+        objective += v * var[cast(int, i)] * var[cast(int, j)]
+    if quadratic_program.objective.sense == QuadraticObjective.Sense.MINIMIZE:
+        mdl.setObjective(objective, sense=gp.GRB.MINIMIZE)
+    else:
+        mdl.setObjective(objective, sense=gp.GRB.MAXIMIZE)
+
+    # add linear constraints
+    for i, l_constraint in enumerate(quadratic_program.linear_constraints):
+        name = l_constraint.name
+        rhs = l_constraint.rhs
+        if rhs == 0 and l_constraint.linear.coefficients.nnz == 0:
+            continue
+        linear_expr = 0
+        for j, v in l_constraint.linear.to_dict().items():
+            linear_expr += v * var[cast(int, j)]
+        sense = l_constraint.sense
+        if sense == Constraint.Sense.EQ:
+            mdl.addConstr(linear_expr == rhs, name=name)
+        elif sense == Constraint.Sense.GE:
+            mdl.addConstr(linear_expr >= rhs, name=name)
+        elif sense == Constraint.Sense.LE:
+            mdl.addConstr(linear_expr <= rhs, name=name)
+        else:
+            # should never happen
+            raise QiskitOptimizationError("Unsupported constraint sense: {}".format(sense))
+
+    # add quadratic constraints
+    for i, q_constraint in enumerate(quadratic_program.quadratic_constraints):
+        name = q_constraint.name
+        rhs = q_constraint.rhs
+        if (
+            rhs == 0
+            and q_constraint.linear.coefficients.nnz == 0
+            and q_constraint.quadratic.coefficients.nnz == 0
+        ):
+            continue
+        quadratic_expr = 0
+        for j, v in q_constraint.linear.to_dict().items():
+            quadratic_expr += v * var[cast(int, j)]
+        for (j, k), v in q_constraint.quadratic.to_dict().items():
+            quadratic_expr += v * var[cast(int, j)] * var[cast(int, k)]
+        sense = q_constraint.sense
+        if sense == Constraint.Sense.EQ:
+            mdl.addConstr(quadratic_expr == rhs, name=name)
+        elif sense == Constraint.Sense.GE:
+            mdl.addConstr(quadratic_expr >= rhs, name=name)
+        elif sense == Constraint.Sense.LE:
+            mdl.addConstr(quadratic_expr <= rhs, name=name)
+        else:
+            # should never happen
+            raise QiskitOptimizationError("Unsupported constraint sense: {}".format(sense))
+
+    return mdl
+
+
+def from_gurobipy(model: Model) -> "QuadraticProgram":
+    """Translate a gurobipy model into a quadratic program.
+
+    Note that this supports only basic functions of gurobipy as follows:
+    - quadratic objective function
+    - linear / quadratic constraints
+    - binary / integer / continuous variables
+
+    Args:
+        model: The gurobipy model to be loaded.
+
+    Returns:
+        The quadratic program corresponding to the model.
+
+    Raises:
+        QiskitOptimizationError: if the model contains unsupported elements.
+        MissingOptionalLibraryError: if gurobipy is not installed.
+    """
+
+    _check_gurobipy_is_installed("from_gurobipy")
+
+    if not isinstance(model, Model):
+        raise QiskitOptimizationError(f"The model is not compatible: {model}")
+
+    # pylint: disable=cyclic-import
+    from qiskit_optimization.problems.quadratic_program import QuadraticProgram
+
+    quadratic_program = QuadraticProgram()
+
+    # Update the model to make sure everything works as expected
+    model.update()
+
+    # get name
+    quadratic_program.name = model.ModelName
+
+    # get variables
+    # keep track of names separately, since gurobipy allows to have None names.
+    var_names = {}
+    for x in model.getVars():
+        if x.vtype == gp.GRB.CONTINUOUS:
+            x_new = quadratic_program.continuous_var(x.lb, x.ub, x.VarName)
+        elif x.vtype == gp.GRB.BINARY:
+            x_new = quadratic_program.binary_var(x.VarName)
+        elif x.vtype == gp.GRB.INTEGER:
+            x_new = quadratic_program.integer_var(x.lb, x.ub, x.VarName)
+        else:
+            raise QiskitOptimizationError(
+                "Unsupported variable type: {} {}".format(x.VarName, x.vtype)
+            )
+        var_names[x] = x_new.name
+
+    # objective sense
+    minimize = model.ModelSense == gp.GRB.MINIMIZE
+
+    # Retrieve the objective
+    objective = model.getObjective()
+    has_quadratic_objective = False
+
+    # Retrieve the linear part in case it is a quadratic objective
+    if isinstance(objective, gp.QuadExpr):
+        linear_part = objective.getLinExpr()
+        has_quadratic_objective = True
