mf-khaneja.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-31T03:40:16.439615Z",
     "start_time": "2019-01-31T03:40:16.431240Z"
    }
   },
   "outputs": [],
   "source": [
    "\n",
    "# coding: utf-8\n",
    "\n",
    "# # Imports\n",
    "\n",
    "# ## Qutip imports 1\n",
    "\n",
    "# In[1]:\n",
    "\n",
    "\n",
    "from qutip.operators import sigmax, sigmay, sigmaz, identity\n",
    "# position, momentum, num, create, destroy, commutator, qeye \n",
    "from qutip.tensor import tensor\n",
    "from qutip.qip.gates import swap, rx, ry, rz\n",
    "from qutip.qobj import Qobj\n",
    "from qutip.visualization import hinton\n",
    "from qutip.visualization import matrix_histogram_complex, matrix_histogram\n",
    "from qutip.random_objects import rand_herm, rand_unitary, rand_dm\n",
    "\n",
    "\n",
    "# ## Qutip imports 2\n",
    "\n",
    "# In[2]:\n",
    "\n",
    "\n",
    "#from \n",
    "\n",
    "\n",
    "# ## Numpy imports \n",
    "\n",
    "# In[2]:\n",
    "\n",
    "\n",
    "from numpy import sin, cos, tan, real, imag,  log, conj\n",
    "from numpy import array, append, linspace, arange\n",
    "from numpy import add, sqrt, abs, dot\n",
    "from numpy.random import random, random_sample, rand, seed, RandomState\n",
    "from numpy import concatenate, trace, diag\n",
    "from numpy import ones, zeros, ones_like, zeros_like\n",
    "from numpy import amax, amin, nanmax, nanmin\n",
    "from numpy import outer, multiply\n",
    "# from numpy import pi\n",
    "\n",
    "\n",
    "# ## Scipy imports\n",
    "\n",
    "# In[17]:\n",
    "\n",
    "\n",
    "from scipy.integrate import ode, odeint, complex_ode\n",
    "from scipy.optimize import minimize\n",
    "from scipy.linalg import eigh, inv, norm, expm\n",
    "# from scipy.linalg import \n",
    "# from scipy import\n",
    "\n",
    "\n",
    "# ## Matplotlib imports\n",
    "\n",
    "# In[18]:\n",
    "\n",
    "\n",
    "from matplotlib.pyplot import plot, figure, show, savefig, axes\n",
    "from matplotlib.pyplot import xlabel, ylabel, title, legend\n",
    "from matplotlib import rcParams\n",
    "from matplotlib.pyplot import style \n",
    "from matplotlib.pyplot import xlim, ylim, axis \n",
    "# beware not same as axes\n",
    "from matplotlib.pyplot import subplot, subplots, text\n",
    "from matplotlib.pyplot import GridSpec\n",
    "from matplotlib.pyplot import scatter, colorbar\n",
    "\n",
    "\n",
    "pgf_with_rc_fonts = {\"pgf.texsystem\": \"pdflatex\"}\n",
    "rcParams.update(pgf_with_rc_fonts)\n",
    "style.use('seaborn-whitegrid')\n",
    "\n",
    "\n",
    "# ## Math imports\n",
    "\n",
    "# In[19]:\n",
    "\n",
    "\n",
    "from math import pi\n",
    "from math import exp\n",
    "\n",
    "\n",
    "# ## Cmath imports\n",
    "\n",
    "# ## Date and datetime imports\n",
    "\n",
    "# In[20]:\n",
    "\n",
    "\n",
    "from datetime import date\n",
    "from datetime import datetime# now\n",
    "\n",
    "\n",
    "# ## Os imports\n",
    "\n",
    "# In[21]:\n",
    "\n",
    "\n",
    "from os import getcwd, mkdir, chdir\n",
    "from os.path import abspath, join \n",
    "\n",
    "\n",
    "# ## Sympy imports\n",
    "\n",
    "# In[22]:\n",
    "\n",
    "\n",
    "from sympy import Function, dsolve, Eq, Derivative, symbols\n",
    "# x, y, z, t = symbols('x y z t')\n",
    "# k, m, n = symbols('k m n', integer=True)\n",
    "# f, g, h = symbols('f g h', cls=Function)\n",
    "\n",
    "\n",
    "# ## Miscellaneous imports\n",
    "\n",
    "# ## Extra useful functions\n",
    "\n",
    "# In[23]:\n",
    "\n",
    "\n",
    "def rint(x):\n",
    "    print(\"x = \", x)\n",
    "    return None\n",
    "\n",
    "\n",
    "# # Next chapter\n",
    "\n",
    "# ## sub topic 1\n",
    "\n",
    "# ## sub topic 2\n",
    "\n",
    "# ## sub topic 3\n",
    "\n",
    "# ### sub sub topic 1\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The code "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## memory wiping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-31T03:40:16.633826Z",
     "start_time": "2019-01-31T03:40:16.630680Z"
    }
   },
   "outputs": [],
   "source": [
    "%reset_selective -f  omega_1, total_time, epsilon, max_iter, time_steps, dt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-31T03:40:16.639987Z",
     "start_time": "2019-01-31T03:40:16.636617Z"
    }
   },
   "outputs": [],
   "source": [
    "%reset_selective -f  Lin, T_s, I"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Building parts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Building the things to be calculated only once"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-31T03:40:16.854177Z",
     "start_time": "2019-01-31T03:40:16.846154Z"
    },
    "code_folding": []
   },
   "outputs": [],
   "source": [
    "def maker(omega_1, H_0, H_1, T_s, Lin, d=2, gamma=0.1):\n",
    "    r\"\"\"maker\n",
    "    Makes all the things that remain constant throught the program, but are \n",
    "    repeatedly used.\n",
    "    \n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    omega_1 : float\n",
    "              frequency corresponding to half of the difference between \n",
    "              energy levels of the qubit\n",
    "              \n",
    "    H_0     : Qobj\n",
    "              Bare Hamiltonian \n",
    "              \n",
    "    H_1     : Qobj\n",
    "              Interaction Hamiltonian \n",
    "              \n",
    "    T_s     : Qobj\n",
    "              Unitary to be implemented in the Hilbert space\n",
    "    \n",
    "    Lin     : Qobj\n",
    "              Linbladian operators\n",
    "\n",
    "    d       : int\n",
    "              Dimension of the matrix. Defaults to 2\n",
    "    \n",
    "    gamma   : float\n",
    "              Damping constantof the Linbladian\n",
    "\n",
    "    \n",
    "    Returns\n",
    "    -------\n",
    "    \n",
    "    ih0     : Qobj\n",
    "              $I\\otimes H_{0}$\n",
    "              \n",
    "    ih1     : Qobj\n",
    "              $I\\otimes H_{1}$\n",
    "\n",
    "    h0ci    : Qobj\n",
    "              $H_{0}^{*}\\otimes I $\n",
    "\n",
    "    h1ci    : Qobj\n",
    "              $H_{1}^{*}\\otimes I $\n",
    "\n",
    "    T       : Qobj\n",
    "              Target unitary transformed to the Liouville space\n",
    "\n",
    "    linbladian : Qobj\n",
    "                 The full lindbladian term as it appears on transformation to \n",
    "                 the Liouville space.\n",
    "        \n",
    "    \"\"\"\n",
    "    I = identity(d)\n",
    "    L_I = tensor(I, I)\n",
    "    ih0 = tensor(I, H_0) \n",
    "    ih1 = tensor(I, H_1) \n",
    "    h0ci = tensor(H_0.conj(), I) \n",
    "    h1ci = tensor(H_1.conj(), I)\n",
    "    x_k = ih1 - h1ci\n",
    "    term1 = tensor(Lin.trans(), Lin)\n",
    "    term2 = tensor(I, ((Lin.dag())*(Lin)))\n",
    "    term3 = tensor(((Lin.trans())*(Lin.conj())), I)\n",
    "    lindbladian = 1j*(gamma)*(term1 - 0.5*(term2 + term3))\n",
    "    T = tensor(T_s.trans(), T_s) # Transforming $T_{s}$ to liouville space\n",
    "    \n",
    "    \n",
    "    return ih0, ih1, h0ci, h1ci, x_k, lindbladian, T, L_I"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-31T03:40:16.867995Z",
     "start_time": "2019-01-31T03:40:16.856895Z"
    }
   },
   "outputs": [],
   "source": [
    "omega_1 = 0.5\n",
    "H_0 = sigmaz() \n",
    "H_1 = sigmay()\n",
    "T_s = sigmax() \n",
    "Lin = sigmaz()\n",
    "ih0, ih1, h0ci, h1ci, x_k, lindbladian, T, L_I  = maker(omega_1,\n",
    "                                                  H_0, H_1, T_s, \n",
    "                                                  Lin, d=2, gamma=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-31T03:40:16.877466Z",
     "start_time": "2019-01-31T03:40:16.870304Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/latex": [
       "Quantum object: dims = [[2, 2], [2, 2]], shape = (4, 4), type = oper, isherm = True\\begin{equation*}\\left(\\begin{array}{*{11}c}1.0 & 0.0 & 0.0 & 0.0\\\\0.0 & 1.0 & 0.0 & 0.0\\\\0.0 & 0.0 & 1.0 & 0.0\\\\0.0 & 0.0 & 0.0 & 1.0\\\\\\end{array}\\right)\\end{equation*}"
      ],
      "text/plain": [
       "Quantum object: dims = [[2, 2], [2, 2]], shape = (4, 4), type = oper, isherm = True\n",
       "Qobj data =\n",
       "[[1. 0. 0. 0.]\n",
       " [0. 1. 0. 0.]\n",
       " [0. 0. 1. 0.]\n",
       " [0. 0. 0. 1.]]"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "L_I"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Building $And(t)$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-31T03:40:16.970623Z",
