Mailing list question.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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JJCcnB3ysatXggQfg9tud5X/+E66/HuLiShejMca4KSUlhZSUlFIdo1TdcUXk\nR1XtXKoISn6uRsAMVe1QyPagd8fNyHAeBty61Vm+/3549NGgnsIYY1xV3rvjivcVMlWr5k0UzzwD\nO3eGMgJjjAk/EZE4RORd4FugpYikisj1oTr30KHQrRt06QJffgn16oXqzMYYE54ipqmqOGX55Pgf\nf0DdujYMiTGm/Amkqaq0N8crhIQEtyMwxpjwYX9DG2OM8YsljlL44w+3IzDGmNCzxBGA/fudZzra\ntXMeDjTGmIrEEoefVKF/f3jzTadr7tVXw7FjbkdljDGhY4nDTyLOUCQ5PawWLoS773Y3JmOMCaXS\nJo7rghFEpOnZ00keOZ57Dl580b14jDEmlEqVOFS1ws6P8de/wpVXHl+eMcNpxjLGmPLOpo4thcOH\nITkZGjaE995zhigxxphIYnOOu/BZdu+GmjWhkj1KaYyJQJY4yslnMcaYUCnvo+NGlN9/h5desvse\nxpjyx+/EISLVReQSEambb/0gEakZvNAiV0YGDBoEt9wCN9zgLBtjTHkRSI3jBeA0YJKI1Mi1/ltg\noohU+FrM00/Dt9867ydNcm6gr1vnakjGGBM0gXzJ71bVJ4GHgCE5K1V1B/AcMDpIsUWsu+6C6647\nvvzdd9CxI7zyimshGWNM0ASSODIBVHUJ0Dr3BlVdCrQKQlwRrWpVeOMNeOqp472tjhyB7Gx34zLG\nmGAIJHHEi0h973tPAdutRR9naJK77oLFi53BEC+4AG6+2e2ojDGm9AJ5+uAlYJaI3ELBiSOpdCGV\nL506wdKlcOCAk0zy270bMjPh5JNDH5sxxgTC7xqHqi4H/g3MA0aIyIsico+I3C8iKYANNJ5P1arO\n1LMFeeopaNwYRo6En34KaVjGGBOQgB8AFJGzgHFAbyAa2A+8DDygqiFvzY/EBwAPHIBTT4W9e4+v\n69QJRoxwbq7XtM7Nxpgy5sqT4yJSCagD7HIjYeSKI+ISx/r1MHw4LFqUd31UFGzdCg0auBOXMabi\nCMmT4yJSxfsAYAMAVc1S1d+BviIS4+/xKrLmzZ3nPRYudCaEyhkksW/fgpPG7t3OcyFr14KnoLtL\nxhgTAn7XOERkPPAXYI2qnpFrfWPgQVW9IZgB+hFXxNU48tu7F6ZNg2bN4PzzT9z+wQdw1VXO+9hY\nOP106NwZBgxwko0xxvgrkBpHIL2qqgIXA3la4FV1s4hMEpHrVPXNAI5bKBG5EHgWp4b0uqo+Hszj\nh4tateDdhH0LAAAZaUlEQVSmmwrfnpJy/P2BA7BggfMSKThxfPMNrF7t1F5yXnXrHp+90BhjAhFI\n4jhJVecXtEFVvxGRK0oZUx7eIUyeB84H0oAlIvKpqq4O5nkiQdeucOGF8MMPznznOTp3Lrj81Knw\nwgt511WqBBMnwp/+dGL5L7+EDRugRg2oXv34q1WrwnuFGWMqnkASR00RaaeqvxSyPbM0ARWgG7BO\nVbcAiMhUYCBQ4RLH9dc7L1VIS4Mff3RePXoUXH5bAR2js7IK7601eTK8864Hoo5BdCZUyoDoTJ59\nLpP+F2eSkZVBZnYmWZ4sPOrhH+OyWfCNh8pVs6lUOZvKVTxEV85m9M0eOnfJJtuTjUc9ZKvz7wcf\nZbN2XTYS5YGobKKiPEiU0q8fNGniNDMq3n9VmTdf+e03QNRbS1IQ5eyzoMEpx8vl7PPd98qO7d5j\nSE6zpXLGGZCYqHmODfDDMuWP373l5Hgz5+mnQ0LCidfnp5+chO17Hsf7b4f2UK/eieV//hl27Tpx\nfXuXyq9YATsLKt/O7fJ5m5jbtYN6BfyhsuKXgj+vv+Xbtyv4D6GKVv6XX5zvg0AEco+jPfAFcC/w\nbv4bCyLyqqqOCiycAs93BdBPVW/yLl8LdFPV2/OVi/h7HIFQVQ5kHmDX4V2kH0lnz5E97Dmyh/Sj\n6cxK2cOGbensO3KQAxmHOJx1iGMcpF3nQ1SqdohDmYc4mHmQo1lHyczO5HBGJhp1zO2PZIwJpXGU\n/T0OVV0hIn8BpgD/FJHpwA/AAeA8XJzjY1xysjMULZC8eTPJjRvDuHHejZH579G/38+GPRuIfuhh\n9h7dy8dXd2DbgW1cMGUhBzIPMKZHBoeOHWLsXGe38b2cf8fOhU7A9F5ALfJs/2U/jP30xPK2bMu2\nXAGWN0HPFGd5XmMCUpoHADsBT+A8AJiTrWYDV6vq/sDCKfA83YFxqnqhd/l+QPPfII/0GkeWJ4vV\nu1bz4/Yf