+    else:
+        linear_part = objective
+
+    # Get the constant
+    constant = linear_part.getConstant()
+
+    # get linear part of objective
+    linear = {}
+    for i in range(linear_part.size()):
+        linear[var_names[linear_part.getVar(i)]] = linear_part.getCoeff(i)
+
+    # get quadratic part of objective
+    quadratic = {}
+    if has_quadratic_objective:
+        for i in range(objective.size()):
+            x = var_names[objective.getVar1(i)]
+            y = var_names[objective.getVar2(i)]
+            v = objective.getCoeff(i)
+            quadratic[x, y] = v
+
+    # set objective
+    if minimize:
+        quadratic_program.minimize(constant, linear, quadratic)
+    else:
+        quadratic_program.maximize(constant, linear, quadratic)
+
+    # check whether there are any general constraints
+    if model.NumSOS > 0 or model.NumGenConstrs > 0:
+        raise QiskitOptimizationError("Unsupported constraint: SOS or General Constraint")
+
+    # get linear constraints
+    for constraint in model.getConstrs():
+        name = constraint.ConstrName
+        sense = constraint.Sense
+
+        left_expr = model.getRow(constraint)
+        rhs = constraint.RHS
+
+        lhs = {}
+        for i in range(left_expr.size()):
+            lhs[var_names[left_expr.getVar(i)]] = left_expr.getCoeff(i)
+
+        if sense == gp.GRB.EQUAL:
+            quadratic_program.linear_constraint(lhs, "==", rhs, name)
+        elif sense == gp.GRB.GREATER_EQUAL:
+            quadratic_program.linear_constraint(lhs, ">=", rhs, name)
+        elif sense == gp.GRB.LESS_EQUAL:
+            quadratic_program.linear_constraint(lhs, "<=", rhs, name)
+        else:
+            raise QiskitOptimizationError("Unsupported constraint sense: {}".format(constraint))
+
+    # get quadratic constraints
+    for constraint in model.getQConstrs():
+        name = constraint.QCName
+        sense = constraint.QCSense
+
+        left_expr = model.getQCRow(constraint)
+        rhs = constraint.QCRHS
+
+        linear = {}
+        quadratic = {}
+
+        linear_part = left_expr.getLinExpr()
+        for i in range(linear_part.size()):
+            linear[var_names[linear_part.getVar(i)]] = linear_part.getCoeff(i)
+
+        for i in range(left_expr.size()):
+            x = var_names[left_expr.getVar1(i)]
+            y = var_names[left_expr.getVar2(i)]
+            v = left_expr.getCoeff(i)
+            quadratic[x, y] = v
+
+        if sense == gp.GRB.EQUAL:
+            quadratic_program.quadratic_constraint(linear, quadratic, "==", rhs, name)
+        elif sense == gp.GRB.GREATER_EQUAL:
+            quadratic_program.quadratic_constraint(linear, quadratic, ">=", rhs, name)
+        elif sense == gp.GRB.LESS_EQUAL:
+            quadratic_program.quadratic_constraint(linear, quadratic, "<=", rhs, name)
+        else:
+            raise QiskitOptimizationError("Unsupported constraint sense: {}".format(constraint))
+
+    return quadratic_program
diff --git a/releasenotes/notes/add-model-translators-11e7f3b53574afa7.yaml b/releasenotes/notes/add-model-translators-11e7f3b53574afa7.yaml
new file mode 100644
index 000000000..a666fb10e
--- /dev/null
+++ b/releasenotes/notes/add-model-translators-11e7f3b53574afa7.yaml
@@ -0,0 +1,18 @@
+---
+features:
+  - |
+    Adds model translators between modeling libraries (e.g., docplex and gurobipy) and ``QuadraticProgram``.
+    Adds translator functions :meth:`~qiskit_optimization.translators.from_docplex_mp`,
+    :meth:`~qiskit_optimization.translators.to_docplex_mp`,
+    :meth:`~qiskit_optimization.translators.from_gurobipy`, and
+    :meth:`~qiskit_optimization.translators.to_gurobipy`
+    to :mod:`~qiskit_optimization.translators`.
+upgrade:
+  - |
+    :meth:`~qiskit_optimization.problems.QuadraticProgram.pprint_as_string` and
+    :meth:`~qiskit_optimization.problems.QuadraticProgram.prettyprint` have been removed,
+    which were deprecated in Qiskit Aqua 0.8.0 release (October 2020).
+deprecations:
+  - |
+    ``QuadraticProgram.from_docplex``, ``QuadraticProgram.to_docplex`` are deprecated because
+    ``from_docplex_mp`` and ``to_docplex_mp`` cover the features.
diff --git a/test/algorithms/test_admm.py b/test/algorithms/test_admm.py
index 774484c55..5a8cf6c57 100644
--- a/test/algorithms/test_admm.py
+++ b/test/algorithms/test_admm.py
@@ -23,6 +23,7 @@
     ADMMState,
 )
 from qiskit_optimization.problems import QuadraticProgram
+from qiskit_optimization.translators import from_docplex_mp
 