     "start_time": "2019-01-31T03:40:16.967895Z"
    }
   },
   "outputs": [],
   "source": [
    "def And(xi):\n",
    "    r\"\"\"making $And(t)$\"\"\"\n",
    "    And = ih0 - h0ci + xi*(ih1 - h1ci) + lindbladian\n",
    "    return And"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-31T03:40:16.984136Z",
     "start_time": "2019-01-31T03:40:16.976213Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/latex": [
       "Quantum object: dims = [[2, 2], [2, 2]], shape = (4, 4), type = oper, isherm = False\\begin{equation*}\\left(\\begin{array}{*{11}c}0.0 & -0.500j & -0.500j & 0.0\\\\0.500j & (-2.0-0.200j) & 0.0 & -0.500j\\\\0.500j & 0.0 & (2.0-0.200j) & -0.500j\\\\0.0 & 0.500j & 0.500j & 0.0\\\\\\end{array}\\right)\\end{equation*}"
      ],
      "text/plain": [
       "Quantum object: dims = [[2, 2], [2, 2]], shape = (4, 4), type = oper, isherm = False\n",
       "Qobj data =\n",
       "[[ 0.+0.j   0.-0.5j  0.-0.5j  0.+0.j ]\n",
       " [ 0.+0.5j -2.-0.2j  0.+0.j   0.-0.5j]\n",
       " [ 0.+0.5j  0.+0.j   2.-0.2j  0.-0.5j]\n",
       " [ 0.+0.j   0.+0.5j  0.+0.5j  0.+0.j ]]"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "And(0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Building $Lnd(t)$ "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-31T03:40:17.092038Z",
     "start_time": "2019-01-31T03:40:17.088481Z"
    }
   },
   "outputs": [],
   "source": [
    "def Lnd(xi, dt):\n",
    "    r\"\"\"Making $Lnd(t) from $A(t)$\"\"\"\n",
    "    Lnd = (-1j*And(xi)*dt).expm()\n",
    "    return Lnd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-31T03:40:17.106370Z",
     "start_time": "2019-01-31T03:40:17.097539Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/latex": [
       "Quantum object: dims = [[2, 2], [2, 2]], shape = (4, 4), type = oper, isherm = False\\begin{equation*}\\left(\\begin{array}{*{11}c}1.000 & (-4.999\\times10^{-04}-4.999\\times10^{-07}j) & (-4.999\\times10^{-04}+4.999\\times10^{-07}j) & 2.500\\times10^{-07}\\\\(4.999\\times10^{-04}+4.999\\times10^{-07}j) & (1.000+0.002j) & -2.500\\times10^{-07} & (-4.999\\times10^{-04}-4.999\\times10^{-07}j)\\\\(4.999\\times10^{-04}-4.999\\times10^{-07}j) & -2.500\\times10^{-07} & (1.000-0.002j) & (-4.999\\times10^{-04}+4.999\\times10^{-07}j)\\\\2.500\\times10^{-07} & (4.999\\times10^{-04}+4.999\\times10^{-07}j) & (4.999\\times10^{-04}-4.999\\times10^{-07}j) & 1.000\\\\\\end{array}\\right)\\end{equation*}"
      ],
      "text/plain": [
       "Quantum object: dims = [[2, 2], [2, 2]], shape = (4, 4), type = oper, isherm = False\n",
       "Qobj data =\n",
       "[[ 9.99999750e-01+0.00000000e+00j -4.99949587e-04-4.99933130e-07j\n",
       "  -4.99949587e-04+4.99933130e-07j  2.49983230e-07+0.00000000e+00j]\n",
       " [ 4.99949587e-04+4.99933130e-07j  9.99797770e-01+1.99959837e-03j\n",
       "  -2.49966565e-07+0.00000000e+00j -4.99949587e-04-4.99933130e-07j]\n",
       " [ 4.99949587e-04-4.99933130e-07j -2.49966565e-07+0.00000000e+00j\n",
       "   9.99797770e-01-1.99959837e-03j -4.99949587e-04+4.99933130e-07j]\n",
       " [ 2.49983230e-07+0.00000000e+00j  4.99949587e-04+4.99933130e-07j\n",
       "   4.99949587e-04-4.99933130e-07j  9.99999750e-01+0.00000000e+00j]]"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Lnd(0.5, 0.001)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Major functions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Major functions 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-31T03:40:17.357563Z",
     "start_time": "2019-01-31T03:40:17.353949Z"
    }
   },
   "outputs": [],
   "source": [
    "# building the function to optimize (optimizee)\n",
    "def Lnd_vec(xi_vec, dt):\n",
    "    r\"\"\"Building the vector of differential $Lnd(t)$\"\"\"\n",
    "    Lnd_vec = [Lnd(xi, dt) for xi in xi_vec] \n",
    "    return Lnd_vec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-31T03:40:17.366228Z",
     "start_time": "2019-01-31T03:40:17.362825Z"
    }
   },
   "outputs": [],
   "source": [
    "def fidelity_calc(A, B):\n",
    "    r\"\"\"Making a generalised fidelity function\"\"\"\n",
    "    first_part = (A - B).dag()\n",
    "    second_part = (A - B)\n",
    "    f_int = (first_part* second_part)\n",
    "    f = f_int.tr()\n",
    "    return f"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-31T03:40:17.373209Z",
     "start_time": "2019-01-31T03:40:17.368912Z"
    }
   },
   "outputs": [],
   "source": [
    "def Lnd_full_maker(xi_vec, dt):\n",
    "    r\"\"\"Building the $Lnd(t)$ for the total time $t$\"\"\"\n",
    "    xi_vec_size = xi_vec.size # finding the size of xi\n",
    "    Lnd_full = L_I # Identity for the for loop of Lnd\n",
    "    Lnd_v = Lnd_vec(xi_vec, dt) # calling Lnd_vec\n",
    "    for i in range(xi_vec_size): # generating Lnd_full\n",
    "        Lnd_full = Lnd_full*Lnd_v[xi_vec_size - 1 - i]\n",
    "    return Lnd_full"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-31T03:40:17.378943Z",
     "start_time": "2019-01-31T03:40:17.375397Z"
    }
   },
   "outputs": [],
   "source": [
    "def F(xi_vec, dt):\n",
    "    r\"\"\"Using the fidelity metric to find out the closeness between $T$\n",
    "    and $Lnd(t)$\"\"\"\n",
    "    Lnd_full = Lnd_full_maker(xi_vec, dt)\n",
    "    F = real(-fidelity_calc(T, Lnd_full))   \n",
    "    return F"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Testing major functions 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-31T03:40:17.487426Z",
     "start_time": "2019-01-31T03:40:17.480401Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.0"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fidelity_calc(sigmax(), sigmay())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-31T03:40:17.497825Z",
     "start_time": "2019-01-31T03:40:17.489901Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fidelity_calc(sigmay(), sigmay())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-31T03:40:17.505391Z",
     "start_time": "2019-01-31T03:40:17.500500Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1., 2.])"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xi_vec_test = array([1.0, 2.0])\n",
    "xi_vec_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-31T03:40:17.512116Z",
     "start_time": "2019-01-31T03:40:17.507557Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xi_vec_test.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-31T03:40:17.520448Z",
     "start_time": "2019-01-31T03:40:17.514466Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1.0, 4.0]"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w_vec = [xi**2 for xi in xi_vec_test]\n",
    "w_vec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-31T03:40:17.532987Z",
     "start_time": "2019-01-31T03:40:17.522794Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-7.998400634487144"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# F(xi_vec, dt)\n",
    "F(xi_vec_test, 0.001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-31T03:40:17.542373Z",
     "start_time": "2019-01-31T03:40:17.535094Z"
    }
   },
   "outputs": [],
   "source": [
    "Lnd_v = Lnd_vec(xi_vec_test, 0.001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-31T03:40:17.549782Z",
     "start_time": "2019-01-31T03:40:17.543868Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Quantum object: dims = [[2, 2], [2, 2]], shape = (4, 4), type = oper, isherm = False\n",
       " Qobj data =\n",
       " [[ 9.99999000e-01+0.00000000e+00j -9.99898674e-04-9.99866010e-07j\n",
       "   -9.99898674e-04+9.99866010e-07j  9.99932670e-07+0.00000000e+00j]\n",
       "  [ 9.99898674e-04+9.99866010e-07j  9.99797021e-01+1.99959737e-03j\n",
       "   -9.99866010e-07+0.00000000e+00j -9.99898674e-04-9.99866010e-07j]\n",
       "  [ 9.99898674e-04-9.99866010e-07j -9.99866010e-07+0.00000000e+00j\n",
       "    9.99797021e-01-1.99959737e-03j -9.99898674e-04+9.99866010e-07j]\n",
       "  [ 9.99932670e-07+0.00000000e+00j  9.99898674e-04+9.99866010e-07j\n",
       "    9.99898674e-04-9.99866010e-07j  9.99999000e-01+0.00000000e+00j]],\n",
       " Quantum object: dims = [[2, 2], [2, 2]], shape = (4, 4), type = oper, isherm = False\n",
       " Qobj data =\n",
       " [[ 9.99996000e-01+0.00000000e+00j -1.99979335e-03-1.99973002e-06j\n",
       "   -1.99979335e-03+1.99973002e-06j  3.99972668e-06+0.00000000e+00j]\n",
       "  [ 1.99979335e-03+1.99973002e-06j  9.99794021e-01+1.99959337e-03j\n",
       "   -3.99946004e-06+0.00000000e+00j -1.99979335e-03-1.99973002e-06j]\n",
       "  [ 1.99979335e-03-1.99973002e-06j -3.99946004e-06+0.00000000e+00j\n",
       "    9.99794021e-01-1.99959337e-03j -1.99979335e-03+1.99973002e-06j]\n",
