{
 "metadata": {
  "name": ""
 },
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 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### QuTiP development notebook for testing extracting and eliminating states from a quantum object\n",
      "\n",
      "Copyright (C) 2011 and later, Paul D. Nation & Robert J. Johansson"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%pylab inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Populating the interactive namespace from numpy and matplotlib\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from qutip import *"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def visualize(result):\n",
      "    fig, ax = subplots(figsize=(12,6))\n",
      "    for idx, e in enumerate(result.expect):\n",
      "        ax.plot(result.times, e, label=\"%d\" % idx)\n",
      "    ax.legend()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Consider two coupled qubits:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "H0 = -2*pi*(tensor(sigmaz(), identity(2)) + tensor(identity(2), sigmaz()))\n",
      "Hint = 2*pi*0.1 * tensor(sigmax(), sigmax())\n",
      "\n",
      "H = H0 + Hint"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "H"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "latex": [
        "$\\text{Quantum object: dims = [[2, 2], [2, 2]], shape = [4, 4], type = oper, isHerm = True}\\\\[1em]\\begin{pmatrix}-12.566 & 0.0 & 0.0 & 0.628\\\\0.0 & 0.0 & 0.628 & 0.0\\\\0.0 & 0.628 & 0.0 & 0.0\\\\0.628 & 0.0 & 0.0 & 12.566\\\\\\end{pmatrix}$"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "Quantum object: dims = [[2, 2], [2, 2]], shape = [4, 4], type = oper, isherm = True\n",
        "Qobj data =\n",
        "[[-12.56637061   0.           0.           0.62831853]\n",
        " [  0.           0.           0.62831853   0.        ]\n",
        " [  0.           0.62831853   0.           0.        ]\n",
        " [  0.62831853   0.           0.          12.56637061]]"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "energy_level_diagram([H], figsize=(6,6), show_ylabels=True);"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAFdCAYAAAD49y2uAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x3a30410>"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "If the highest energy state is not initially occupied, and if we neglect thermal excitations, it will never be occupied, and therefore not really necessary to include in a calculation of for example the dynamics of the three lowest states."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "psi_gg = tensor(basis(2, 0), basis(2, 0))\n",
      "psi_eg = tensor(basis(2, 1), basis(2, 0))\n",
      "psi_ge = tensor(basis(2, 0), basis(2, 1))\n",
      "psi_ee = tensor(basis(2, 1), basis(2, 1))\n",
      "\n",
      "c_op1 = tensor(destroy(2), identity(2))\n",
      "c_op2 = tensor(identity(2), destroy(2))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "psi0 = psi_eg"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "tlist = linspace(0, 15, 100)\n",
      "result = mesolve(H, psi0, tlist,\n",
      "                 [sqrt(0.1) * c_op1, sqrt(0.25) * c_op2],\n",
      "                 [ket2dm(psi_gg), ket2dm(psi_ge), ket2dm(psi_eg), ket2dm(psi_ee)])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "visualize(result)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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RrmdPaNXKrVvvRUTER6mHOCsiImDfPjhxwupKrpGUkkSv6b34vMXnloVhgH53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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x3b04410>"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Note that the fourth state is not included in the dynamics at all. So..."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Assume we want to create a new Qobj instance using the three lowest states:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# We are only interested in these states\n",
      "keep_states = [0, 1, 2]\n",
      "\n",
      "# or equivalently, not interested in these states\n",
      "remove_states = [3]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "H_sub = H.eliminate_states(remove_states)\n",
      "\n",
      "H_sub"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "latex": [