 
 class TestADMMOptimizer(QiskitOptimizationTestCase):
@@ -34,8 +35,7 @@ def test_admm_maximization(self):
         c = mdl.continuous_var(lb=0, ub=10, name="c")
         x = mdl.binary_var(name="x")
         mdl.maximize(c + x * x)
-        op = QuadraticProgram()
-        op.from_docplex(mdl)
+        op = from_docplex_mp(mdl)
 
         admm_params = ADMMParameters()
 
@@ -75,8 +75,7 @@ def test_admm_ex4(self):
         mdl.add_constraint(2 * v + 10 * w + t <= 3, "cons1")
         mdl.add_constraint(v + w + t >= b, "cons2")
 
-        op = QuadraticProgram()
-        op.from_docplex(mdl)
+        op = from_docplex_mp(mdl)
 
         admm_params = ADMMParameters(
             rho_initial=1001, beta=1000, factor_c=900, maxiter=100, three_block=False
@@ -112,8 +111,7 @@ def test_admm_ex4_no_bin_var_in_objective(self):
         mdl.add_constraint(2 * v + 10 * w + t <= 3, "cons1")
         mdl.add_constraint(v + w + t >= b, "cons2")
 
-        op = QuadraticProgram()
-        op.from_docplex(mdl)
+        op = from_docplex_mp(mdl)
 
         admm_params = ADMMParameters(
             rho_initial=1001, beta=1000, factor_c=900, maxiter=100, three_block=False
@@ -148,8 +146,7 @@ def test_admm_ex5(self):
         mdl.add_constraint(v + w + t >= 1, "cons2")
         mdl.add_constraint(v + w == 1, "cons3")
 
-        op = QuadraticProgram()
-        op.from_docplex(mdl)
+        op = from_docplex_mp(mdl)
 
         admm_params = ADMMParameters(
             rho_initial=1001, beta=1000, factor_c=900, maxiter=100, three_block=False
@@ -181,8 +178,7 @@ def test_admm_ex5_warm_start(self):
         mdl.add_constraint(v + w + t >= 1, "cons2")
         mdl.add_constraint(v + w == 1, "cons3")
 
-        op = QuadraticProgram()
-        op.from_docplex(mdl)
+        op = from_docplex_mp(mdl)
 
         admm_params = ADMMParameters(
             rho_initial=1001,
@@ -224,8 +220,7 @@ def test_admm_ex6(self):
         mdl.add_constraint(v + w + t >= 1, "cons2")
         mdl.add_constraint(v + w == 1, "cons3")
 
-        op = QuadraticProgram()
-        op.from_docplex(mdl)
+        op = from_docplex_mp(mdl)
 
         admm_params = ADMMParameters(
             rho_initial=1001,
@@ -264,8 +259,7 @@ def test_admm_ex6_max(self):
         mdl.add_constraint(v + w + t >= 1, "cons2")
         mdl.add_constraint(v + w == 1, "cons3")
 
-        op = QuadraticProgram()
-        op.from_docplex(mdl)
+        op = from_docplex_mp(mdl)
 
         admm_params = ADMMParameters(
             rho_initial=1001,
@@ -301,8 +295,7 @@ def test_equality_constraints_with_continuous_variables(self):
         mdl.add_constraint(2 * v + w >= 2, "cons1")
         mdl.add_constraint(w + t == 1, "cons2")
 
-        op = QuadraticProgram()
-        op.from_docplex(mdl)
+        op = from_docplex_mp(mdl)
 
         admm_params = ADMMParameters(
             rho_initial=1001,
@@ -335,8 +328,7 @@ def test_quad_constraints(self):
         mdl.add_constraint(v + w >= 1, "cons2")
         mdl.add_constraint(v ** 2 + w ** 2 <= 1, "cons2")
 
-        op = QuadraticProgram()
-        op.from_docplex(mdl)
+        op = from_docplex_mp(mdl)
 
         admm_params = ADMMParameters(
             rho_initial=1001,
@@ -378,8 +370,7 @@ def test_integer_variables(self):
         w = mdl.continuous_var(name="w", lb=0.0)
 
         mdl.minimize(v + w)
-        op = QuadraticProgram()
-        op.from_docplex(mdl)
+        op = from_docplex_mp(mdl)
 
         solver = ADMMOptimizer()
         solution = solver.solve(op)
diff --git a/test/algorithms/test_grover_optimizer.py b/test/algorithms/test_grover_optimizer.py
index 893920dc5..792973dd1 100644
--- a/test/algorithms/test_grover_optimizer.py
+++ b/test/algorithms/test_grover_optimizer.py
@@ -34,6 +34,7 @@
     QuadraticProgramToQubo,
 )
 from qiskit_optimization.problems import QuadraticProgram
+from qiskit_optimization.translators import from_docplex_mp
 
 
 @ddt
@@ -74,8 +75,7 @@ def test_qubo_gas_int_zero(self):
         x_0 = model.binary_var(name="x0")
         x_1 = model.binary_var(name="x1")
         model.minimize(0 * x_0 + 0 * x_1)
-        op = QuadraticProgram()
-        op.from_docplex(model)
+        op = from_docplex_mp(model)
 
         # Will not find a negative, should return 0.
         gmf = GroverOptimizer(1, num_iterations=1, quantum_instance=self.sv_simulator)
@@ -92,8 +92,7 @@ def test_qubo_gas_int_simple(self):
         x_0 = model.binary_var(name="x0")
         x_1 = model.binary_var(name="x1")
         model.minimize(-x_0 + 2 * x_1)
-        op = QuadraticProgram()
-        op.from_docplex(model)
+        op = from_docplex_mp(model)
 
         # Get the optimum key and value.
         gmf = GroverOptimizer(4, num_iterations=self.n_iter, quantum_instance=self.sv_simulator)
@@ -112,8 +111,7 @@ def test_qubo_gas_int_simple_maximize(self):
         x_0 = model.binary_var(name="x0")
         x_1 = model.binary_var(name="x1")
         model.maximize(-x_0 + 2 * x_1)
-        op = QuadraticProgram()
-        op.from_docplex(model)
+        op = from_docplex_mp(model)
 