       "  [ 3.99972668e-06+0.00000000e+00j  1.99979335e-03+1.99973002e-06j\n",
       "    1.99979335e-03-1.99973002e-06j  9.99996000e-01+0.00000000e+00j]]]"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Lnd_v"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Major Functions 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-31T03:40:17.633762Z",
     "start_time": "2019-01-31T03:40:17.630238Z"
    }
   },
   "outputs": [],
   "source": [
    "def Lnd_comma_k_maker(xi_vec, k, dt):\n",
    "    r\"\"\"Making of the derivative of full $Lnd(t)$ at time $t_{k}$\"\"\"\n",
    "    N = xi_vec.size \n",
    "    # Determining the size of xi, and thus the time_steps indirectly.\n",
    "    Lnd_v = Lnd_vec(xi_vec, dt)# Making of the full $Lnd(t)$\n",
    "    inner_part = L_I # Beginner for the for loop\n",
    "    for i in range(N):\n",
    "        if i == ( N - 1 - k ):\n",
    "            # The step at which $X_{k}(t)$ has to be inserted \n",
    "            inner_part = inner_part*x_k*Lnd_v[k - 1]\n",
    "        else:\n",
    "            # Usual multiplications of $Lnd_{k}$\n",
    "            inner_part = inner_part*Lnd_v[N - 1 - i]\n",
    "    lnd_comma_k = inner_part\n",
    "    return lnd_comma_k\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-31T03:40:17.649588Z",
     "start_time": "2019-01-31T03:40:17.638971Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/latex": [
       "Quantum object: dims = [[2, 2], [2, 2]], shape = (4, 4), type = oper, isherm = False\\begin{equation*}\\left(\\begin{array}{*{11}c}1.000 & (-0.003-6.998\\times10^{-06}j) & (-0.003+6.998\\times10^{-06}j) & 8.999\\times10^{-06}\\\\(0.003+4.999\\times10^{-06}j) & (1.000+0.004j) & (-8.997\\times10^{-06}+5.998\\times10^{-09}j) & (-0.003-4.999\\times10^{-06}j)\\\\(0.003-4.999\\times10^{-06}j) & (-8.997\\times10^{-06}-5.998\\times10^{-09}j) & (1.000-0.004j) & (-0.003+4.999\\times10^{-06}j)\\\\8.999\\times10^{-06} & (0.003+6.998\\times10^{-06}j) & (0.003-6.998\\times10^{-06}j) & 1.000\\\\\\end{array}\\right)\\end{equation*}"
      ],
      "text/plain": [
       "Quantum object: dims = [[2, 2], [2, 2]], shape = (4, 4), type = oper, isherm = False\n",
       "Qobj data =\n",
       "[[ 9.99991001e-01+0.00000000e+00j -2.99927211e-03-6.99796565e-06j\n",
       "  -2.99927211e-03+6.99796565e-06j  8.99882878e-06+0.00000000e+00j]\n",
       " [ 2.99947607e-03+4.99878084e-06j  9.99583086e-01+3.99836500e-03j\n",
       "  -8.99749372e-06+5.99798035e-09j -2.99947607e-03-4.99878084e-06j]\n",
       " [ 2.99947607e-03-4.99878084e-06j -8.99749372e-06-5.99798035e-09j\n",
       "   9.99583086e-01-3.99836500e-03j -2.99947607e-03+4.99878084e-06j]\n",
       " [ 8.99882878e-06+0.00000000e+00j  2.99927211e-03+6.99796565e-06j\n",
       "   2.99927211e-03-6.99796565e-06j  9.99991001e-01+0.00000000e+00j]]"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Lnd_comma_k_maker(xi_vec, k, dt)\n",
    "Lnd_comma_k_maker(xi_vec_test, 2, 0.001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-31T03:40:17.656013Z",
     "start_time": "2019-01-31T03:40:17.651363Z"
    }
   },
   "outputs": [],
   "source": [
    "def updater_nd(xi_vec, dt, epsilon):\n",
    "    r\"\"\"Implementing the GRAPE update step\"\"\"\n",
    "    xi_vec_size = xi_vec.size # finding the size of xi\n",
    "    Lnd_full = Lnd_full_maker(xi_vec, dt)\n",
    "    di = []\n",
    "    for k in range(xi_vec_size):\n",
    "        # Building the thing to be added to the old function\n",
    "        Lnd_comma_k = Lnd_comma_k_maker(xi_vec, k, dt)\n",
    "        differentiated = T - Lnd_comma_k\n",
    "        plain = T - Lnd_full\n",
    "        c = -differentiated.dag()*plain\n",
    "        d = -plain.dag()*differentiated\n",
    "        inside = c.tr() + d.tr()\n",
    "        di.append(epsilon*inside)\n",
    "\n",
    "    diff = array(di)\n",
    "    xi_new_vec = xi_vec + diff\n",
    "    return diff, xi_new_vec\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-31T03:40:17.675523Z",
     "start_time": "2019-01-31T03:40:17.658347Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([-0.008+0.j, -0.008+0.j]), array([0.992+0.j, 1.992+0.j]))"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  updater_nd(xi_vec, dt, epsilon)\n",
    "updater_nd(xi_vec_test, 0.001, 0.001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-31T03:40:17.682150Z",
     "start_time": "2019-01-31T03:40:17.677236Z"
    },
    "code_folding": []
   },
   "outputs": [],
   "source": [
    "def terminator_nd(max_iter, time_steps, total_time, epsilon):\n",
    "    r\"\"\"Brief description of the function\"\"\"\n",
    "    \n",
    "    xi_initial =  1000*random_sample((time_steps,))\n",
    "    dt = total_time/time_steps\n",
    "    xi_diff, xi_new_vec = updater_nd(xi_initial, dt, epsilon)\n",
    "    \n",
    "    for i in range(max_iter):\n",
    "        if amax(xi_diff) < epsilon**2 :\n",
    "            xi_final = xi_new_vec\n",
    "            break\n",
    "        else :\n",
    "            xi_diff, xi_new_vec = updater_nd(xi_new_vec, dt, epsilon)\n",
    "            print(i)\n",
    "            print(amax(xi_diff))\n",
    "            \n",
    "        \n",
    "    xi_final = xi_new_vec    \n",
    "    return xi_final"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# code with dissipation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Building parts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Building $A(t)$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-31T03:40:18.002803Z",
     "start_time": "2019-01-31T03:40:18.000406Z"
    }
   },
   "outputs": [],
   "source": [
    "def A(xi):\n",
    "    r\"\"\"making $A(t)$\"\"\"\n",
    "    A = ih0 - h0ci + xi*(ih1 - h1ci) + lindbladian\n",
    "    return A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-31T03:40:18.019979Z",
     "start_time": "2019-01-31T03:40:18.012431Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/latex": [
       "Quantum object: dims = [[2, 2], [2, 2]], shape = (4, 4), type = oper, isherm = False\\begin{equation*}\\left(\\begin{array}{*{11}c}0.0 & -0.500j & -0.500j & 0.0\\\\0.500j & (-2.0-0.200j) & 0.0 & -0.500j\\\\0.500j & 0.0 & (2.0-0.200j) & -0.500j\\\\0.0 & 0.500j & 0.500j & 0.0\\\\\\end{array}\\right)\\end{equation*}"
      ],
      "text/plain": [
       "Quantum object: dims = [[2, 2], [2, 2]], shape = (4, 4), type = oper, isherm = False\n",
       "Qobj data =\n",
       "[[ 0.+0.j   0.-0.5j  0.-0.5j  0.+0.j ]\n",
       " [ 0.+0.5j -2.-0.2j  0.+0.j   0.-0.5j]\n",
       " [ 0.+0.5j  0.+0.j   2.-0.2j  0.-0.5j]\n",
       " [ 0.+0.j   0.+0.5j  0.+0.5j  0.+0.j ]]"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A(0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Building $L(t)$ and the Identity in the Liouville space"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-31T03:40:18.123024Z",
     "start_time": "2019-01-31T03:40:18.120453Z"
    }
   },
   "outputs": [],
   "source": [
    "def L(xi, dt):\n",
    "    r\"\"\"Making $L(t) from $A(t)$\"\"\"\n",
    "    L = (-1j*A(xi)*dt).expm()\n",
    "    return L"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-31T03:40:18.137648Z",
     "start_time": "2019-01-31T03:40:18.129091Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/latex": [
       "Quantum object: dims = [[2, 2], [2, 2]], shape = (4, 4), type = oper, isherm = False\\begin{equation*}\\left(\\begin{array}{*{11}c}1.000 & (-4.999\\times10^{-04}-4.999\\times10^{-07}j) & (-4.999\\times10^{-04}+4.999\\times10^{-07}j) & 2.500\\times10^{-07}\\\\(4.999\\times10^{-04}+4.999\\times10^{-07}j) & (1.000+0.002j) & -2.500\\times10^{-07} & (-4.999\\times10^{-04}-4.999\\times10^{-07}j)\\\\(4.999\\times10^{-04}-4.999\\times10^{-07}j) & -2.500\\times10^{-07} & (1.000-0.002j) & (-4.999\\times10^{-04}+4.999\\times10^{-07}j)\\\\2.500\\times10^{-07} & (4.999\\times10^{-04}+4.999\\times10^{-07}j) & (4.999\\times10^{-04}-4.999\\times10^{-07}j) & 1.000\\\\\\end{array}\\right)\\end{equation*}"
      ],
      "text/plain": [
       "Quantum object: dims = [[2, 2], [2, 2]], shape = (4, 4), type = oper, isherm = False\n",
       "Qobj data =\n",
       "[[ 9.99999750e-01+0.00000000e+00j -4.99949587e-04-4.99933130e-07j\n",
       "  -4.99949587e-04+4.99933130e-07j  2.49983230e-07+0.00000000e+00j]\n",
       " [ 4.99949587e-04+4.99933130e-07j  9.99797770e-01+1.99959837e-03j\n",
       "  -2.49966565e-07+0.00000000e+00j -4.99949587e-04-4.99933130e-07j]\n",
       " [ 4.99949587e-04-4.99933130e-07j -2.49966565e-07+0.00000000e+00j\n",
       "   9.99797770e-01-1.99959837e-03j -4.99949587e-04+4.99933130e-07j]\n",
       " [ 2.49983230e-07+0.00000000e+00j  4.99949587e-04+4.99933130e-07j\n",