+SHtB1btWsW63etI3Zfqa2IJlSrRVXyvqtFV8yxXia5CpahKREdFIxqFEI0QhWg0QjRo\nFDEnRVOlchTREk10VDRR4rzftzeKzKNOeXD2R4XERKFaNRDvj5F424J+2yocOCDOp1dB1SnTuLFQ\nM+54OUEQETZsgP37nPKCgPePqBYthFq1jpfL2WfNGkhPF3w/vt7yp50GdeqceF1WrXK6REOuxhWF\nNm0KbgpYufJ4+dzcKv9LIeXbhkF58X2FQNu2ULeA61/o8YsoX2DTVtvC46lQ5b1NVZ/f84grDwDW\nBZoCf6jq5lIdrODjRwNrcG6ObwcWA0NV9dd85SIqcew5sof5W+aTsjmF77d9z087fuJI1pGAjnVS\npZOoV60edarVofZJtYmPiSc+Jp7aJ9WmdkxtYqvEUqNKDapXqU71ytVPeH9SpZOoWslJEJWjKvu+\nXI0x5V+ouuPmoaq7RKQa0ElEtgb76XFVzRaRW4E5HO+O+2sxu4UdVeWH7T/w6epPmbluJst3LC9R\nTSJKomhcqzEt4lvQtHZTGsY1pEFsAxrENuCU2FNoENuAWifVsi97Y0zIlLrGAb4us32Aq4B/udFV\nNlxrHD/t+IlJyyfx4aoP2Xag6PEfG8Y1pPPJnelychc6JnakVd1WNKnVhKqVqoYoWmNMRROSsapE\npD/wHyAe+AmYDkxV1VRvs9LzqlrAUwJlK5wSx8HMg0xePpnXl73Osh3LCiwTLdF0bdCVXo17cW6j\nc+naoCsJ1QvoA2qMMWUoVIljAfAZzh3FjsCFQCwwFyeJ9FPVi/06aBCEQ+L449AfPPf9c7yw5AXS\nj6afsD0+Jp6LW17MwFYD6dO0D3FV41yI0hhjjgvVPY5vcvdo8o6OOwAYBtwITAjgmBFt39F9TJg/\ngeeXPM/RrKN5tlWNrsqg1oO47vTr6N2kN5WiSn1byRhjXBWMm+NZODWN6aUPJ7JkebJ49YdX+UfK\nP9h1OG8/uKa1m/KX7n/h2g7XUuukWi5FaIwxwRdIU1VnoKeqPlM2IQUm1E1Vy7YvY8R/R7DijxV5\n1nc+uTP3nXMfV7S+guio6JDFY4wxgQhVU9UeYJD3wbxngSXeWkeFkOXJ4omFTzAuZRzHPMeH52hU\nsxGP93mcq9peZV1jjTHlWiA1jnlADM5Df/HAIWAhzs3xFJxEEvJphkJR40jdl8rQj4by7dZvfeti\nKsXw4HkPcmf3O4mpbPNYGWMiS6h6VU3MGWBQRDrgDDmSjDNOVS3gZ1U93a+DBkFZJ47vf/uegVMH\n8vuh333rzjzlTKZcPoWWdVqW2XmNMaYshSpxXIqTKL4BZqpqhnd9FNAZOFlVZ/h10CAoy8QxbeU0\nRvx3hK/HVKWoSvzjvH8w5twx1kvKGBPRQpI4vCeqDPQEVnjnG3ddWSWOx755jDH/G+Nbjo+J5+Or\nPqZn455BP5cxxoRayBJHASduDNwN7AZeUNWdRe5QBoKdOFSVsSljeXj+w751req04rNrPqN5fPOg\nnccYY9wUqqaq+UB1nJvhc4EFOcOoi0gd4HFVvdGvgwZBsBPHuJRxjJ833rfcq3EvPrrqI2rH1A7a\nOYwxxm2h6o77sveVANwFZIvIcpweVRuBRgEcM6w89/1zeZJG/+b9+fjqjzmp0kkuRmWMMeEhkMRx\nHtBSVdNE5FSgl3fdYJyayG1BjC/kPvn1E+744g7fcr9m/fjk6k9shFpjjPEKJHGkq2oagKqmApOB\nySJSFXgOKHg42AiwbPsyrv3kWt88Gd0bduejqz6ypGGMMbkEMj94MxFJyr/S2y33ZuCWUkflgt2H\nd3PZ+5dx+NhhAJrVbsZnQz+jepXqLkdmjDHhJZDE8RLwg4gM9c6/4eN9Yjzihh/xqIcR/x1B6r5U\nAOKqxjFj6AzqVCtgImNjjKng/G6qUtWvRWQMThPV8yIyG/gOSMUZhqRFcEMse88seoaZ62b6lt+6\n/C1a12vtYkTGGBO+An6OQ0Q6Av8E+gE5NY9lwGBV3Ric8PyKJ6DuuL/88QtdXulCZnYmAHefdTf/\n6vuvYIdnjDFhyZUHAEWkJtAc2Keq60t1sNLF4XfiOJZ9jDNfO9M3vWuXk7uw6IZFVI6uXBYhGmNM\n2AnVcxx5qOo+4IfSHscN//r2X76kUTW6KlMun2JJwxhjilHkzXERuUtEzi/uICLSWEQeF5F2wQut\nbG1M38hD8x/yLU/oPYE29dq4GJExxkSGIpuqRGQdsF9Vu+RadzUwBlgFvKGqX3nXV8LpjrtPVd8q\n06gLjtWvpqqL3r2Iz9d9DsDp9U9nyaglNtKtMabCCaSpqrjuuGcCl+Rbdy0wCagHzBaRjSLyAFBP\nVZ8HOvoTQFFE5EoR+UVEsr1T1gbFF+u/8CUNQfjPRf+xpGGMMSVUZOJQ1T05T4nnshWYqKoX4HS9\nnQrcCqSKyA9AF4JnBXA5MC9YB8zyZHHPnHt8yzd0uoEzG54ZrMMbY0y5F8gDgC8A/xGR3qq6UVUf\nAJKAy4C3gWuCFZyqrlHVdUDQJvGetGwSK3euBKBGlRo83PvhYvYwxhiTm9+JQ1VX4tQw6opIQ++6\nbFWdqarPqOr2YAcZLEezjuYZ9fa+c+6jfo36LkZkjDGRx++GfRHpCgwHPlTV30obgIh8CSTmXgUo\n8Dd/p6AdN26c731ycjLJycl5tr/242tsO7ANgPo16nPXWXcFFLMxxkSqlJQUUlJSSnWMQCZyehvo\nAexR1aDdsC7mnHOBu1X1xyLKFNmr6sixIzSb2IztB50K0bP9nuWO7ncUWt4YYyqCUD0A+BvQGagW\nwL6lUar7HG8se8OXNE6ucTI3dbkpKEEZY0xFE8jN8VVA62A0UxVHRC4Tka1Ad+AzEZkVyHGyPdk8\ntegp3/J959xHTOWYIEVpjDEVSyBNVTHANGAJME1VV5dFYP4qqqnqg5UfcNWHVwEQHxNP6p2pNs+G\nMcZQNg8AFuQ9oBXO0+MrRWS7iLwnIqNFpFUAxytTqsqT3z7pW76l6y2WNIwxphQCSRxbVbUlEIcz\n3/hLQH3gWWCViHwbxPhK7bvfvmNJ2hLAGcjw1m63uhyRMcZEtkBujs8TkUeBRcBcVZ0PPOSdc/xs\noGEwAyytF5e+6Ht/TftrSKyRWERpY4wxxQloPg5vkkgG0lV1cbCDCkRB9zh2Hd7FKU+f4pukaemo\npXRpEMwRUYwxJrKFbD4OVc0AZgeybyi9sewNX9Lodko3SxrGGBMEQRkSVkQaA3cDu4EXVHVnMI5b\nGqrKqz++6lu+pestLkZjjDHlRyDdcecD1YG5QAowX1X3e7fVAR5X1RuDHGdJ4srTVLUwdSE9JvUA\nIK5qHDvu3mHPbhhjTD6haqp62ftKAO4CskVkOU4S2Qg0CuCYQffm8jd974e0HWJJwxhjgiSQxHEe\n0FJV00TkVJwuuecBg3FqIrcFMb6AHD52mGmrpvmWR5w+wsVojDGmfAkkcaTnTO6kqqnAZGCyt6fV\nc8CyIMYXkE9Xf8r+jP0AtIhvwVkNz3I5ImOMKT8CeQCwmYgk5V/p7Wl1M+D6Xej3fnnP9354x+GI\nBG0eKGOMqfACSRwvAT+IyFARic69QVU9QFZQIgvQ3qN7mb3heE/hIe2GuBiNMcaUP343Vanq1yIy\nBqeJ6nkRmQ18B6QCTXHmIXfN9DXTfc9udD65M83jm7sZjjHGlDuBPgD4uogsBf6Jc1M858/6Zd5l\n10xbefym+NVtr3YxEmOMKZ8CmTo2FmgN7FLVi0WkJtAc2Keq64MdoD/Sj6QzZ8Mc3/LgNq7mMGOM\nKZcCqXGsxXmG43sR+RFIx2mqKnRa11D5fN3nHPMcA+CMBmfQpHYTlyMyxpjyJ5DEMQbnifNJOStE\n5HzgKRH5VFXnBS06P01fO933/rLTLnMrDGOMKdcCGh230IOJjAR2q+qnQTtoyc+tsY/EciDzAAAr\n/rSCdgntQh2GMcZElFDNAFgoVX0D5ylyV+QkjSa1mtC2Xlu3wjDGmHLN78QhIleLyHIRmSYiA0Qk\n/zFOClJsAbu01aX20J8xxpSRQO5xXAu8AVwITAf2iUgKsA5ogDNelasubXWp2yEYY0y5FUhT1Rbg\neVUdgDMS7r9w5h+/FKgCuDqpd40qNehxag83QzDGmHItkBrHROAlEfkM+FxVHwUeDW5YgevdpDdV\noqu4HYYxxpRbftc4VHUt8CfgGM7zHGVGRJ4QkV+991Q+EpG44vbp16xfWYZkjDEVXkC9qlTVo6pf\nqOr2YAeUzxygraqejnMPZUxxO1jiMMaYshXU7rjBpqpfeUfcBefp9IZFlW9WuxnN4puVfWDGGFOB\nhXXiyGckMKuoAn2b9Q1RKMYYU3EFNDpuMInIl0Bi7lWAAn9T1RneMn8Djqnqu0Uda8+sPYxbMg6A\n5ORkkpOTyyJkY4yJWCkpKaSkpJTqGEEdcqQsiMh1wCigt3eWwcLK6b6j+4irWuz9c2OMMV6BDDkS\n1olDRC4EngLOU9XdxZTVcP4sxhgTjspj4liH81BhTtL4TlULnNPcEocxxviv3CUOf1jiMMYY/7k+\nOq4xxpjyzxKHMcYYv1jiMMYY4xdLHMYYY/xiicMYY4xfLHEYY4zxiyUOY4wxfrHEYYwxxi+WOIwx\nxvjFEocxxhi/WOIwxhjjF0scxhhj/GKJwxhjjF8scRhjjPGLJQ5jjDF+scRhjDHGL5Y4jDHG+MUS\nhzHGGL9Y4jDGGOMXSxzGGGP8YonDGGOMXyxxGGOM8UtYJw4ReUhEfhKRZSLyhYjUdzsmY4yp6MI6\ncQBPqGpHVe0EzATGuh1QaaWkpLgdQolYnMETCTGCxRlskRJnIMI6cajqwVyL1QGPW7EES6T8MFmc\nwRMJMYLFGWyREmcgKrkdQHFEZAIwHNgL9HI5HGOMqfBcr3GIyJci8nOu1wrvv5cAqOrfVfVU4B3g\nNnejNcYYI6rqdgwlIiJJwOeq2r6Q7ZHxQYwxJsyoqvhTPqybqkSkuaqu9y5eBvxaWFl/P7gxxpjA\nhHWNQ0Q+BFri3BTfAtysqtvdjcoYYyq2sE4cxhhjwo/rN8dLS0QuFJHVIrJWRO5zO57CiMjmXA8z\nLnY7nhwi8rqI/C4iP+daV1tE5ojIGhGZLSI13YzRG1NBcY4Vkd9E5Efv60I3Y/TG1FBEvhaRld6O\nHrd714fVNS0gztu868PmmopIVRH53vs7s0JExnrXh9u1LCzOsLmWuYlIlDee6d5lv69nRNc4RCQK\nWAucD6QBS4Ahqrra1cAKICIbgS6qmu52LLmJSA/gIDBFVTt41z0O7FbVJ7zJuLaq3h+GcY4FDqjq\n027Glpt3dIP6qrpcRGoAPwADgesJo2taRJxXE0bXVESqqephEYkGFgK3A1cQRteyiDj7E0bXMoeI\n/AXoAsSp6qWB/L5Heo2jG7BOVbeo6jFgKs4PfzgSwvB6q+o3QP5kNhCY7H0/GadjgqsKiROc6xo2\nVHWHqi73vj+I06GjIWF2TQuJ8xTv5rC5pqp62Pu2Kk5nHiXMriUUGieE0bUEp6YJDABey7Xa7+sZ\ndl9kfjoF2Jpr+TeO//CHGwW+FJElIjLK7WCKkaCqv4PzBQMkuBxPUW4VkeUi8prbTRb5iUhj4HTg\nOyAxXK9prji/964Km2vqbVZZBuwAvlTVJYThtSwkTgija+n1DPBXjic2COB6RnriiCTnqGpnnGz/\nZ2/TS6QI1/bMF4Gmqno6zi9s2DQJeJt/PgTu8P5Fn/8ahsU1LSDOsLqmqurxjlXXEOgmIm0Jw2tZ\nQJxtCLNrKSIXAb97a5pF1YSKvZ6Rnji2AafmWm7oXRd2croRq+pO4BOcZrZw9buIJIKvLfwPl+Mp\nkKru1OM36V4FznAznhwiUgnny/gtVf3UuzrsrmlBcYbrNVXV/UAKcCFheC1z5I4zDK/lOcCl3vut\n7wG9ReQtYIe/1zPSE8cSoLmINBKRKsAQYLrLMZ1ARKp5/7JDRKoDfYFf3I0qDyHvXyDTgeu870cA\nn+bfwSV54pS8w+wPInyu6RvAKlX9d6514XhNT4gznK6piNTNad4RkRjgApx7MWF1LQuJc3U4XUsA\nVX1AVU9V1aY435Vfq+owYAZ+Xs+I7lUFTndc4N84SfB1VX3M5ZBOICJNcGoZinPj7J1wiVNE3gWS\ngTrA7zhD1/8X+ABIwnnw8ipV3etWjFBonL1w2uY9wGZgdE5brVtE5BxgPrAC5/9bgQeAxcA0wuSa\nFhHnNYTJNRWR9jg3a6O8r/dV9Z8iEk94XcvC4pxCmFzL/ESkJ3C3t1eV39cz4hOHMcaY0Ir0pipj\njDEhZonDGGOMXyxxGGOM8YslDmOMMX6xxGGMMcYvljiMMcb4xRKHMcYYv1jiMMYY4xdLHMaUknfM\nJ2MqDEscxpSCiFwJXFvG5xgnIqeX5TmM8YcNOWJMgESkN3Cpqt5ZxuepijMQ3WhV3VSW5zKmJCxx\nGFMIEWkKLAC6q+rWfNvigK+A81T1aAhiaQ68CZyr9ktrXGZNVcYU7lKgNs5ovPk9ALwdiqQBoKrr\ngVSc0WuNcZXVOIwphIh8CNRR1V751lfDmbK4uaoWNA96WcVzJvCGqrYN1TmNKYjVOIwpXA+cOSvy\nuwjYFMqk4bUEOMU7faoxrrFuhMbkIiJXAdfjNFElAL1E5Axghqq+5C12AfBtAfteCvQBOuLMpBYP\nDPZu7gE8oqqzCtgvCXgQOAQc9b6eUNUjucupqkdEFuFMn7qylB/VmIBZ4jAmF1WdBkwTkZtwEkAf\nVc3MV6wT8EruFSJSGUhW1dtFZDHwFvCxqo7xbr8P+A/QKN9+J+HcZL9YVdd5b8h/Dyyj4GmQV+LM\nKmeMa6ypypiC9QKWFJA0ABoD+afW7Al8433fFEhT1WdybRecGkh+FwD1cO6Z4D3uM8CXhcSVDjQr\nLnhjypLVOIwpWDLwWiHbanJi4lgB7BWRDjgJ4tl82zsCPxVwrL1ALeAXEfkcmKaqjxQR1x7v+Y1x\njdU4jMlHRNoAicC8Qooo+X53VPV3Vc0AeuPcq1iS63iVcO5LnND0pKoLgPuAKsCfgfki8qciwvMA\n0SX+MMaUAUscxpyoN3AM7w1wEakpIg1zbd9Lwc1O4NRUFqpqVq51A4BYYKqIRInIqbl3UNUnVfVU\noB3O/Y0/FxFbHWCfH5/FmKCzxGHMiXoAy1X1sHf5DpxEkmMTBSQOEYkCzgPm5ts0DJirqqk4va46\nect/LCLLcwqp6q/A28DGImKr4z2/Ma6xxGHMiaKBzQAi0hU4rKq5nx7/BmhTwH6dcO5XpORb3xKY\nLiLRwFXAZ971XXB6VOE9VwLOk+EPFRFbS5weV8a4xp4cNyYf7w3uF4BFwO+q+lS+7X2AZ1W1Xb71\nlwAPA51V1ZNr/WCc5zq2Av9W1dXe9efi9MaKAeKAasBLqrq0kLgE5+Z4H1X9IRif1ZhAWOIwxk8i\nUgXYBnRQ1e0hPO8ZOONjtQrVOY0piDVVGeMn77MdLwBlOpx6AW7jxG6+xoSc1TiMCYCIVMdpyjpP\nVfM/01EW52sCfAJ0UdXssj6fMUWxGocxAVDVQ8AoCn9IMGi8z4G8CAyzpGHCgdU4jCkFEekHnKaq\n/y7Dc4zH6c6bUlbnMMYfljiMCXMiEpW7l5YxbrPEYYwxxi92j8MYY4xfLHEYY4zxiyUOY4wxfrHE\nYYwxxi+WOIwxxvjFEocxxhi//D95hWDX0n+pJAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x111521860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from qutip import *\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "N=25\n",
    "a=destroy(N)\n",
    "n_op = a.dag()*a\n",
    "H=2*np.pi*a.dag()*a\n",
    "kappa=0.25\n",
    "n_th=0.0  # was 2.0\n",
    "c_ops=[np.sqrt(kappa*(1+n_th))*a,np.sqrt(kappa*n_th)*a.dag()]\n",
    "times=np.linspace(0,40,200)\n",
    "rho01=thermal_dm(N,2)\n",
    "rho02=fock_dm(N,2)\n",
    "rho03=coherent_dm(N,np.sqrt(2))\n",
    "n1=mesolve(H,rho01,times,c_ops,[a.dag()*a]).expect[0]\n",
    "n2=mesolve(H,rho02,times,c_ops,[a.dag()*a]).expect[0]\n",
    "n3=mesolve(H,rho03,times,c_ops,[a.dag()*a]).expect[0]\n",
    "m1=mesolve(H,rho01,times,c_ops,[a.dag()*a.dag()*a*a]).expect[0]\n",
    "m2=mesolve(H,rho02,times,c_ops,[a.dag()*a.dag()*a*a]).expect[0]\n",
    "m3=mesolve(H,rho03,times,c_ops,[a.dag()*a.dag()*a*a]).expect[0]\n",
    "plt.plot(times,np.real(m1-n1*n1),label='(a)',lw=3,ls='--')\n",
    "plt.plot(times,np.real(m2-n2*n2),label='(b)',lw=3,ls='-')\n",
    "plt.plot(times,np.real(m3-n3*n3),label='(c)',lw=1,ls=':')\n",
    "plt.legend(['(thermal state)','(fock state)','(coherent state)'])\n",
    "plt.xlabel(times,fontsize=20,fontweight='bold')\n",
    "plt.xlabel(r'$t(ns)$',fontsize=20,fontweight='bold')\n",
    "plt.ylabel(r'$variance - <n> $',fontsize=20,fontweight='bold')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from qutip import *\n",
    "import numpy as np\n",
    "import cmath\n",
    "\n",
    "# Try to make a pi/2 pulse on a state ket |1> and see which axis it will be\n",
    "\n",
    "# generating arbitrary qubit state\n",
    "def gen_qubit_state(alpha, beta):\n",
    "    return alpha*ket([1], dim=2) + beta*ket([0], dim=2)\n",
    "\n",
    "imaginary_unit = cmath.sqrt(-1)\n",
    "# getting the ket\n",
    "#psi_0 = gen_qubit_state(1/np.sqrt(2), -1j*1/np.sqrt(2))\n",