         # Get the optimum key and value.
         gmf = GroverOptimizer(4, num_iterations=self.n_iter, quantum_instance=self.sv_simulator)
@@ -133,8 +131,7 @@ def test_qubo_gas_int_paper_example(self, simulator):
         x_1 = model.binary_var(name="x1")
         x_2 = model.binary_var(name="x2")
         model.minimize(-x_0 + 2 * x_1 - 3 * x_2 - 2 * x_0 * x_2 - 1 * x_1 * x_2)
-        op = QuadraticProgram()
-        op.from_docplex(model)
+        op = from_docplex_mp(model)
 
         # Get the optimum key and value.
         q_instance = self.sv_simulator if simulator == "sv" else self.qasm_simulator
@@ -150,8 +147,7 @@ def test_converter_list(self):
         x_0 = model.binary_var(name="x0")
         x_1 = model.binary_var(name="x1")
         model.maximize(-x_0 + 2 * x_1)
-        op = QuadraticProgram()
-        op.from_docplex(model)
+        op = from_docplex_mp(model)
 
         # Get the optimum key and value.
         # a single converter.
@@ -231,8 +227,7 @@ def test_bit_ordering(self, simulator):
         x = mdl.binary_var("x")
         y = mdl.binary_var("y")
         mdl.minimize(x - 2 * y)
-        op = QuadraticProgram()
-        op.from_docplex(mdl)
+        op = from_docplex_mp(mdl)
         opt_sol = -2
         success = OptimizationResultStatus.SUCCESS
         grover_optimizer = GroverOptimizer(
diff --git a/test/algorithms/test_gurobi_optimizer.py b/test/algorithms/test_gurobi_optimizer.py
index 6a3e756f9..e562a1900 100644
--- a/test/algorithms/test_gurobi_optimizer.py
+++ b/test/algorithms/test_gurobi_optimizer.py
@@ -14,7 +14,9 @@
 
 import unittest
 from test.optimization_test_case import QiskitOptimizationTestCase, requires_extra_library
-from ddt import ddt, data
+
+from ddt import data, ddt
+
 from qiskit_optimization.algorithms import GurobiOptimizer
 from qiskit_optimization.problems import QuadraticProgram
 
diff --git a/test/algorithms/test_min_eigen_optimizer.py b/test/algorithms/test_min_eigen_optimizer.py
index 885ee9804..c91afaa18 100644
--- a/test/algorithms/test_min_eigen_optimizer.py
+++ b/test/algorithms/test_min_eigen_optimizer.py
@@ -39,6 +39,7 @@
     QuadraticProgramToQubo,
 )
 from qiskit_optimization.problems import QuadraticProgram
+from qiskit_optimization.translators import from_docplex_mp
 
 
 @ddt
@@ -87,8 +88,7 @@ def setUp(self):
         x = mdl.binary_var("x")
         y = mdl.binary_var("y")
         mdl.minimize(x - 2 * y)
-        self.op_ordering = QuadraticProgram()
-        self.op_ordering.from_docplex(mdl)
+        self.op_ordering = from_docplex_mp(mdl)
 
     @data(
         ("exact", None, "op_ip1.lp"),
diff --git a/test/algorithms/test_warm_start_qaoa.py b/test/algorithms/test_warm_start_qaoa.py
index 78b336117..417b66f77 100644
--- a/test/algorithms/test_warm_start_qaoa.py
+++ b/test/algorithms/test_warm_start_qaoa.py
@@ -29,6 +29,7 @@
     WarmStartQAOAOptimizer,
 )
 from qiskit_optimization.applications.max_cut import Maxcut
+from qiskit_optimization.translators import from_docplex_mp
 
 
 class TestWarmStartQAOAOptimizer(QiskitOptimizationTestCase):
@@ -80,8 +81,7 @@ def test_constrained_binary(self):
         model.add_constraint(2 * v + 10 * w + t <= 3, "cons1")
         model.add_constraint(v + w + t >= 2, "cons2")
 
-        problem = QuadraticProgram()
-        problem.from_docplex(model)
+        problem = from_docplex_mp(model)
 
         backend = BasicAer.get_backend("statevector_simulator")
         qaoa = QAOA(quantum_instance=backend, reps=1)
@@ -109,8 +109,7 @@ def test_simple_qubo(self):
         v = model.binary_var(name="v")
 
         model.minimize((u - v + 2) ** 2)
-        problem = QuadraticProgram()
-        problem.from_docplex(model)
+        problem = from_docplex_mp(model)
 
         backend = BasicAer.get_backend("statevector_simulator")
         qaoa = QAOA(quantum_instance=backend, reps=1)
diff --git a/test/converters/test_converters.py b/test/converters/test_converters.py
index 48c5089e9..727c1a0a9 100644
--- a/test/converters/test_converters.py
+++ b/test/converters/test_converters.py
@@ -37,6 +37,7 @@
     MaximizeToMinimize,
 )
 from qiskit_optimization.problems import Constraint, Variable
+from qiskit_optimization.translators import from_docplex_mp
 
 logger = logging.getLogger(__name__)
 
@@ -480,8 +481,7 @@ def test_continuous_variable_decode(self):
         c = mdl.continuous_var(lb=0, ub=10.9, name="c")
         x = mdl.binary_var(name="x")
         mdl.maximize(c + x * x)
-        op = QuadraticProgram()
-        op.from_docplex(mdl)
+        op = from_docplex_mp(mdl)
         converter = IntegerToBinary()
         op = converter.convert(op)
         admm_params = ADMMParameters()
diff --git a/test/problems/test_quadratic_program.py b/test/problems/test_quadratic_program.py
index c86bed3ee..a46f6347a 100644
--- a/test/problems/test_quadratic_program.py
+++ b/test/problems/test_quadratic_program.py
@@ -14,6 +14,7 @@
 
 import tempfile
 import unittest
+import warnings
 from os import path
 from test.optimization_test_case import QiskitOptimizationTestCase, requires_extra_library
 