       "   4.99949587e-04-4.99933130e-07j  9.99999750e-01+0.00000000e+00j]]"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "L(0.5, 0.001)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Major functions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Major functions 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-31T03:40:18.353279Z",
     "start_time": "2019-01-31T03:40:18.350492Z"
    }
   },
   "outputs": [],
   "source": [
    "# building the function to optimize (optimizee)\n",
    "def L_vec(xi_vec, dt):\n",
    "    r\"\"\"Building the vector of differential $L(t)$\"\"\"\n",
    "    L_vec = [L(xi, dt) for xi in xi_vec] \n",
    "    return L_vec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-31T03:40:18.359197Z",
     "start_time": "2019-01-31T03:40:18.355570Z"
    }
   },
   "outputs": [],
   "source": [
    "def fidelity_calc(A, B):\n",
    "    r\"\"\"Making a generalised fidelity function\"\"\"\n",
    "    first_part = (A - B).dag()\n",
    "    second_part = (A - B)\n",
    "    f_int = (first_part* second_part)\n",
    "    f = f_int.tr()\n",
    "    return f"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-31T03:40:18.364878Z",
     "start_time": "2019-01-31T03:40:18.361146Z"
    }
   },
   "outputs": [],
   "source": [
    "def L_full_maker(xi_vec, dt):\n",
    "    r\"\"\"Building the $L(t)$ for the total time $t$\"\"\"\n",
    "    xi_vec_size = xi_vec.size # finding the size of xi\n",
    "    L_full = L_I # Identity for the for loop of L\n",
    "    L_v = L_vec(xi_vec, dt) # calling L_vec\n",
    "    for i in range(xi_vec_size): # generating L_full\n",
    "        L_full = L_full*L_v[xi_vec_size - 1 - i]\n",
    "    return L_full"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-31T03:40:18.372659Z",
     "start_time": "2019-01-31T03:40:18.369671Z"
    }
   },
   "outputs": [],
   "source": [
    "def F(xi_vec, dt):\n",
    "    r\"\"\"Using the fidelity metric to find out the closeness between $T$\n",
    "    and $L(t)$\"\"\"\n",
    "    L_full = L_full_maker(xi_vec, dt)\n",
    "    F = real(-fidelity_calc(T, L_full))   \n",
    "    return F"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Testing major functions 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-31T03:40:18.484707Z",
     "start_time": "2019-01-31T03:40:18.477922Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.0"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fidelity_calc(sigmax(), sigmay())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-31T03:40:18.492683Z",
     "start_time": "2019-01-31T03:40:18.486792Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fidelity_calc(sigmay(), sigmay())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-31T03:40:18.501127Z",
     "start_time": "2019-01-31T03:40:18.495168Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1., 2.])"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xi_vec_test = array([1.0, 2.0])\n",
    "xi_vec_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-31T03:40:18.507456Z",
     "start_time": "2019-01-31T03:40:18.503304Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xi_vec_test.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-31T03:40:18.514794Z",
     "start_time": "2019-01-31T03:40:18.509328Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1.0, 4.0]"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w_vec = [xi**2 for xi in xi_vec_test]\n",
    "w_vec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-31T03:40:18.527425Z",
     "start_time": "2019-01-31T03:40:18.517566Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-7.998400634487144"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# F(xi_vec, dt)\n",
    "F(xi_vec_test, 0.001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-31T03:40:18.536423Z",
     "start_time": "2019-01-31T03:40:18.529196Z"
    }
   },
   "outputs": [],
   "source": [
    "L_v = L_vec(xi_vec_test, 0.001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-31T03:40:18.543997Z",
     "start_time": "2019-01-31T03:40:18.538408Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Quantum object: dims = [[2, 2], [2, 2]], shape = (4, 4), type = oper, isherm = False\n",
       " Qobj data =\n",
       " [[ 9.99999000e-01+0.00000000e+00j -9.99898674e-04-9.99866010e-07j\n",
       "   -9.99898674e-04+9.99866010e-07j  9.99932670e-07+0.00000000e+00j]\n",
       "  [ 9.99898674e-04+9.99866010e-07j  9.99797021e-01+1.99959737e-03j\n",
       "   -9.99866010e-07+0.00000000e+00j -9.99898674e-04-9.99866010e-07j]\n",
       "  [ 9.99898674e-04-9.99866010e-07j -9.99866010e-07+0.00000000e+00j\n",
       "    9.99797021e-01-1.99959737e-03j -9.99898674e-04+9.99866010e-07j]\n",
       "  [ 9.99932670e-07+0.00000000e+00j  9.99898674e-04+9.99866010e-07j\n",
       "    9.99898674e-04-9.99866010e-07j  9.99999000e-01+0.00000000e+00j]],\n",
       " Quantum object: dims = [[2, 2], [2, 2]], shape = (4, 4), type = oper, isherm = False\n",
       " Qobj data =\n",
       " [[ 9.99996000e-01+0.00000000e+00j -1.99979335e-03-1.99973002e-06j\n",
       "   -1.99979335e-03+1.99973002e-06j  3.99972668e-06+0.00000000e+00j]\n",
       "  [ 1.99979335e-03+1.99973002e-06j  9.99794021e-01+1.99959337e-03j\n",
       "   -3.99946004e-06+0.00000000e+00j -1.99979335e-03-1.99973002e-06j]\n",
       "  [ 1.99979335e-03-1.99973002e-06j -3.99946004e-06+0.00000000e+00j\n",
       "    9.99794021e-01-1.99959337e-03j -1.99979335e-03+1.99973002e-06j]\n",
       "  [ 3.99972668e-06+0.00000000e+00j  1.99979335e-03+1.99973002e-06j\n",
       "    1.99979335e-03-1.99973002e-06j  9.99996000e-01+0.00000000e+00j]]]"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "L_v"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Major Functions 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-31T03:40:18.637750Z",
     "start_time": "2019-01-31T03:40:18.634224Z"
    }
   },
   "outputs": [],
   "source": [
    "def L_comma_k_maker(xi_vec, k, dt):\n",
    "    r\"\"\"Making of the derivative of full $L(t)$ at time $t_{k}$\"\"\"\n",
    "    N = xi_vec.size \n",
    "    # Determining the size of xi, and thus the time_steps indirectly.\n",
    "    L_v = L_vec(xi_vec, dt)# Making of the full $L(t)$\n",
    "    inner_part = L_I # Beginner for the for loop\n",
    "    for i in range(N):\n",
    "        if i == ( N - 1 - k ):\n",
    "            # The step at which $X_{k}(t)$ has to be inserted \n",
    "            inner_part = inner_part*x_k*L_v[k - 1]\n",
    "        else:\n",
    "            # Usual multiplications of $L_{k}$\n",
    "            inner_part = inner_part*L_v[N - 1 - i]\n",
    "    l_comma_k = inner_part\n",
    "    return l_comma_k\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-31T03:40:18.653313Z",
     "start_time": "2019-01-31T03:40:18.643046Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/latex": [
       "Quantum object: dims = [[2, 2], [2, 2]], shape = (4, 4), type = oper, isherm = False\\begin{equation*}\\left(\\begin{array}{*{11}c}1.000 & (-0.003-6.998\\times10^{-06}j) & (-0.003+6.998\\times10^{-06}j) & 8.999\\times10^{-06}\\\\(0.003+4.999\\times10^{-06}j) & (1.000+0.004j) & (-8.997\\times10^{-06}+5.998\\times10^{-09}j) & (-0.003-4.999\\times10^{-06}j)\\\\(0.003-4.999\\times10^{-06}j) & (-8.997\\times10^{-06}-5.998\\times10^{-09}j) & (1.000-0.004j) & (-0.003+4.999\\times10^{-06}j)\\\\8.999\\times10^{-06} & (0.003+6.998\\times10^{-06}j) & (0.003-6.998\\times10^{-06}j) & 1.000\\\\\\end{array}\\right)\\end{equation*}"
      ],
      "text/plain": [
       "Quantum object: dims = [[2, 2], [2, 2]], shape = (4, 4), type = oper, isherm = False\n",
       "Qobj data =\n",
       "[[ 9.99991001e-01+0.00000000e+00j -2.99927211e-03-6.99796565e-06j\n",
       "  -2.99927211e-03+6.99796565e-06j  8.99882878e-06+0.00000000e+00j]\n",
       " [ 2.99947607e-03+4.99878084e-06j  9.99583086e-01+3.99836500e-03j\n",
       "  -8.99749372e-06+5.99798035e-09j -2.99947607e-03-4.99878084e-06j]\n",
       " [ 2.99947607e-03-4.99878084e-06j -8.99749372e-06-5.99798035e-09j\n",
       "   9.99583086e-01-3.99836500e-03j -2.99947607e-03+4.99878084e-06j]\n",
       " [ 8.99882878e-06+0.00000000e+00j  2.99927211e-03+6.99796565e-06j\n",
       "   2.99927211e-03-6.99796565e-06j  9.99991001e-01+0.00000000e+00j]]"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# L_comma_k_maker(xi_vec, k, dt)\n",
    "L_comma_k_maker(xi_vec_test, 2, 0.001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-31T03:40:18.660372Z",
     "start_time": "2019-01-31T03:40:18.655570Z"
    }
   },
   "outputs": [],
   "source": [
    "def updater(xi_vec, dt, epsilon):\n",