    "psi_0 = gen_qubit_state(0, 1)\n",
    "# rabi frequency \n",
    "omega = 2*np.pi\n",
    "# energy splitting of the system hamiltonian\n",
    "w0 = 2*np.pi\n",
    "# define Hamiltonian\n",
    "def Ham2(t, args=None):\n",
    "    return w0*sigmaz() + omega*np.sin(w0*t)*sigmax()\n",
    "\n",
    "def H1_coeff(t, args):\n",
    "    return np.sin(w0*t)\n",
    "H0 = w0*sigmaz()\n",
    "H1 = omega*sigmax()\n",
    "Ham3 = [H0,[H1,H1_coeff]]\n",
    "\n",
    "time = np.linspace(0,0.5,100)\n",
    "\n",
    "eval_operators = [sigmax(),sigmay(),sigmaz()]\n",
    "Ham1 = w0*sigmaz()\n",
    "#exp_list = sesolve(Ham1, psi_0, time, eval_operators)\n",
    "exp_list2 = mesolve(Ham2, psi_0, time, [], eval_operators)\n",
    "#exp_list3 = mesolve(Ham3, coherent(2,1), time, sigmaz(), eval_operators)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([  0.00000000e+00,   2.12969205e-05,   1.70255094e-04,\n",
       "          5.73889335e-04,   1.35791917e-03,   2.64610842e-03,\n",
       "          4.55959179e-03,   7.21618193e-03,   1.07296573e-02,\n",
       "          1.52090289e-02,   2.07577765e-02,   2.74730679e-02,\n",
       "          3.54449542e-02,   4.47555468e-02,   5.54781815e-02,\n",
       "          6.76765750e-02,   8.14039835e-02,   9.67023718e-02,\n",
       "          1.13601605e-01,   1.32118677e-01,   1.52256983e-01,\n",
       "          1.74005667e-01,   1.97339039e-01,   2.22216091e-01,\n",
       "          2.48580138e-01,   2.76358569e-01,   3.05462765e-01,\n",
       "          3.35788158e-01,   3.67214478e-01,   3.99606174e-01,\n",
       "          4.32813036e-01,   4.66671005e-01,   5.01003195e-01,\n",
       "          5.35621104e-01,   5.70326025e-01,   6.04910639e-01,\n",
       "          6.39160784e-01,   6.72857369e-01,   7.05778434e-01,\n",
       "          7.37701306e-01,   7.68404847e-01,   7.97671738e-01,\n",
       "          8.25290796e-01,   8.51059254e-01,   8.74785002e-01,\n",
       "          8.96288729e-01,   9.15405943e-01,   9.31988834e-01,\n",
       "          9.45907945e-01,   9.57053622e-01,   9.65337231e-01,\n",
       "          9.70692100e-01,   9.73074192e-01,   9.72462490e-01,\n",
       "          9.68859078e-01,   9.62288943e-01,   9.52799485e-01,\n",
       "          9.40459745e-01,   9.25359389e-01,   9.07607439e-01,\n",
       "          8.87330799e-01,   8.64672592e-01,   8.39790344e-01,\n",
       "          8.12854037e-01,   7.84044083e-01,   7.53549233e-01,\n",