@@ -881,7 +882,10 @@ def test_write_to_lp_file(self):
             q_p.write_to_lp_file("")
 
     def test_docplex(self):
-        """test from_docplex and to_docplex"""
+        """DEPRECATED test from_docplex and to_docplex
+
+        This test will be removed when `from_docplex` and `to_docplex` are removed.
+        """
         q_p = QuadraticProgram("test")
         q_p.binary_var(name="x")
         q_p.integer_var(name="y", lowerbound=-2, upperbound=4)
@@ -894,7 +898,9 @@ def test_docplex(self):
         q_p.linear_constraint({"x": 2, "z": -1}, "==", 1)
         q_p.quadratic_constraint({"x": 2, "z": -1}, {("y", "z"): 3}, "==", 1)
         q_p2 = QuadraticProgram()
-        q_p2.from_docplex(q_p.to_docplex())
+        with warnings.catch_warnings():
+            warnings.simplefilter("ignore")
+            q_p2.from_docplex(q_p.to_docplex())
         self.assertEqual(q_p.export_as_lp_string(), q_p2.export_as_lp_string())
 
         mod = Model("test")
@@ -909,34 +915,44 @@ def test_docplex(self):
         with self.assertRaises(QiskitOptimizationError):
             mod = Model()
             mod.semiinteger_var(lb=1, name="x")
-            q_p.from_docplex(mod)
+            with warnings.catch_warnings():
+                warnings.simplefilter("ignore")
+                q_p.from_docplex(mod)
 
         with self.assertRaises(QiskitOptimizationError):
             mod = Model()
             x = mod.binary_var("x")
             mod.add_range(0, 2 * x, 1)
-            q_p.from_docplex(mod)
+            with warnings.catch_warnings():
+                warnings.simplefilter("ignore")
+                q_p.from_docplex(mod)
 
         with self.assertRaises(QiskitOptimizationError):
             mod = Model()
             x = mod.binary_var("x")
             y = mod.binary_var("y")
             mod.add_indicator(x, x + y <= 1, 1)
-            q_p.from_docplex(mod)
+            with warnings.catch_warnings():
+                warnings.simplefilter("ignore")
+                q_p.from_docplex(mod)
 
         with self.assertRaises(QiskitOptimizationError):
             mod = Model()
             x = mod.binary_var("x")
             y = mod.binary_var("y")
             mod.add_equivalence(x, x + y <= 1, 1)
-            q_p.from_docplex(mod)
+            with warnings.catch_warnings():
+                warnings.simplefilter("ignore")
+                q_p.from_docplex(mod)
 
         with self.assertRaises(QiskitOptimizationError):
             mod = Model()
             x = mod.binary_var("x")
             y = mod.binary_var("y")
             mod.add(mod.not_equal_constraint(x, y + 1))
-            q_p.from_docplex(mod)
+            with warnings.catch_warnings():
+                warnings.simplefilter("ignore")
+                q_p.from_docplex(mod)
 
         # test from_docplex without explicit variable names
         mod = Model()
@@ -951,7 +967,9 @@ def test_docplex(self):
         mod.add_constraint(x * y >= z)  # quadratic GE
         mod.add_constraint(x * y <= z)  # quadratic LE
         q_p = QuadraticProgram()
-        q_p.from_docplex(mod)
+        with warnings.catch_warnings():
+            warnings.simplefilter("ignore")
+            q_p.from_docplex(mod)
         var_names = [v.name for v in q_p.variables]
         self.assertListEqual(var_names, ["x0", "x1", "x2"])
         senses = [Constraint.Sense.EQ, Constraint.Sense.GE, Constraint.Sense.LE]
@@ -965,76 +983,6 @@ def test_docplex(self):
             self.assertDictEqual(c.quadratic.to_dict(use_name=True), {("x0", "x1"): 1})
             self.assertEqual(c.sense, senses[i])
 