    "    r\"\"\"Implementing the GRAPE update step\"\"\"\n",
    "    xi_vec_size = xi_vec.size # finding the size of xi\n",
    "    L_full = L_full_maker(xi_vec, dt)\n",
    "    di = []\n",
    "    for k in range(xi_vec_size):\n",
    "        # Building the thing to be added to the old function\n",
    "        L_comma_k = L_comma_k_maker(xi_vec, k, dt)\n",
    "        differentiated = T - L_comma_k\n",
    "        plain = T - L_full\n",
    "        c = -differentiated.dag()*plain\n",
    "        d = -plain.dag()*differentiated\n",
    "        inside = c.tr() + d.tr()\n",
    "        di.append(epsilon*inside)\n",
    "\n",
    "    diff = array(di)\n",
    "    xi_new_vec = xi_vec + diff\n",
    "    return diff, xi_new_vec\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-31T03:40:18.680184Z",
     "start_time": "2019-01-31T03:40:18.662516Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([-0.008+0.j, -0.008+0.j]), array([0.992+0.j, 1.992+0.j]))"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  updater(xi_vec, dt, epsilon)\n",
    "updater(xi_vec_test, 0.001, 0.001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-31T03:40:18.685853Z",
     "start_time": "2019-01-31T03:40:18.681947Z"
    },
    "code_folding": []
   },
   "outputs": [],
   "source": [
    "def terminator(max_iter, time_steps, total_time, epsilon):\n",
    "    r\"\"\"Brief description of the function\"\"\"\n",
    "    \n",
    "    xi_initial = terminator_nd(max_iter, time_steps, total_time, epsilon)\n",
    "    #1000*random_sample((time_steps,))\n",
    "    dt = total_time/time_steps\n",
    "    xi_diff, xi_new_vec = updater(xi_initial, dt, epsilon)\n",
    "    \n",
    "    for i in range(max_iter):\n",
    "        if amax(xi_diff) < epsilon**2 :\n",
    "            xi_final = xi_new_vec\n",
    "            break\n",
    "        else :\n",
    "            xi_diff, xi_new_vec = updater(xi_new_vec, dt, epsilon)\n",
    "            print(i)\n",
    "            print(amax(xi_diff))\n",
    "            \n",
    "        \n",
    "    xi_final = xi_new_vec    \n",
    "    return xi_final"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Running stuff"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Try1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-31T03:40:18.959889Z",
     "start_time": "2019-01-31T03:40:18.956904Z"
    }
   },
   "outputs": [],
   "source": [
    "total_time = pi/omega_1\n",
    "epsilon = 10**(-6)\n",
    "max_iter = 10#10**4#1000#100#50#20\n",
    "time_steps = 20\n",
    "dt = total_time/time_steps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-31T03:40:20.451122Z",
     "start_time": "2019-01-31T03:40:18.962640Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([664.44707966+0.j, 378.84550639+0.j, 107.00126181+0.j,\n",
       "       727.28162685+0.j, 182.44178593+0.j, 729.25909561+0.j,\n",
       "       193.8722369 +0.j, 429.06197952+0.j, 584.25862923+0.j,\n",
       "       732.69905518+0.j, 570.20255188+0.j, 441.77527779+0.j,\n",
       "       721.05678317+0.j, 479.53941961+0.j,  77.30110438+0.j,\n",
       "       412.8307956 +0.j, 109.21045877+0.j, 622.60098639+0.j,\n",
       "       565.68743914+0.j, 565.49503273+0.j])"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xi_opt = terminator(max_iter, time_steps, total_time, epsilon)\n",
    "xi_opt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-31T03:40:20.491848Z",
     "start_time": "2019-01-31T03:40:20.453468Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-4.219386244073984"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "F(xi_opt, dt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-31T03:40:20.497859Z",
     "start_time": "2019-01-31T03:40:20.493680Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max_iter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-31T03:40:20.506472Z",
     "start_time": "2019-01-31T03:40:20.500094Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/latex": [
       "Quantum object: dims = [[2, 2], [2, 2]], shape = (4, 4), type = oper, isherm = True\\begin{equation*}\\left(\\begin{array}{*{11}c}0.0 & 0.0 & 0.0 & 1.0\\\\0.0 & 0.0 & 1.0 & 0.0\\\\0.0 & 1.0 & 0.0 & 0.0\\\\1.0 & 0.0 & 0.0 & 0.0\\\\\\end{array}\\right)\\end{equation*}"
      ],
      "text/plain": [
       "Quantum object: dims = [[2, 2], [2, 2]], shape = (4, 4), type = oper, isherm = True\n",
       "Qobj data =\n",
       "[[0. 0. 0. 1.]\n",
       " [0. 0. 1. 0.]\n",
       " [0. 1. 0. 0.]\n",
       " [1. 0. 0. 0.]]"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-31T03:40:20.548520Z",
     "start_time": "2019-01-31T03:40:20.508492Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/latex": [
       "Quantum object: dims = [[2, 2], [2, 2]], shape = (4, 4), type = oper, isherm = False\\begin{equation*}\\left(\\begin{array}{*{11}c}0.234 & (-0.016+0.003j) & (-0.016-0.003j) & 0.766\\\\(0.016-0.003j) & (-0.124-0.001j) & (-0.409-0.005j) & (-0.016+0.003j)\\\\(0.016+0.003j) & (-0.409+0.005j) & (-0.124+0.001j) & (-0.016-0.003j)\\\\0.766 & (0.016-0.003j) & (0.016+0.003j) & 0.234\\\\\\end{array}\\right)\\end{equation*}"
      ],
      "text/plain": [
       "Quantum object: dims = [[2, 2], [2, 2]], shape = (4, 4), type = oper, isherm = False\n",
       "Qobj data =\n",
       "[[ 0.23379157+0.j         -0.01645227+0.0034021j  -0.01645227-0.0034021j\n",
       "   0.76620843+0.j        ]\n",
       " [ 0.01608122-0.00275066j -0.12394731-0.00107552j -0.408507  -0.00481791j\n",
       "  -0.01608122+0.00275066j]\n",
       " [ 0.01608122+0.00275066j -0.408507  +0.00481791j -0.12394731+0.00107552j\n",
       "  -0.01608122-0.00275066j]\n",
       " [ 0.76620843+0.j          0.01645227-0.0034021j   0.01645227+0.0034021j\n",
       "   0.23379157+0.j        ]]"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "L_full_maker(xi_opt, dt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-31T03:40:20.837549Z",
     "start_time": "2019-01-31T03:40:20.550527Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Figure size 576x432 with 2 Axes>,\n",
       " <matplotlib.axes._subplots.AxesSubplot at 0x118d26278>)"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hinton(L_full_maker(xi_opt, dt))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Try2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-31T03:40:20.842970Z",
     "start_time": "2019-01-31T03:40:20.839755Z"
    }
   },
   "outputs": [],
   "source": [
    "total_time = 3*pi/omega_1\n",
    "epsilon = 10**(-6)\n",
    "max_iter = 10#10**4#1000#100#50#20\n",
    "time_steps = 20\n",
    "dt = total_time/time_steps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-31T03:40:22.357549Z",
     "start_time": "2019-01-31T03:40:20.845288Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([789.75374067+0.j, 556.96868729+0.j, 673.29548007+0.j,\n",
       "       937.676596  +0.j, 435.41258631+0.j, 395.82353912+0.j,\n",
       "       419.18694666+0.j, 452.63354314+0.j, 978.87088382+0.j,\n",
       "       560.17413249+0.j, 242.34209716+0.j, 517.85472488+0.j,\n",
       "       136.72789654+0.j, 515.55371161+0.j,  18.50448957+0.j,\n",
       "       831.54123599+0.j, 376.12662475+0.j, 338.893785  +0.j,\n",
       "       999.51140249+0.j, 487.11898296+0.j])"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xi_opt = terminator(max_iter, time_steps, total_time, epsilon)\n",
    "xi_opt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-31T03:40:22.398265Z",
     "start_time": "2019-01-31T03:40:22.359320Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-3.092416218266722"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "F(xi_opt, dt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-31T03:40:22.404589Z",
     "start_time": "2019-01-31T03:40:22.400404Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max_iter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-31T03:40:22.412031Z",
     "start_time": "2019-01-31T03:40:22.406542Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/latex": [
       "Quantum object: dims = [[2, 2], [2, 2]], shape = (4, 4), type = oper, isherm = True\\begin{equation*}\\left(\\begin{array}{*{11}c}0.0 & 0.0 & 0.0 & 1.0\\\\0.0 & 0.0 & 1.0 & 0.0\\\\0.0 & 1.0 & 0.0 & 0.0\\\\1.0 & 0.0 & 0.0 & 0.0\\\\\\end{array}\\right)\\end{equation*}"
      ],
      "text/plain": [
       "Quantum object: dims = [[2, 2], [2, 2]], shape = (4, 4), type = oper, isherm = True\n",
       "Qobj data =\n",
       "[[0. 0. 0. 1.]\n",
       " [0. 0. 1. 0.]\n",
       " [0. 1. 0. 0.]\n",
       " [1. 0. 0. 0.]]"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-31T03:40:22.455643Z",