       "          7.21564467e-01,   6.88288891e-01,   6.53923669e-01,\n",
       "          6.18670031e-01,   5.82727364e-01,   5.46291417e-01,\n",
       "          5.09552650e-01,   4.72694713e-01,   4.35893099e-01,\n",
       "          3.99313955e-01,   3.63113068e-01,   3.27435026e-01,\n",
       "          2.92412542e-01,   2.58165950e-01,   2.24802860e-01,\n",
       "          1.92417959e-01,   1.61092955e-01,   1.30896645e-01,\n",
       "          1.01885103e-01,   7.41019593e-02,   4.75787809e-02,\n",
       "          2.23355144e-02,  -1.61900203e-03,  -2.42864800e-02,\n",
       "         -4.56785842e-02,  -6.58163079e-02,  -8.47293300e-02,\n",
       "         -1.02455363e-01,  -1.19039497e-01,  -1.34533547e-01,\n",
       "         -1.48995407e-01,  -1.62488414e-01,  -1.75080724e-01,\n",
       "         -1.86844707e-01]),\n",
       " array([ 0.        , -0.00100659, -0.00402125, -0.00902872, -0.01600343,\n",
       "        -0.02490928, -0.03569946, -0.04831607, -0.06268981, -0.0787396 ,\n",
       "        -0.09637222, -0.11548193, -0.13595014, -0.1576452 , -0.1804222 ,\n",
       "        -0.20412294, -0.22857599, -0.25359702, -0.27898918, -0.3045438 ,\n",
       "        -0.33004128, -0.35525222, -0.37993885, -0.40385664, -0.42675626,\n",
       "        -0.44838573, -0.46849284, -0.48682777, -0.50314593, -0.51721094,\n",
       "        -0.52879768, -0.53769547, -0.54371121, -0.54667249, -0.54643062,\n",
       "        -0.54286337, -0.53587765, -0.52541169, -0.51143703, -0.49395996,\n",
       "        -0.47302259, -0.44870333, -0.42111692, -0.39041382, -0.3567791 ,\n",
       "        -0.32043072, -0.2816173 , -0.2406154 , -0.19772622, -0.15327205,\n",
       "        -0.10759221, -0.06103881, -0.01397227,  0.03324327,  0.08024461,\n",
       "         0.12667404,  0.17218375,  0.21644011,  0.25912756,  0.2999522 ,\n",
       "         0.33864491,  0.37496412,  0.40869791,  0.43966578,  0.46771979,\n",
       "         0.49274512,  0.51466029,  0.53341671,  0.54899783,  0.56141797,\n",
       "         0.57072059,  0.57697641,  0.58028117,  0.58075319,  0.57853082,\n",
       "         0.57376974,  0.56664028,  0.55732467,  0.54601438,  0.5329076 ,\n",
       "         0.51820677,  0.50211629,  0.48484045,  0.46658149,  0.44753791,\n",
       "         0.427903  ,  0.40786353,  0.38759869,  0.36727924,  0.34706677,\n",
       "         0.32711326,  0.30756066,  0.2885407 ,  0.2701748 ,  0.25257403,\n",