        "$\\text{Quantum object: dims = [[3], [3]], shape = [3, 3], type = oper, isHerm = True}\\\\[1em]\\begin{pmatrix}-12.566 & 0.0 & 0.0\\\\0.0 & 0.0 & 0.628\\\\0.0 & 0.628 & 0.0\\\\\\end{pmatrix}$"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "Quantum object: dims = [[3], [3]], shape = [3, 3], type = oper, isherm = True\n",
        "Qobj data =\n",
        "[[-12.56637061   0.           0.        ]\n",
        " [  0.           0.           0.62831853]\n",
        " [  0.           0.62831853   0.        ]]"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "H_sub = H.extract_states(keep_states)\n",
      "\n",
      "H_sub"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "latex": [
        "$\\text{Quantum object: dims = [[3], [3]], shape = [3, 3], type = oper, isHerm = True}\\\\[1em]\\begin{pmatrix}-12.566 & 0.0 & 0.0\\\\0.0 & 0.0 & 0.628\\\\0.0 & 0.628 & 0.0\\\\\\end{pmatrix}$"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 13,
       "text": [
        "Quantum object: dims = [[3], [3]], shape = [3, 3], type = oper, isherm = True\n",
        "Qobj data =\n",
        "[[-12.56637061   0.           0.        ]\n",
        " [  0.           0.           0.62831853]\n",
        " [  0.           0.62831853   0.        ]]"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "energy_level_diagram([H_sub], figsize=(6,6), show_ylabels=True);"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAFiCAYAAAAN25jWAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nCCTuAIHEHSDQ/wBrJk8CS2YKTQAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x3b07110>"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "psi0_sub = psi0.extract_states(keep_states, normalize=True)\n",
      "\n",
      "psi0_sub"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "latex": [
        "$\\text{Quantum object: dims = [[3], [1]], shape = [3, 1], type = ket}\\\\[1em]\\begin{pmatrix}0.0\\\\0.0\\\\1.0\\\\\\end{pmatrix}$"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 15,
       "text": [
        "Quantum object: dims = [[3], [1]], shape = [3, 1], type = ket\n",
        "Qobj data =\n",
        "[[ 0.]\n",
        " [ 0.]\n",
        " [ 1.]]"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "c_op1_sub = c_op1.extract_states(keep_states)\n",
      "c_op2_sub = c_op2.extract_states(keep_states)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "psi_gg_sub = psi_gg.extract_states(keep_states)\n",
      "psi_eg_sub = psi_eg.extract_states(keep_states)\n",
      "psi_ge_sub = psi_ge.extract_states(keep_states)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "tlist = linspace(0, 15, 100)\n",
      "result = mesolve(H_sub, psi0_sub, tlist,\n",
      "                 [sqrt(0.1) * c_op1_sub, sqrt(0.25) * c_op2_sub],\n",
      "                 [ket2dm(psi_gg_sub), ket2dm(psi_ge_sub), ket2dm(psi_eg_sub)])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "visualize(result)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Kjz76KC+//PJVnz969CgPPvggBw8eJDk5mRdeeIF+/fpdfyNnL83/9Rc884z5m1VERNzK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       "text": [
        "<matplotlib.figure.Figure at 0x3b1f710>"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Voila! Same results with fewer quantum states in the calculation, and therefore potentially more efficient."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Software versions"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from qutip.ipynbtools import version_table\n",
      "version_table()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<table><tr><th>Software</th><th>Version</th></tr><tr><td>Cython</td><td>0.17.4</td></tr><tr><td>SciPy</td><td>0.11.0</td></tr><tr><td>QuTiP</td><td>2.3.0.dev-fcfbaeb</td></tr><tr><td>Python</td><td>2.7.4 (default, Apr 19 2013, 18:28:01) \n",
        "[GCC 4.7.3]</td></tr><tr><td>IPython</td><td>2.0.0-dev</td></tr><tr><td>OS</td><td>posix [linux2]</td></tr><tr><td>Numpy</td><td>1.7.1</td></tr><tr><td>matplotlib</td><td>1.4.x</td></tr><tr><td colspan='2'>Sat Aug 24 21:29:47 2013 JST</td></tr></table>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 20,
       "text": [
        "<IPython.core.display.HTML at 0x3a35210>"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}