     "start_time": "2019-01-31T03:40:22.414698Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/latex": [
       "Quantum object: dims = [[2, 2], [2, 2]], shape = (4, 4), type = oper, isherm = False\\begin{equation*}\\left(\\begin{array}{*{11}c}0.521 & (-0.073-0.002j) & (-0.073+0.002j) & 0.479\\\\(0.073-8.099\\times10^{-04}j) & (0.032+0.005j) & (0.009+0.005j) & (-0.073+8.099\\times10^{-04}j)\\\\(0.073+8.099\\times10^{-04}j) & (0.009-0.005j) & (0.032-0.005j) & (-0.073-8.099\\times10^{-04}j)\\\\0.479 & (0.073+0.002j) & (0.073-0.002j) & 0.521\\\\\\end{array}\\right)\\end{equation*}"
      ],
      "text/plain": [
       "Quantum object: dims = [[2, 2], [2, 2]], shape = (4, 4), type = oper, isherm = False\n",
       "Qobj data =\n",
       "[[ 0.52072137+0.j         -0.07265601-0.00201343j -0.07265601+0.00201343j\n",
       "   0.47927863+0.j        ]\n",
       " [ 0.07294427-0.00080992j  0.0321609 +0.00523571j  0.00923518+0.00458431j\n",
       "  -0.07294427+0.00080992j]\n",
       " [ 0.07294427+0.00080992j  0.00923518-0.00458431j  0.0321609 -0.00523571j\n",
       "  -0.07294427-0.00080992j]\n",
       " [ 0.47927863+0.j          0.07265601+0.00201343j  0.07265601-0.00201343j\n",
       "   0.52072137+0.j        ]]"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "L_full_maker(xi_opt, dt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-31T03:40:22.733541Z",
     "start_time": "2019-01-31T03:40:22.457796Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Figure size 576x432 with 2 Axes>,\n",
       " <matplotlib.axes._subplots.AxesSubplot at 0x119461390>)"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hinton(L_full_maker(xi_opt, dt))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Try2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-31T03:40:22.739247Z",
     "start_time": "2019-01-31T03:40:22.735348Z"
    }
   },
   "outputs": [],
   "source": [
    "total_time = 4*pi/omega_1\n",
    "epsilon = 10**(-6)\n",
    "max_iter = 10#10**4#1000#100#50#20\n",
    "time_steps = 20\n",
    "dt = total_time/time_steps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-31T03:40:24.203368Z",
     "start_time": "2019-01-31T03:40:22.743376Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([921.5702214 +0.j, 756.62390835+0.j,  65.95312248+0.j,\n",
       "       573.47364336+0.j, 106.91379795+0.j, 255.4183076 +0.j,\n",
       "       703.75718151+0.j, 513.46546795+0.j, 854.34191328+0.j,\n",
       "       432.34206428+0.j, 530.95697353+0.j, 212.40197576+0.j,\n",
       "       781.41034017+0.j, 274.0190033 +0.j, 858.41249586+0.j,\n",
       "       348.83942537+0.j, 254.12661776+0.j,  51.85833269+0.j,\n",
       "         5.22903744+0.j, 629.88884338+0.j])"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xi_opt = terminator(max_iter, time_steps, total_time, epsilon)\n",
    "xi_opt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-31T03:40:24.243846Z",
     "start_time": "2019-01-31T03:40:24.205398Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-3.023404166032716"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "F(xi_opt, dt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-31T03:40:24.249742Z",
     "start_time": "2019-01-31T03:40:24.245844Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max_iter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-31T03:40:24.258116Z",
     "start_time": "2019-01-31T03:40:24.252497Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/latex": [
       "Quantum object: dims = [[2, 2], [2, 2]], shape = (4, 4), type = oper, isherm = True\\begin{equation*}\\left(\\begin{array}{*{11}c}0.0 & 0.0 & 0.0 & 1.0\\\\0.0 & 0.0 & 1.0 & 0.0\\\\0.0 & 1.0 & 0.0 & 0.0\\\\1.0 & 0.0 & 0.0 & 0.0\\\\\\end{array}\\right)\\end{equation*}"
      ],
      "text/plain": [
       "Quantum object: dims = [[2, 2], [2, 2]], shape = (4, 4), type = oper, isherm = True\n",
       "Qobj data =\n",
       "[[0. 0. 0. 1.]\n",
       " [0. 0. 1. 0.]\n",
       " [0. 1. 0. 0.]\n",
       " [1. 0. 0. 0.]]"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-31T03:40:24.313554Z",
     "start_time": "2019-01-31T03:40:24.260063Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/latex": [
       "Quantum object: dims = [[2, 2], [2, 2]], shape = (4, 4), type = oper, isherm = False\\begin{equation*}\\left(\\begin{array}{*{11}c}0.461 & (-0.012+6.345\\times10^{-05}j) & (-0.012-6.345\\times10^{-05}j) & 0.539\\\\(0.011+0.003j) & (-0.035-0.002j) & (-0.041-0.005j) & (-0.011-0.003j)\\\\(0.011-0.003j) & (-0.041+0.005j) & (-0.035+0.002j) & (-0.011+0.003j)\\\\0.539 & (0.012-6.345\\times10^{-05}j) & (0.012+6.345\\times10^{-05}j) & 0.461\\\\\\end{array}\\right)\\end{equation*}"
      ],
      "text/plain": [
       "Quantum object: dims = [[2, 2], [2, 2]], shape = (4, 4), type = oper, isherm = False\n",
       "Qobj data =\n",
       "[[ 0.46134345+0.00000000e+00j -0.01188662+6.34450600e-05j\n",
       "  -0.01188662-6.34450600e-05j  0.53865655+0.00000000e+00j]\n",
       " [ 0.01144117+2.61278484e-03j -0.03486451-2.32443413e-03j\n",
       "  -0.04125944-5.13025647e-03j -0.01144117-2.61278484e-03j]\n",
       " [ 0.01144117-2.61278484e-03j -0.04125944+5.13025647e-03j\n",
       "  -0.03486451+2.32443413e-03j -0.01144117+2.61278484e-03j]\n",
       " [ 0.53865655+0.00000000e+00j  0.01188662-6.34450600e-05j\n",
       "   0.01188662+6.34450600e-05j  0.46134345+0.00000000e+00j]]"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "L_full_maker(xi_opt, dt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-31T03:40:24.655291Z",
     "start_time": "2019-01-31T03:40:24.316017Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Figure size 576x432 with 2 Axes>,\n",
       " <matplotlib.axes._subplots.AxesSubplot at 0x1a22ec5d68>)"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hinton(L_full_maker(xi_opt, dt))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Try2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-31T03:40:24.659988Z",
     "start_time": "2019-01-31T03:40:24.656989Z"
    }
   },
   "outputs": [],
   "source": [
    "total_time = 5*pi/omega_1\n",
    "epsilon = 10**(-6)\n",
    "max_iter = 10#10**4#1000#100#50#20\n",
    "time_steps = 20\n",
    "dt = total_time/time_steps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-31T03:40:26.096632Z",
     "start_time": "2019-01-31T03:40:24.662344Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([130.8209312 +0.j, 884.89862915+0.j, 959.20321219+0.j,\n",
       "       498.29494498+0.j, 355.84298734+0.j, 568.20249344+0.j,\n",
       "       923.16070827+0.j, 844.81458222+0.j, 241.83831399+0.j,\n",
       "       721.92677815+0.j, 499.08316577+0.j, 889.29270742+0.j,\n",
       "       449.72399213+0.j, 435.78788133+0.j, 278.47756219+0.j,\n",
       "       973.90977039+0.j, 367.88713662+0.j, 554.77033809+0.j,\n",
       "       377.57633898+0.j, 541.75842192+0.j])"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xi_opt = terminator(max_iter, time_steps, total_time, epsilon)\n",
    "xi_opt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-31T03:40:26.137234Z",
     "start_time": "2019-01-31T03:40:26.099087Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-3.007462360424914"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "F(xi_opt, dt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-31T03:40:26.143719Z",
     "start_time": "2019-01-31T03:40:26.139376Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max_iter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-31T03:40:26.151675Z",
     "start_time": "2019-01-31T03:40:26.145552Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/latex": [
       "Quantum object: dims = [[2, 2], [2, 2]], shape = (4, 4), type = oper, isherm = True\\begin{equation*}\\left(\\begin{array}{*{11}c}0.0 & 0.0 & 0.0 & 1.0\\\\0.0 & 0.0 & 1.0 & 0.0\\\\0.0 & 1.0 & 0.0 & 0.0\\\\1.0 & 0.0 & 0.0 & 0.0\\\\\\end{array}\\right)\\end{equation*}"
      ],
      "text/plain": [
       "Quantum object: dims = [[2, 2], [2, 2]], shape = (4, 4), type = oper, isherm = True\n",
       "Qobj data =\n",
       "[[0. 0. 0. 1.]\n",
       " [0. 0. 1. 0.]\n",
       " [0. 1. 0. 0.]\n",
       " [1. 0. 0. 0.]]"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-31T03:40:26.193088Z",
     "start_time": "2019-01-31T03:40:26.153719Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/latex": [
       "Quantum object: dims = [[2, 2], [2, 2]], shape = (4, 4), type = oper, isherm = False\\begin{equation*}\\left(\\begin{array}{*{11}c}0.487 & (0.017-1.021\\times10^{-04}j) & (0.017+1.021\\times10^{-04}j) & 0.513\\\\(-0.017+4.004\\times10^{-05}j) & (-0.012-2.908\\times10^{-04}j) & (-0.014+1.940\\times10^{-04}j) & (0.017-4.004\\times10^{-05}j)\\\\(-0.017-4.004\\times10^{-05}j) & (-0.014-1.940\\times10^{-04}j) & (-0.012+2.908\\times10^{-04}j) & (0.017+4.004\\times10^{-05}j)\\\\0.513 & (-0.017+1.021\\times10^{-04}j) & (-0.017-1.021\\times10^{-04}j) & 0.487\\\\\\end{array}\\right)\\end{equation*}"
