rsohlot · November 8, 2019 09:54 · Aug 16, 2019
diff --git a/quantum_gate_and_noise.ipynb b/quantum_gate_and_noise.ipynb
@@ -0,0 +1,143 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from qiskit import Aer, IBMQ, execute\n",
+    "from qiskit.providers.aer import noise\n",
+    "from qiskit import QuantumCircuit, QuantumRegister, ClassicalRegister\n",
+    "from qiskit.tools.visualization import plot_histogram\n",
+    "from qiskit.tools.monitor import job_monitor\n",
+    "\n",
+    "provider = IBMQ.load_account()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "backend = Aer.get_backend('qasm_simulator')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "device = provider.get_backend('ibmq_16_melbourne')\n",
+    "properties = device.properties()\n",
+    "coupling_map = device.configuration().coupling_map\n",
+    "\n",
+    "gate_times = [\n",
+    "    ('u1', None, 0), ('u2', None, 100), ('u3', None, 200)\n",
+    "]\n",
+    "\n",
+    "noise_model = noise.device.basic_device_noise_model(properties, gate_times=gate_times)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 361.2x138.46 with 1 Axes>"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "q = QuantumRegister(1)\n",
+    "c = ClassicalRegister(1)\n",
+    "qc = QuantumCircuit(q, c)\n",
+    "qc.iden(q[0])\n",
+    "qc.measure(q, c)\n",
+    "\n",
+    "qc.draw(output='mpl')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAdAAAAE1CAYAAABX1LExAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAGXlJREFUeJzt3XuUXWWZ5/HvY2IWyXQTSaiYFBpJ4aVLYIpcxDYKwYVxuDgsQZag6BCRYQIjjqIijlecaVmdQQijIkJ3C4pOFMHR0WSAEAdooJVctIPB2BFIRpJwSWI7BuhK4jN/7FNwKE6lznmtW1Lfz1pn1d7vfvfez/6nfmvf3h2ZiSRJas2LhrsASZL2RQaoJEkFDFBJkgoYoJIkFTBAJUkqYIBKklTAAJUkqYABKklSAQNUkqQCY4e7gOE0efLknD59+nCXIUkaQX7+858/mZlt/fUb1QE6ffp0VqxYMdxlSJJGkEmTJm1spp+XcCVJKmCASpJUwACVJKmAASpJUgEDVJKkAgaoJEkFDFBJkgoYoJIkFTBAJUkqYIBKklTAAJUkqYABKklSAQNUkqQCBqgkSQUMUEmSChigkiQVMEAlSSpggEqSVMAAlSSpgAEqSVIBA1SSpAIGqCRJBQxQSZIKGKDSfuYDH/gAr371q5k7d27D5ZnJJZdcwuzZs3nTm97EL37xi2eXLV++nKOPPprZs2ezePHiZ9t37NjBqaeeypw5czj11FP53e9+N+jHIY10Bqi0n3n3u9/NTTfd1Ofy5cuX85vf/IaVK1dy5ZVX8pGPfASAPXv2cPHFF/Pd736X++67j5tvvplf/epXACxevJh58+axcuVK5s2b97xwlUYrA1Taz8ydO5eDDjqoz+VLly7lzDPPJCJ43etex+9//3u2bt3KqlWrmDFjBoceeijjxo3jtNNOY9myZQAsW7aMM888E4AzzzyTpUuXDsmxSCOZASqNMlu2bOGQQw55dr69vZ0tW7b02Q7w+OOPM3XqVACmTp3KE088MbRFSyOQASqNMpn5graI6LNdUmMGqDTKtLe38+ijjz47v3nzZqZOndpnO8CUKVPYunUrAFu3bqWtrW1oi5ZGIANUGmVOPPFElixZQmZy//33c+CBBzJ16lRmzZrFQw89xMaNG+nu7uaWW27hhBNOAOCEE05gyZIlACxZsoQTTzxxOA9BGhHGDncBkgbWueeeyz333MO2bds4/PDDueSSS9i9ezcA73vf+5g/fz633347s2fPZvz48Xz5y18GYOzYsSxatIjTTz+dPXv2cNZZZ9HZ2QnAhz70Ic455xxuvPFGXvayl/H1r3992I5PGimi0X2P0WLmzJm5YsWK4S5DkjSCTJo0aVVmzumvn5dwJUkqYIBKklTAAJUkqYABKklSAQNUkqQCBqgkSQUMUEmSCgxpgEbEsRHxw4h4NCIyIhY0sc6REXFnRDxdW+8z0WuAzoiYFxGrIuKZiHgoIhYO2kFIksTQn4H+GfAA8J+Ap/vrHBEHArcDjwGvAz4IfAy4qK7PDGApcC8wE7gM+FJEvGOgi5ckqceQDuWXmUupwo6IuL6JVc4CJgBnZ+bTwAMR0QlcFBFXZDWM0kJgc2ZeWFvnwYh4PfBR4OaBPgZJkmDk3wN9A3B3LTx73Aq0A4fW9bmt13q3AnMi4sWDXqEkaVQa6YPJTwV+26vtsbplD9f+Lm/QZyxwMLClfkFEnAecBzBt2jRWr14NVJ94mjBhAhs2bABg4sSJdHR0sGbNGgDGjBlDV1cX69evZ+fOnQB0dnayfft2rlp+xAAcqiTpT/Hxt21g48aNALS1tdHW1sa6desAGD9+PJ2dnaxdu5Zdu3YB0NXVxaZNm9ixYwcAHR0ddHd3N72/kR6gAL1Hu48G7c30qRoyrwWuhWow+VmzZj1veX/zr3nNa543f8ghh/RVtyRpCE2ePJnJkyc/r633//AjjzzyefMzZsxgxowZRfsb6Zdwt1KdYdabUvv7WD99dgPbBq80SdJoNtID9D7gmIg4oK5tPrAZeKSuz1t6rTcfWJmZuwa9QknSqDTU74H+WUQcFRFH1fY9vTY/vbb8soi4o26VbwNPAddHxBERcRpwCdDzBC7ANcDLImJxRHRGxLnAAuDyoTouSdLoM9RnoHOANbXfeODS2vTna8unAYf1dM7Mf6Y6m2wHVgJfAb4IXFHX52HgJOBY4OfAJ4EPZqavsEiSBs1Qvwf6f3juAZ9Gyxc0aFtLFY572+6dwKy99ZEkaSCN9HugkiSNSAaoJEkFDFBJkgoYoJIkFTBAJUkqYIBKklTAAJUkqYABKklSAQNUkqQCBqgkSQUMUEmSChigkiQVMEAlSSpggEqSVMAAlSSpgAEqSVIBA1SSpAIGqCRJBQxQSZIKGKCSJBUwQCVJKmCASpJUwACVJKmAASpJUgEDVJKkAgaoJEkFDFBJkgoYoJIkFTBAJUkqYIBKklTAAJUkqYABKklSAQNUkqQCBqgkSQUMUEmSChigkiQVMEAlSSpggEqSVMAAlSSpgAEqSVIBA1SSpAIGqCRJBQxQSZIKGKCSJBUwQCVJKtBSgEbEOyPirXXzn4mI30bErRExbeDLkyRpZGr1DPRzPRMRMQv4z8B/B14MfLGZDUTEBRHxcEQ8ExGrIuKYvfT9XERkH78ptT7H9bH8L1o8NkmSmja2xf6vANbXpk8F/mdmLoqI24Bb+1s5Is4ArgIuAP6+9ndZRLw2Mzc1WOVy4JpebUuAzMzHe7UfDmyvm3+iv3okSSrV6hnoM8Cf16aPB5bXpv+5rn1vLgKuz8zrMvPBzLwQ2AKc36hzZv4hM7f2/KjOdI8BrmvQ/fH6vpm5p4XjkiSpJa0G6N3AFyPi08AcYGmt/dXA/93bihExDpgN3NZr0W3A3Cb3/37gd8DNDZatjIgtEXFHRLy5ye1JklSk1Uu4HwC+CpwOLMzMzbX2E+n/Eu7BwBjgsV7tjwFv6W/HEfEi4BzgG5n5L3WLes5g7wfGAe8F7oiI4zLzrgbbOQ84D2DatGmsXr0agPb2diZMmMCGDRsAmDhxIh0dHaxZswaAMWPG0NXVxfr169m5cycAnZ2dbN++HTiov/IlSYNs27ZtbNy4EYC2tjba2tpYt24dAOPHj6ezs5O1a9eya9cuALq6uti0aRM7duwAoKOjg+7u7qb3F5k5wIfQx44i2oFHgWMz8+669s8C78rMvT70ExEnAz8CjsjMX/bTdymwOzNP2Vu/mTNn5ooVK5o9hD5dfIMBKknDbdHZOwZkO5MmTVqVmXP669fye6ARcUBEnB4RH4+Il9TaDouISf2s+iSwB5jaq30KLzwrbeQ84N7+wrPmp8CrmugnSVKRVt8DfSXwK6onY/8K6AnN84FFe1s3M7uBVcD8XovmA/f2s9924GQaPzzUyFFUl3YlSRoUrd4DXUz10M/5VA/z9Pgh8PUm1r8C+GZE/Ay4B1gItFN7VSUiLgOOzszje613DrAT+G7vDUbEh4BHgF9S3QN9D/B24B3NHpQkSa1qNUDnAn+ZmXsior59E1UQ7lVmficiJgOfAqYBDwAnZebGWpdpwGH160S1o/cD38rMpxpsdhzV+6KHAE9TBenJmbm0QV9JkgZEqwEK1buYvU2nehe0X5l5NXB1H8sWNGhLYMZetreIfi4fS5I00Fp9iOg2qsEQemREHAhcCvx4wKqSJGmEa/UM9CLgJxGxHjgA+A7wSqqnaN85wLVJkjRitRSgmbk5Io4C3gXMojqDvZbq/uTTg1CfJEkjUsv3QGtB+Xe1nyRJo1K/ARoRpwH/KzN31ab7lJm3DFhlkiSNYM2cgX6PavSgx2vTfUmqsW4lSdrv9RugmfmiRtOSJI1mrQ7ld2xEvCB0I2JMRBw7cGVJkjSytXpG+ROeG/+23ktqyyRJGhVaDdCgutfZ22SqsWolSRoVmnqNJSJ+WJtM4MaIqP+g9RjgCPr5oookSfuTZt8D3Vb7G8AOqkHbe3QDf0/znxqTJGmf11SAZub7ACLiEeDyzPRyrSRpVGt1KL9LB6sQSZL2Jc2MRPSPwLzM3BERa2n8EBEAmfmvB7I4SZJGqmbOQG8Geh4a2ttIRJIkjRrNjER0aaNpSZJGM4fmkySpQDP3QPd637Oe90AlSaNFs19jkSRJdVq6BypJkireA5UkqYDvgUqSVMD3QCVJKuB7oJIkFWhpLNweEXEY0FmbfTAzfzNwJUmSNPK1FKARMRn4W+AU4I/PNcePgHMyc1ufK0uStB9p9SncvwFeCRwDHFD7HQvMwO+BSpJGkVYv4f4b4PjMvK+u7Z6I+A/A8oErS5Kkka3VM9AngEYf034K8PKtJGnUaDVAPw8sjohDehpq01+sLZMkaVQoGUx+BvBIRDxamz8EeAaYQnWPVJKk/Z6DyUuSVMDB5CVJKuBg8pIkFWgpQCNiXERcGhG/johnImJP/W+wipQkaaRp9Qz0vwBnUz11+0fgY8BXqF5huWBgS5MkaeRqNUDfCSzMzK8Be4AfZOYHgc8C8we6OEmSRqpWA/SlwLra9B+Al9Sm/zfw1oEqSpKkka7VAN0EtNemN1AN7QfwBuDpgSpKkqSRrtUA/T5wfG36KuDSiHgYuB4HUZAkjSItDSafmZ+om/5eRPwWmAv8OjN/NNDFSZI0UhV9ULtHZv4D8A8DVIskSfuMlgdSiIhZEfGNiFhZ+30zImYNRnGSJI1UrQ6kcBZwPzANWFr7vRT4WUS8Z+DLkyRpZGr1Eu5fAZ/OzC/UN0bEJ4D/Ctw4UIVJkjSStXoJtw34boP2m6g+Z9aviLggIh6uDQW4KiKO2UvfQyMiG/xO6NVvXm1bz0TEQxGxsKWjkiSpRa0G6E+A4xq0Hwfc2d/KEXEG1esvXwBmAvcCyyJiej+rnkB12bjnt6JumzOoLiXfW9vmZcCXIuId/dUjSVKpZj6ofVrd7DLgsoiYw3NP3/4lcBrwuSb2dxFwfWZeV5u/sHY2eT7wib5XY1tmbu1j2UJgc2ZeWJt/MCJeD3wUuLmJmiRJalnpB7XPq/3qfQm4uq+NRMQ4YDZwea9Ft1G9S7o3t0TEAcA/AVdmZn1Nb6hto96twNkR8eLM3NWrjmdrnzZtGqtXrwagvb2dCRMmsGHDBgAmTpxIR0cHa9asAWDMmDF0dXWxfv16du7cCUBnZyfbt28HDuqnfEnSYNu2bRsbN24EoK2tjba2Ntatq0afHT9+PJ2dnaxdu5Zdu6pY6OrqYtOmTezYsQOAjo4Ouru7m95fZOYAH0IfO4poBx4F5mXmXXXtnwHOyszXNFjnYKqvv9wD7AZOAT4JnJ2ZN9b6/Bq4MTM/X7fesVSXlNszc0tfNc2cOTNXrFjR1+KmXXyDASpJw23R2TsGZDuTJk1alZlz+uv3Jw2kUKh3YkeDtqpj5pNUn07rsbIWqhfz/Cd+G22zUbskSQOiZCCFkyPiroh4MiKeiIg7I+KkJlZ9kuoTaFN7tU8BHmuhhJ8Cr6qb39rHNndTfadUkqQB1+pACudSDSj/G+DjwCXAw8D3I+Kcva2bmd3AKl743dD5VE/QNusooP6y7H3AWxpsc2Xv+5+SJA2UVi/hfhy4KDO/XNf2txGxiipM/66f9a8AvhkRP6O6r7mQ6vNo1wBExGXA0Zl5fG3+bGAXsAb4I/Bvgf9Yq6PHNcAHImIx8DXgjcAC4F0tHpskSU1rNUCnU308u7dlvPDp2hfIzO9ExGTgU1Tvcz4AnJSZG2tdpgGH9VrtU8ArqC7//ho4p+cBoto2H65dQr6S6nWYzcAHM9NXWCRJg6bVAN1EdXl0Q6/2twIbX9j9hTLzavp43SUzF/SavwG4oYlt3gk4oL0kaci0GqCXU43yM4vqvmUCbwLeC1y4txUlSdqftPpB7a9FxOPAR6hGHwJ4EHhnZv5goIuTJGmkajpAI2Is1aXauzLz+4NXkiRJI1/Tr7Fk5m7gFuDPB68cSZL2Da0OpPAL4JWDUYgkSfuSVgP0c8AXI+LtEfHyiJhU/xuE+iRJGpFafQr3x7W/t/D8cWZ7xrMdMxBFSZI00rUaoG8elCokSdrHNBWgETEB+G/A24EXA8upRvt5chBrkyRpxGr2HuilVOPL/hj4H1SjEX11kGqSJGnEa/YS7mnA+zNzCUBEfAu4JyLGZOaeQatOkqQRqtkz0JcDd/fMZObPqL632T4YRUmSNNI1G6BjgO5ebbtp/SEkSZL2C80GYAA3RsS/1LUdAFwXEU/1NGTmKQNZnCRJI1WzAdrok2I3NmiTJGlUaCpAM/N9g12IJEn7klaH8pMkSRigkiQVMUAlSSpggEqSVMAAlSSpgAEqSVIBA1SSpAIGqCRJBQxQSZIKGKCSJBUwQCVJKmCASpJUwACVJKmAASpJUgEDVJKkAgaoJEkFDFBJkgoYoJIkFTBAJUkqYIBKklTAAJUkqYABKklSAQNUkqQCBqgkSQUMUEmSChigkiQVMEAlSSpggEqSVMAAlSSpwJAHaERcEBEPR8QzEbEqIo7ZS9/jIuIHEbElIp6KiH+MiHMa9MkGv78Y/KORJI1WQxqgEXEGcBXwBWAmcC+wLCKm97HKXGAtcDpwBPBV4NqIeHeDvocD0+p+/zSw1UuS9JyxQ7y/i4DrM/O62vyFEXECcD7wid6dM/MLvZq+GhFvBt4BfLvXsscz88mBLliSpEaG7Aw0IsYBs4Hbei26jepMs1kHAjsatK+sXeq9oxaykiQNmqE8Az0YGAM81qv9MeAtzWwgIt4GHA+8sa55C9UZ7P3AOOC9wB0RcVxm3tVgG+cB5wFMmzaN1atXA9De3s6ECRPYsGEDABMnTqSjo4M1a9YAMGbMGLq6uli/fj07d+4EoLOzk+3btwMHNVO+JGkQbdu2jY0bNwLQ1tZGW1sb69atA2D8+PF0dnaydu1adu3aBUBXVxebNm1ix47qnKyjo4Pu7u6m9xeZOcCH0MeOItqBR4FjM/PuuvbPAu/KzL0+9BMRbwSWAR/PzK/203cpsDszT9lbv5kzZ+aKFSuaPYQ+XXyDASpJw23R2Y0uTrZu0qRJqzJzTn/9hvIhoieBPcDUXu1TeOFZ6fNExJuowvMz/YVnzU+BV5UUKUlSM4YsQDOzG1gFzO+1aD7V07gNRcSxVOF5aWYubnJ3R1Fd2pUkaVAM9VO4VwDfjIifAfcAC4F24BqAiLgMODozj6/NHwf8GLga+FZE9Jy97snMJ2p9PgQ8AvyS6h7oe4C3Uz2pK0nSoBjSAM3M70TEZOBTVO9qPgCclJkba12mAYfVrbIAmAB8tPbrsRE4tDY9DrgcOAR4mipIT87MpYNzFJIkDf0ZKJl5NdUZZaNlCxrML2jUt67PImDRwFQnSVJzHAtXkqQCBqgkSQUMUEmSChigkiQVMEAlSSpggEqSVMAAlSSpgAEqSVIBA1SSpAIGqCRJBQxQSZIKGKCSJBUwQCVJKmCASpJUwACVJKmAASpJUgEDVJKkAgaoJEkFDFBJkgoYoJIkFTBAJUkqYIBKklTAAJUkqYABKklSAQNUkqQCBqgkSQUMUEmSChigkiQVMEAlSSpggEqSVMAAlSSpgAEqSVIBA1SSpAIGqCRJBQxQSZIKGKCSJBUwQCVJKmCASpJUwACVJKmAASpJUgEDVJKkAgaoJEkFDFBJkgoYoJIkFTBAJUkqYIBKklRgyAM0Ii6IiIcj4pmIWBURx/TT/8iIuDMino6IRyPiMxERvfrMq23rmYh4KCIWDu5RSJJGuyEN0Ig4A7gK+AIwE7gXWBYR0/vofyBwO/AY8Drgg8DHgIvq+swAlta2NRO4DPhSRLxj8I5EkjTaDfUZ6EXA9Zl5XWY+mJkXAluA8/vofxYwATg7Mx/IzJuBvwYuqjsLXQhszswLa9u8DrgB+OjgHookaTQbsgCNiHHAbOC2XotuA+b2sdobgLsz8+m6tluBduDQuj69t3krMCciXvyn1CxJUl/GDuG+DgbGUF2OrfcY8JY+1pkK/LZB/55lD9f+Lm/QZ2xtn1vqF0TEecB5tdk/TJo0aX2T9Uv7u4OBJ4e7CKnU33x4wDb1imY6DWWA9she89Ggrb/+vdub6VM1ZF4LXNtPjdKoExErM3POcNch7SuG8h7ok8AeqjPGelN44Vlpj6199Kdunb767Aa2FVUqSVI/hixAM7MbWAXM77VoPtUTtI3cBxwTEQf06r8ZeKSuT+9LwPOBlZm560+pWZKkvgz1U7hXAAsi4tyI6IyIq6geCLoGICIui4g76vp/G3gKuD4ijoiI04BLgCsys+fy7DXAyyJicW2b5wILgMuH6Jik/YW3NqQWxHM5NEQ7jLgAuBiYBjwAfDgz76otux44LjMPret/JPAV4GhgB1Vgfr4uQImIecCVwOFUZ6d/nZnXDMXxSJJGpyEPUEmS9geOhStJUgEDVJKkAgaoJEkFDFBpFIuIV0XES4e7Dmlf5ENE0igTEVOA9wIfBp6gGnRkC/A94ObM3DmM5Un7DANUGmVqr4u9FvgR1Whdk4GjgE6qsacXZebtw1agtI8wQKVRpPYZwP8HnFT3/nUALwdeD/x7qoG0z8jMnw9bodI+wHug0ujyWqqvGHX3NGRlU2beBLyNKmDPGKb6pH2GASqNLg8BjwNX1h4get7/gNqY1TcAJw5HcdK+xACVRpHax+k/CYwHvgH8u4h4eUT8K4CImADMoxpmU9JeeA9UGoUi4gjg08ApwE6qrxo9QfVloy3AuZm5dvgqlEY+A1QaxWqvtJwMvB14hurM86bM/NWwFibtAwxQSQBExIsy84/DXYe0rzBAJUkq4ENEkiQVMEAlSSpggEqSVMAAlSSpgAEqSVIBA1SSpAL/H6cNeT/2IDUSAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 504x360 with 1 Axes>"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "result = execute(qc, backend, shots=1000).result()\n",
+    "counts = result.get_counts(qc)\n",
+    "plot_histogram(counts)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 504x360 with 1 Axes>"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "result = execute(qc, backend,\n",
+    "                       noise_model=noise_model,\n",
+    "                       coupling_map=coupling_map,\n",
+    "                       basis_gates=noise_model.basis_gates,\n",
+    "                       shots=1000).result()\n",
+    "counts = result.get_counts(qc)\n",
+    "plot_histogram(counts)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}