-    def test_gurobipy(self):
-        """test from_gurobipy and to_gurobipy"""
-        try:
-            import gurobipy as gp
-        except ImportError as ex:
-            self.skipTest("gurobipy not installed: {}".format(str(ex)))
-            return
-        q_p = QuadraticProgram("test")
-        q_p.binary_var(name="x")
-        q_p.integer_var(name="y", lowerbound=-2, upperbound=4)
-        q_p.continuous_var(name="z", lowerbound=-1.5, upperbound=3.2)
-        q_p.minimize(constant=1, linear={"x": 1, "y": 2}, quadratic={("x", "y"): -1, ("z", "z"): 2})
-        q_p.linear_constraint({"x": 2, "z": -1}, "==", 1)
-        q_p.quadratic_constraint({"x": 2, "z": -1}, {("y", "z"): 3}, "==", 1)
-        q_p2 = QuadraticProgram()
-        q_p2.from_gurobipy(q_p.to_gurobipy())
-        self.assertEqual(q_p.export_as_lp_string(), q_p2.export_as_lp_string())
-
-        mod = gp.Model("test")
-        x = mod.addVar(vtype=gp.GRB.BINARY, name="x")
-        y = mod.addVar(vtype=gp.GRB.INTEGER, lb=-2, ub=4, name="y")
-        z = mod.addVar(vtype=gp.GRB.CONTINUOUS, lb=-1.5, ub=3.2, name="z")
-        mod.setObjective(1 + x + 2 * y - x * y + 2 * z * z)
-        mod.optimize()
-        mod.addConstr(2 * x - z == 1, name="c0")
-        mod.addConstr(2 * x - z + 3 * y * z == 1, name="q0")
-
-        # Here I am unsure what to do, let's come back to it later
-        # self.assertEqual(q_p.export_as_lp_string(), mod.export_as_lp_string())
-
-        with self.assertRaises(QiskitOptimizationError):
-            mod = gp.Model()
-            mod.addVar(vtype=gp.GRB.SEMIINT, lb=1, name="x")
-            q_p.from_gurobipy(mod)
-
-        with self.assertRaises(QiskitOptimizationError):
-            mod = gp.Model()
-            x = mod.addVar(vtype=gp.GRB.BINARY, name="x")
-            y = mod.addVar(vtype=gp.GRB.BINARY, name="y")
-            mod.addConstr((x == 1) >> (x + y <= 1))
-            q_p.from_gurobipy(mod)
-
-        # test from_gurobipy without explicit variable names
-        mod = gp.Model()
-        x = mod.addVar(vtype=gp.GRB.BINARY)
-        y = mod.addVar(vtype=gp.GRB.CONTINUOUS)
-        z = mod.addVar(vtype=gp.GRB.INTEGER)
-        mod.setObjective(x + y + z + x * y + y * z + x * z)
-        mod.optimize()
-        mod.addConstr(x + y == z)  # linear EQ
-        mod.addConstr(x + y >= z)  # linear GE
-        mod.addConstr(x + y <= z)  # linear LE
-        mod.addConstr(x * y == z)  # quadratic EQ
-        mod.addConstr(x * y >= z)  # quadratic GE
-        mod.addConstr(x * y <= z)  # quadratic LE
-        q_p = QuadraticProgram()
-        q_p.from_gurobipy(mod)
-        var_names = [v.name for v in q_p.variables]
-        self.assertListEqual(var_names, ["C0", "C1", "C2"])
-        senses = [Constraint.Sense.EQ, Constraint.Sense.GE, Constraint.Sense.LE]
-        for i, c in enumerate(q_p.linear_constraints):
-            self.assertDictEqual(c.linear.to_dict(use_name=True), {"C0": 1, "C1": 1, "C2": -1})
-            self.assertEqual(c.rhs, 0)
-            self.assertEqual(c.sense, senses[i])
-        for i, c in enumerate(q_p.quadratic_constraints):
-            self.assertEqual(c.rhs, 0)
-            self.assertDictEqual(c.linear.to_dict(use_name=True), {"C2": -1})
-            self.assertDictEqual(c.quadratic.to_dict(use_name=True), {("C0", "C1"): 1})
-            self.assertEqual(c.sense, senses[i])
-
     def test_substitute_variables(self):
         """test substitute variables"""
         q_p = QuadraticProgram("test")
@@ -1119,23 +1067,16 @@ def test_substitute_variables(self):
 
     def test_feasibility(self):
         """Tests feasibility methods."""
-        mod = Model("test")
-        # 0, 5
-        x = mod.continuous_var(
-            -1,
-            1,
-            "x",
-        )
-        y = mod.continuous_var(-10, 10, "y")
-        mod.minimize(x + y)
-        mod.add(x + y <= 10, "c0")
-        mod.add(x + y >= -10, "c1")
-        mod.add(x + y == 5, "c2")
-        mod.add(x * x + y <= 10, "c3")
-        mod.add(x * x + y >= 5, "c4")
-        mod.add(x * x + y * y == 25, "c5")
-        q_p = QuadraticProgram()
-        q_p.from_docplex(mod)
+        q_p = QuadraticProgram("test")
+        _ = q_p.continuous_var(-1, 1, "x")
+        _ = q_p.continuous_var(-10, 10, "y")
+        q_p.minimize(linear={"x": 1, "y": 1})
+        q_p.linear_constraint({"x": 1, "y": 1}, "<=", 10, "c0")
+        q_p.linear_constraint({"x": 1, "y": 1}, ">=", -10, "c1")
+        q_p.linear_constraint({"x": 1, "y": 1}, "==", 5, "c2")
+        q_p.quadratic_constraint({"y": 1}, {("x", "x"): 1}, "<=", 10, "c3")
+        q_p.quadratic_constraint({"y": 1}, {("x", "x"): 1}, ">=", 5, "c4")
+        q_p.quadratic_constraint(None, {("x", "x"): 1, ("y", "y"): 1}, "==", 25, "c5")
 