      ],
      "text/plain": [
       "Quantum object: dims = [[2, 2], [2, 2]], shape = (4, 4), type = oper, isherm = False\n",
       "Qobj data =\n",
       "[[ 0.48694859+0.00000000e+00j  0.01722313-1.02067074e-04j\n",
       "   0.01722313+1.02067074e-04j  0.51305141+0.00000000e+00j]\n",
       " [-0.01721472+4.00382681e-05j -0.01211542-2.90776019e-04j\n",
       "  -0.01398246+1.94049302e-04j  0.01721472-4.00382681e-05j]\n",
       " [-0.01721472-4.00382681e-05j -0.01398246-1.94049302e-04j\n",
       "  -0.01211542+2.90776019e-04j  0.01721472+4.00382681e-05j]\n",
       " [ 0.51305141+0.00000000e+00j -0.01722313+1.02067074e-04j\n",
       "  -0.01722313-1.02067074e-04j  0.48694859+0.00000000e+00j]]"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "L_full_maker(xi_opt, dt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-31T03:40:26.460504Z",
     "start_time": "2019-01-31T03:40:26.194787Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Figure size 576x432 with 2 Axes>,\n",
       " <matplotlib.axes._subplots.AxesSubplot at 0x1a23221f98>)"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hinton(L_full_maker(xi_opt, dt))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Try2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-31T03:40:26.466850Z",
     "start_time": "2019-01-31T03:40:26.463600Z"
    }
   },
   "outputs": [],
   "source": [
    "total_time = 10*pi/omega_1\n",
    "epsilon = 10**(-6)\n",
    "max_iter = 10#10**4#1000#100#50#20\n",
    "time_steps = 20\n",
    "dt = total_time/time_steps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-31T03:40:27.935487Z",
     "start_time": "2019-01-31T03:40:26.469393Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([223.7274885 +0.j, 639.62630204+0.j, 541.48785948+0.j,\n",
       "       751.58436922+0.j, 764.8426518 +0.j, 296.00922486+0.j,\n",
       "       126.15714927+0.j, 386.65578067+0.j, 373.67108782+0.j,\n",
       "       551.03998792+0.j, 322.46822048+0.j, 896.89556233+0.j,\n",
       "       743.82172746+0.j, 735.07660576+0.j, 339.24210679+0.j,\n",
       "        86.60209728+0.j, 456.08411937+0.j, 310.91369033+0.j,\n",
       "       512.73578685+0.j, 233.64748357+0.j])"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xi_opt = terminator(max_iter, time_steps, total_time, epsilon)\n",
    "xi_opt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-31T03:40:27.977340Z",
     "start_time": "2019-01-31T03:40:27.937751Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-3.000013218736381"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "F(xi_opt, dt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-31T03:40:27.983349Z",
     "start_time": "2019-01-31T03:40:27.979001Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max_iter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-31T03:40:27.990916Z",
     "start_time": "2019-01-31T03:40:27.985571Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/latex": [
       "Quantum object: dims = [[2, 2], [2, 2]], shape = (4, 4), type = oper, isherm = True\\begin{equation*}\\left(\\begin{array}{*{11}c}0.0 & 0.0 & 0.0 & 1.0\\\\0.0 & 0.0 & 1.0 & 0.0\\\\0.0 & 1.0 & 0.0 & 0.0\\\\1.0 & 0.0 & 0.0 & 0.0\\\\\\end{array}\\right)\\end{equation*}"
      ],
      "text/plain": [
       "Quantum object: dims = [[2, 2], [2, 2]], shape = (4, 4), type = oper, isherm = True\n",
       "Qobj data =\n",
       "[[0. 0. 0. 1.]\n",
       " [0. 0. 1. 0.]\n",
       " [0. 1. 0. 0.]\n",
       " [1. 0. 0. 0.]]"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-31T03:40:28.032834Z",
     "start_time": "2019-01-31T03:40:27.993136Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/latex": [
       "Quantum object: dims = [[2, 2], [2, 2]], shape = (4, 4), type = oper, isherm = False\\begin{equation*}\\left(\\begin{array}{*{11}c}0.500 & (-8.445\\times10^{-04}+8.780\\times10^{-07}j) & (-8.445\\times10^{-04}-8.780\\times10^{-07}j) & 0.500\\\\(8.439\\times10^{-04}-1.974\\times10^{-06}j) & (-3.967\\times10^{-04}+7.587\\times10^{-06}j) & (-4.002\\times10^{-04}-3.247\\times10^{-06}j) & (-8.439\\times10^{-04}+1.974\\times10^{-06}j)\\\\(8.439\\times10^{-04}+1.974\\times10^{-06}j) & (-4.002\\times10^{-04}+3.247\\times10^{-06}j) & (-3.967\\times10^{-04}-7.587\\times10^{-06}j) & (-8.439\\times10^{-04}-1.974\\times10^{-06}j)\\\\0.500 & (8.445\\times10^{-04}-8.780\\times10^{-07}j) & (8.445\\times10^{-04}+8.780\\times10^{-07}j) & 0.500\\\\\\end{array}\\right)\\end{equation*}"
      ],
      "text/plain": [
       "Quantum object: dims = [[2, 2], [2, 2]], shape = (4, 4), type = oper, isherm = False\n",
       "Qobj data =\n",
       "[[ 4.99601314e-01+0.00000000e+00j -8.44451456e-04+8.78041811e-07j\n",
       "  -8.44451456e-04-8.78041812e-07j  5.00398686e-01+0.00000000e+00j]\n",
       " [ 8.43925211e-04-1.97422006e-06j -3.96725596e-04+7.58652527e-06j\n",
       "  -4.00247762e-04-3.24717202e-06j -8.43925211e-04+1.97422006e-06j]\n",
       " [ 8.43925211e-04+1.97422006e-06j -4.00247762e-04+3.24717202e-06j\n",
       "  -3.96725596e-04-7.58652527e-06j -8.43925211e-04-1.97422006e-06j]\n",
       " [ 5.00398686e-01+0.00000000e+00j  8.44451456e-04-8.78041812e-07j\n",
       "   8.44451456e-04+8.78041811e-07j  4.99601314e-01+0.00000000e+00j]]"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "L_full_maker(xi_opt, dt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-31T03:40:28.302563Z",
     "start_time": "2019-01-31T03:40:28.034711Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Figure size 576x432 with 2 Axes>,\n",
       " <matplotlib.axes._subplots.AxesSubplot at 0x1a23283320>)"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hinton(L_full_maker(xi_opt, dt))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Try2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-31T03:40:28.308085Z",
     "start_time": "2019-01-31T03:40:28.304769Z"
    }
   },
   "outputs": [],
   "source": [
    "total_time = 3*pi/omega_1\n",
    "epsilon = 10**(-6)\n",
    "max_iter = 10#10**4#1000#100#50#20\n",
    "time_steps = 200\n",
    "dt = total_time/time_steps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-31T03:42:43.737092Z",
     "start_time": "2019-01-31T03:40:28.310128Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([669.93282255+0.j, 855.41399193+0.j, 165.54949034+0.j,\n",
       "       482.48858536+0.j, 342.62081729+0.j, 206.0307676 +0.j,\n",
       "        77.38694075+0.j, 928.72262927+0.j, 998.41513427+0.j,\n",
       "       246.8964603 +0.j, 738.44610915+0.j, 686.53431526+0.j,\n",
       "       767.47644267+0.j, 416.29453042+0.j, 652.17942595+0.j,\n",
       "       317.33050076+0.j, 659.06194186+0.j, 395.15892462+0.j,\n",
       "       466.08489753+0.j, 951.99193941+0.j, 481.86586549+0.j,\n",
       "       407.51861575+0.j, 579.64486577+0.j, 262.82841781+0.j,\n",
       "       936.95958765+0.j, 515.61931657+0.j, 777.14590254+0.j,\n",
       "       916.78003194+0.j, 336.84291003+0.j, 657.54373832+0.j,\n",
       "       471.22309698+0.j, 565.44572085+0.j, 864.53446624+0.j,\n",
       "       343.09491824+0.j, 798.31476124+0.j, 887.06739546+0.j,\n",
       "       271.1093658 +0.j, 239.78865928+0.j, 761.52379115+0.j,\n",
       "       641.9599327 +0.j, 328.99058299+0.j, 203.86798119+0.j,\n",
       "       174.495657  +0.j, 380.26907774+0.j, 150.02915331+0.j,\n",
       "       774.07224879+0.j, 664.37065901+0.j, 186.59876501+0.j,\n",
       "       205.14649299+0.j, 509.72715266+0.j, 395.11915114+0.j,\n",
       "       878.73691297+0.j,  56.89012308+0.j, 302.54549052+0.j,\n",
       "       952.11792012+0.j, 935.39214027+0.j, 168.77894849+0.j,\n",
       "        42.83826835+0.j, 721.28450165+0.j, 943.16708843+0.j,\n",
       "       191.43983231+0.j, 590.02735898+0.j, 918.91793153+0.j,\n",
       "       264.14531268+0.j, 110.55664753+0.j, 840.23900242+0.j,\n",
       "       244.52906839+0.j, 365.73489064+0.j, 460.88746218+0.j,\n",
       "        16.80439811+0.j, 557.64732578+0.j, 426.90843091+0.j,\n",
       "        34.59304061+0.j, 469.28173023+0.j, 827.90798531+0.j,\n",
       "       771.60920906+0.j, 160.99940884+0.j, 887.91482141+0.j,\n",
       "       774.05714096+0.j, 659.77766528+0.j, 967.91706016+0.j,\n",
       "       794.26750628+0.j, 716.00701303+0.j, 903.9740441 +0.j,\n",
       "       594.95492305+0.j, 455.19533352+0.j, 294.65264451+0.j,\n",
       "       639.50108058+0.j,  57.92504166+0.j, 648.08523143+0.j,\n",