       "         0.23583926,  0.22006122,  0.20532073,  0.19168892,  0.17922739]),\n",
       " array([ 1.        ,  0.99999949,  0.9999919 ,  0.99995908,  0.99987101,\n",
       "         0.99968621,  0.99935217,  0.99880603,  0.99797538,  0.99677919,\n",
       "         0.99512889,  0.99292958,  0.99008142,  0.98648109,  0.98202342,\n",
       "         0.97660315,  0.9701167 ,  0.96246413,  0.95355111,  0.9432909 ,\n",
       "         0.93160644,  0.9184323 ,  0.90371665,  0.88742314,  0.86953264,\n",
       "         0.85004481,  0.82897947,  0.80637773,  0.78230282,  0.75684063,\n",
       "         0.73009991,  0.70221204,  0.67333047,  0.64362972,  0.61330401,\n",
       "         0.58256543,  0.55164176,  0.52077396,  0.49021318,  0.46021771,\n",
       "         0.43104944,  0.40297037,  0.37623881,  0.35110568,  0.32781073,\n",
       "         0.30657898,  0.2876172 ,  0.27111076,  0.25722073,  0.24608138,\n",
       "         0.23779812,  0.23244593,  0.23006823,  0.23067638,  0.23424963,\n",
       "         0.2407357 ,  0.25005179,  0.26208615,  0.27670004,  0.29373018,\n",
       "         0.31299149,  0.33428015,  0.35737683,  0.38205014,  0.40806014,\n",
       "         0.43516181,  0.46310852,  0.49165539,  0.5205624 ,  0.54959736,\n",
       "         0.57853853,  0.607177  ,  0.63531871,  0.66278612,  0.68941954,\n",
       "         0.71507807,  0.73964024,  0.76300427,  0.78508802,  0.80582866,\n",
       "         0.82518205,  0.84312191,  0.85963876,  0.87473869,  0.88844203,\n",
       "         0.90078184,  0.91180239,  0.92155758,  0.93010932,  0.9375259 ,\n",
       "         0.94388049,  0.94924952,  0.95371128,  0.95734449,  0.96022703,\n",
       "         0.96243471,  0.96404016,  0.96511186,  0.96571326,  0.96590196])]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exp_list2.expect"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x10fbf6550>]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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IYbqZdTGzi8uOeQGYa2ZzgPuAy3I9r4hIvjRv7mVoTjnFxyiLkaqWiohU0WWX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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10497e278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(exp_list2.expect[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [qutip]",
   "language": "python",
   "name": "Python [qutip]"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}