         self.assertTrue(q_p.is_feasible([0, 5]))
         self.assertFalse(q_p.is_feasible([1, 10]))
diff --git a/test/translators/__init__.py b/test/translators/__init__.py
new file mode 100755
index 000000000..6cf51fe8c
--- /dev/null
+++ b/test/translators/__init__.py
@@ -0,0 +1,13 @@
+# This code is part of Qiskit.
+#
+# (C) Copyright IBM 2021.
+#
+# This code is licensed under the Apache License, Version 2.0. You may
+# obtain a copy of this license in the LICENSE.txt file in the root directory
+# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
+#
+# Any modifications or derivative works of this code must retain this
+# copyright notice, and modified files need to carry a notice indicating
+# that they have been altered from the originals.
+
+""" Translators test packages """
diff --git a/test/translators/test_docplex_mp.py b/test/translators/test_docplex_mp.py
new file mode 100644
index 000000000..66fea9cdc
--- /dev/null
+++ b/test/translators/test_docplex_mp.py
@@ -0,0 +1,108 @@
+# This code is part of Qiskit.
+#
+# (C) Copyright IBM 2021.
+#
+# This code is licensed under the Apache License, Version 2.0. You may
+# obtain a copy of this license in the LICENSE.txt file in the root directory
+# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
+#
+# Any modifications or derivative works of this code must retain this
+# copyright notice, and modified files need to carry a notice indicating
+# that they have been altered from the originals.
+
+"""Test from_docplex_mp and to_docplex_mp"""
+
+from test.optimization_test_case import QiskitOptimizationTestCase
+
+from docplex.mp.model import Model
+
+from qiskit_optimization.exceptions import QiskitOptimizationError
+from qiskit_optimization.problems import Constraint, QuadraticProgram
+from qiskit_optimization.translators.docplex_mp import from_docplex_mp, to_docplex_mp
+
+
+class TestDocplexMpTranslator(QiskitOptimizationTestCase):
+    """Test from_docplex_mp and to_docplex_mp"""
+
+    def test_from_and_to(self):
+        """test from_docplex_mp and to_docplex_mp"""
+        q_p = QuadraticProgram("test")
+        q_p.binary_var(name="x")
+        q_p.integer_var(name="y", lowerbound=-2, upperbound=4)
+        q_p.continuous_var(name="z", lowerbound=-1.5, upperbound=3.2)
+        q_p.minimize(
+            constant=1,
+            linear={"x": 1, "y": 2},
+            quadratic={("x", "y"): -1, ("z", "z"): 2},
+        )
+        q_p.linear_constraint({"x": 2, "z": -1}, "==", 1)
+        q_p.quadratic_constraint({"x": 2, "z": -1}, {("y", "z"): 3}, "==", 1)
+        q_p2 = from_docplex_mp(to_docplex_mp(q_p))
+        self.assertEqual(q_p.export_as_lp_string(), q_p2.export_as_lp_string())
+
+        mod = Model("test")
+        x = mod.binary_var("x")
+        y = mod.integer_var(-2, 4, "y")
+        z = mod.continuous_var(-1.5, 3.2, "z")
+        mod.minimize(1 + x + 2 * y - x * y + 2 * z * z)
+        mod.add(2 * x - z == 1, "c0")
+        mod.add(2 * x - z + 3 * y * z == 1, "q0")
+        self.assertEqual(q_p.export_as_lp_string(), mod.export_as_lp_string())
+
+        with self.assertRaises(QiskitOptimizationError):
+            mod = Model()
+            mod.semiinteger_var(lb=1, name="x")
+            _ = from_docplex_mp(mod)
+
+        with self.assertRaises(QiskitOptimizationError):
+            mod = Model()
+            x = mod.binary_var("x")
+            mod.add_range(0, 2 * x, 1)
+            _ = from_docplex_mp(mod)
+
+        with self.assertRaises(QiskitOptimizationError):
+            mod = Model()
+            x = mod.binary_var("x")
+            y = mod.binary_var("y")
+            mod.add_indicator(x, x + y <= 1, 1)
+            _ = from_docplex_mp(mod)
+
+        with self.assertRaises(QiskitOptimizationError):
+            mod = Model()
+            x = mod.binary_var("x")
+            y = mod.binary_var("y")
+            mod.add_equivalence(x, x + y <= 1, 1)
+            _ = from_docplex_mp(mod)
+
+        with self.assertRaises(QiskitOptimizationError):
+            mod = Model()
+            x = mod.binary_var("x")
+            y = mod.binary_var("y")
+            mod.add(mod.not_equal_constraint(x, y + 1))
+            _ = from_docplex_mp(mod)
+
+        # test from_docplex without explicit variable names
+        mod = Model()
+        x = mod.binary_var()
+        y = mod.continuous_var()
+        z = mod.integer_var()
+        mod.minimize(x + y + z + x * y + y * z + x * z)
+        mod.add_constraint(x + y == z)  # linear EQ
+        mod.add_constraint(x + y >= z)  # linear GE
+        mod.add_constraint(x + y <= z)  # linear LE
+        mod.add_constraint(x * y == z)  # quadratic EQ
+        mod.add_constraint(x * y >= z)  # quadratic GE
+        mod.add_constraint(x * y <= z)  # quadratic LE
+        q_p = from_docplex_mp(mod)
+        var_names = [v.name for v in q_p.variables]
+        self.assertListEqual(var_names, ["x0", "x1", "x2"])
+        senses = [Constraint.Sense.EQ, Constraint.Sense.GE, Constraint.Sense.LE]
+        for i, c in enumerate(q_p.linear_constraints):
+            self.assertDictEqual(c.linear.to_dict(use_name=True), {"x0": 1, "x1": 1, "x2": -1})
+            self.assertEqual(c.rhs, 0)
+            self.assertEqual(c.sense, senses[i])
+        for i, c in enumerate(q_p.quadratic_constraints):
+            self.assertEqual(c.rhs, 0)
+            self.assertDictEqual(c.linear.to_dict(use_name=True), {"x2": -1})
+            self.assertDictEqual(c.quadratic.to_dict(use_name=True), {("x0", "x1"): 1})
+            self.assertEqual(c.sense, senses[i])
diff --git a/test/translators/test_gurobi.py b/test/translators/test_gurobi.py
new file mode 100644
index 000000000..915a1b359
--- /dev/null
+++ b/test/translators/test_gurobi.py
@@ -0,0 +1,89 @@
+# This code is part of Qiskit.
+#
+# (C) Copyright IBM 2021.
+#
+# This code is licensed under the Apache License, Version 2.0. You may
+# obtain a copy of this license in the LICENSE.txt file in the root directory
+# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
+#
+# Any modifications or derivative works of this code must retain this
+# copyright notice, and modified files need to carry a notice indicating
+# that they have been altered from the originals.
+
+"""Test from_gurobipy and to_gurobipy"""
+
+from test.optimization_test_case import QiskitOptimizationTestCase, requires_extra_library
+
+from qiskit_optimization.exceptions import QiskitOptimizationError
+from qiskit_optimization.problems import Constraint, QuadraticProgram
+from qiskit_optimization.translators.gurobipy import from_gurobipy, to_gurobipy
+
+
+class TestGurobiTranslator(QiskitOptimizationTestCase):
+    """Test from_gurobipy and to_gurobipy"""
+
+    @requires_extra_library
+    def test_from_and_to(self):
+        """test from_gurobipy and to_gurobipy"""
+        q_p = QuadraticProgram("test")
+        q_p.binary_var(name="x")
+        q_p.integer_var(name="y", lowerbound=-2, upperbound=4)
+        q_p.continuous_var(name="z", lowerbound=-1.5, upperbound=3.2)
+        q_p.minimize(constant=1, linear={"x": 1, "y": 2}, quadratic={("x", "y"): -1, ("z", "z"): 2})
+        q_p.linear_constraint({"x": 2, "z": -1}, "==", 1)
+        q_p.quadratic_constraint({"x": 2, "z": -1}, {("y", "z"): 3}, "==", 1)
+        q_p2 = from_gurobipy(to_gurobipy(q_p))
+        self.assertEqual(q_p.export_as_lp_string(), q_p2.export_as_lp_string())
+
+        import gurobipy as gp
+
+        mod = gp.Model("test")
+        x = mod.addVar(vtype=gp.GRB.BINARY, name="x")
+        y = mod.addVar(vtype=gp.GRB.INTEGER, lb=-2, ub=4, name="y")
+        z = mod.addVar(vtype=gp.GRB.CONTINUOUS, lb=-1.5, ub=3.2, name="z")
+        mod.setObjective(1 + x + 2 * y - x * y + 2 * z * z)
+        mod.optimize()
+        mod.addConstr(2 * x - z == 1, name="c0")
+        mod.addConstr(2 * x - z + 3 * y * z == 1, name="q0")
+
+        # Here I am unsure what to do, let's come back to it later
+        # self.assertEqual(q_p.export_as_lp_string(), mod.export_as_lp_string())
+
+        with self.assertRaises(QiskitOptimizationError):
+            mod = gp.Model()
+            mod.addVar(vtype=gp.GRB.SEMIINT, lb=1, name="x")
+            _ = from_gurobipy(mod)
+
+        with self.assertRaises(QiskitOptimizationError):
+            mod = gp.Model()
+            x = mod.addVar(vtype=gp.GRB.BINARY, name="x")
+            y = mod.addVar(vtype=gp.GRB.BINARY, name="y")
+            mod.addConstr((x == 1) >> (x + y <= 1))
+            _ = from_gurobipy(mod)
+
+        # test from_gurobipy without explicit variable names
+        mod = gp.Model()
+        x = mod.addVar(vtype=gp.GRB.BINARY)
+        y = mod.addVar(vtype=gp.GRB.CONTINUOUS)
+        z = mod.addVar(vtype=gp.GRB.INTEGER)
+        mod.setObjective(x + y + z + x * y + y * z + x * z)
+        mod.optimize()
+        mod.addConstr(x + y == z)  # linear EQ
+        mod.addConstr(x + y >= z)  # linear GE
+        mod.addConstr(x + y <= z)  # linear LE
+        mod.addConstr(x * y == z)  # quadratic EQ
+        mod.addConstr(x * y >= z)  # quadratic GE
+        mod.addConstr(x * y <= z)  # quadratic LE
+        q_p = from_gurobipy(mod)
+        var_names = [v.name for v in q_p.variables]
+        self.assertListEqual(var_names, ["C0", "C1", "C2"])
+        senses = [Constraint.Sense.EQ, Constraint.Sense.GE, Constraint.Sense.LE]
+        for i, c in enumerate(q_p.linear_constraints):
+            self.assertDictEqual(c.linear.to_dict(use_name=True), {"C0": 1, "C1": 1, "C2": -1})
+            self.assertEqual(c.rhs, 0)
+            self.assertEqual(c.sense, senses[i])
+        for i, c in enumerate(q_p.quadratic_constraints):
+            self.assertEqual(c.rhs, 0)
+            self.assertDictEqual(c.linear.to_dict(use_name=True), {"C2": -1})
+            self.assertDictEqual(c.quadratic.to_dict(use_name=True), {("C0", "C1"): 1})
+            self.assertEqual(c.sense, senses[i])