       "       990.29116443+0.j, 248.12973034+0.j, 326.23136382+0.j,\n",
       "       137.8958129 +0.j, 246.64328254+0.j, 479.38779217+0.j,\n",
       "       136.99262525+0.j, 310.88835878+0.j, 684.15992466+0.j,\n",
       "       205.4962925 +0.j, 393.97918937+0.j, 672.73914462+0.j,\n",
       "        84.41032536+0.j, 263.02753502+0.j,   7.35800547+0.j,\n",
       "       350.3278593 +0.j, 537.48567455+0.j, 288.1840845 +0.j,\n",
       "       283.20807099+0.j, 579.5339533 +0.j, 933.06593949+0.j,\n",
       "       154.63814044+0.j, 354.84542733+0.j, 472.39786749+0.j,\n",
       "       129.78762985+0.j, 704.70611328+0.j, 268.88088426+0.j,\n",
       "       342.76734562+0.j, 663.77426842+0.j, 422.59335515+0.j,\n",
       "       965.02525503+0.j, 752.09583116+0.j, 253.57637326+0.j,\n",
       "       661.82046964+0.j, 908.93474421+0.j, 512.36389549+0.j,\n",
       "       845.77365467+0.j, 247.22315585+0.j, 930.61570013+0.j,\n",
       "       796.39736197+0.j,  38.40924931+0.j, 890.0680635 +0.j,\n",
       "        71.89707251+0.j, 674.76902476+0.j, 585.33873846+0.j,\n",
       "       287.33659807+0.j,   7.47991655+0.j, 721.5631295 +0.j,\n",
       "       664.25623593+0.j, 647.8142481 +0.j,  41.5851709 +0.j,\n",
       "       326.95597884+0.j, 480.48261945+0.j, 276.31149495+0.j,\n",
       "       587.84450682+0.j, 346.39464427+0.j,  11.73162544+0.j,\n",
       "       197.77678649+0.j, 770.44670411+0.j, 426.5403376 +0.j,\n",
       "       836.21291591+0.j, 986.82423913+0.j, 763.19484939+0.j,\n",
       "       460.06730957+0.j, 528.1613289 +0.j, 231.89413279+0.j,\n",
       "       705.72235957+0.j, 320.65140903+0.j, 347.6738189 +0.j,\n",
       "       305.59120239+0.j, 591.90915538+0.j, 905.59210079+0.j,\n",
       "        97.53488793+0.j, 750.91855875+0.j, 269.02883922+0.j,\n",
       "       869.1268034 +0.j,  68.06615307+0.j, 226.13029477+0.j,\n",
       "       850.48666113+0.j, 157.27771996+0.j, 121.97482676+0.j,\n",
       "       951.75455275+0.j, 735.16980132+0.j, 890.62563803+0.j,\n",
       "       864.20485918+0.j, 804.02072863+0.j, 839.03210648+0.j,\n",
       "       119.02059533+0.j, 809.77461001+0.j, 697.76835101+0.j,\n",
       "       649.46349745+0.j, 328.43888966+0.j, 877.88996181+0.j,\n",
       "        34.30519198+0.j, 506.9439703 +0.j, 436.64946157+0.j,\n",
       "       739.05947666+0.j, 179.6868555 +0.j, 190.7134763 +0.j,\n",
       "       113.38668545+0.j, 265.18699785+0.j, 196.75473433+0.j,\n",
       "       613.14040876+0.j, 424.66723412+0.j, 289.55943658+0.j,\n",
       "       157.43457651+0.j, 553.64222278+0.j, 418.0809171 +0.j,\n",
       "       634.1678144 +0.j,  74.95239631+0.j])"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xi_opt = terminator(max_iter, time_steps, total_time, epsilon)\n",
    "xi_opt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-31T03:42:44.114796Z",
     "start_time": "2019-01-31T03:42:43.740498Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-3.105557715080997"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "F(xi_opt, dt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-31T03:42:44.120689Z",
     "start_time": "2019-01-31T03:42:44.116761Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max_iter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-31T03:42:44.132679Z",
     "start_time": "2019-01-31T03:42:44.122886Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/latex": [
       "Quantum object: dims = [[2, 2], [2, 2]], shape = (4, 4), type = oper, isherm = True\\begin{equation*}\\left(\\begin{array}{*{11}c}0.0 & 0.0 & 0.0 & 1.0\\\\0.0 & 0.0 & 1.0 & 0.0\\\\0.0 & 1.0 & 0.0 & 0.0\\\\1.0 & 0.0 & 0.0 & 0.0\\\\\\end{array}\\right)\\end{equation*}"
      ],
      "text/plain": [
       "Quantum object: dims = [[2, 2], [2, 2]], shape = (4, 4), type = oper, isherm = True\n",
       "Qobj data =\n",
       "[[0. 0. 0. 1.]\n",
       " [0. 0. 1. 0.]\n",
       " [0. 1. 0. 0.]\n",
       " [1. 0. 0. 0.]]"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-31T03:42:44.471625Z",
     "start_time": "2019-01-31T03:42:44.134836Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/latex": [
       "Quantum object: dims = [[2, 2], [2, 2]], shape = (4, 4), type = oper, isherm = False\\begin{equation*}\\left(\\begin{array}{*{11}c}0.451 & (0.054-0.002j) & (0.054+0.002j) & 0.549\\\\(-0.056-0.002j) & (-0.041-0.001j) & (-0.064+0.009j) & (0.056+0.002j)\\\\(-0.056+0.002j) & (-0.064-0.009j) & (-0.041+0.001j) & (0.056-0.002j)\\\\0.549 & (-0.054+0.002j) & (-0.054-0.002j) & 0.451\\\\\\end{array}\\right)\\end{equation*}"
      ],
      "text/plain": [
       "Quantum object: dims = [[2, 2], [2, 2]], shape = (4, 4), type = oper, isherm = False\n",
       "Qobj data =\n",
       "[[ 0.45053501+0.j          0.05386567-0.00219087j  0.05386567+0.00219087j\n",
       "   0.54946499+0.j        ]\n",
       " [-0.05576711-0.00175285j -0.04067547-0.00109118j -0.06444199+0.00917307j\n",
       "   0.05576711+0.00175285j]\n",
       " [-0.05576711+0.00175285j -0.06444199-0.00917307j -0.04067547+0.00109118j\n",
       "   0.05576711-0.00175285j]\n",
       " [ 0.54946499+0.j         -0.05386567+0.00219087j -0.05386567-0.00219087j\n",
       "   0.45053501+0.j        ]]"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "L_full_maker(xi_opt, dt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-31T03:42:45.079669Z",
     "start_time": "2019-01-31T03:42:44.473847Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Figure size 576x432 with 2 Axes>,\n",
       " <matplotlib.axes._subplots.AxesSubplot at 0x1a232334a8>)"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hinton(L_full_maker(xi_opt, dt))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Try2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-31T03:42:45.086786Z",
     "start_time": "2019-01-31T03:42:45.083204Z"
    }
   },
   "outputs": [],
   "source": [
    "total_time = 3*pi/omega_1\n",
    "epsilon = 10**(-6)\n",
    "max_iter = 10#10**4#1000#100#50#20\n",
    "time_steps = 50\n",
    "dt = total_time/time_steps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-31T03:42:53.157533Z",
     "start_time": "2019-01-31T03:42:45.089238Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([983.35006857+0.j, 854.18599804+0.j, 427.39158478+0.j,\n",
       "       568.22      +0.j, 557.67520848+0.j, 215.20662373+0.j,\n",
       "       531.89079128+0.j, 180.95001119+0.j, 985.00507013+0.j,\n",
       "       285.75321228+0.j, 230.22505894+0.j, 392.17777385+0.j,\n",
       "       911.97270469+0.j, 865.95937681+0.j, 719.17747998+0.j,\n",
       "       614.34209737+0.j, 102.76796751+0.j, 122.0661469 +0.j,\n",
       "       182.22958974+0.j, 641.75827772+0.j,  13.03504773+0.j,\n",
       "       785.10642376+0.j, 268.16224425+0.j, 776.70699808+0.j,\n",
       "       382.81870874+0.j, 269.94700403+0.j,  82.49822442+0.j,\n",
       "       514.10921463+0.j, 917.88703593+0.j, 709.00061536+0.j,\n",
       "       328.04657417+0.j, 510.19530155+0.j, 792.68882212+0.j,\n",
       "       781.24520002+0.j, 636.8738876 +0.j, 884.35109957+0.j,\n",
       "       125.25199229+0.j, 498.7457331 +0.j, 133.36702511+0.j,\n",
       "       900.08614773+0.j, 225.70716339+0.j, 173.72539579+0.j,\n",
       "       373.7743205 +0.j, 616.07328852+0.j, 173.27636418+0.j,\n",
       "       915.84662279+0.j, 164.29794128+0.j, 802.09244618+0.j,\n",
       "       185.7917225 +0.j, 332.02434511+0.j])"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xi_opt = terminator(max_iter, time_steps, total_time, epsilon)\n",
    "xi_opt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-31T03:42:53.249732Z",
     "start_time": "2019-01-31T03:42:53.159933Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-3.089390859721715"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "F(xi_opt, dt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-31T03:42:53.259584Z",
     "start_time": "2019-01-31T03:42:53.255481Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max_iter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-31T03:42:53.268118Z",
     "start_time": "2019-01-31T03:42:53.261969Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/latex": [
       "Quantum object: dims = [[2, 2], [2, 2]], shape = (4, 4), type = oper, isherm = True\\begin{equation*}\\left(\\begin{array}{*{11}c}0.0 & 0.0 & 0.0 & 1.0\\\\0.0 & 0.0 & 1.0 & 0.0\\\\0.0 & 1.0 & 0.0 & 0.0\\\\1.0 & 0.0 & 0.0 & 0.0\\\\\\end{array}\\right)\\end{equation*}"
      ],
      "text/plain": [
       "Quantum object: dims = [[2, 2], [2, 2]], shape = (4, 4), type = oper, isherm = True\n",
       "Qobj data =\n",
       "[[0. 0. 0. 1.]\n",
       " [0. 0. 1. 0.]\n",
       " [0. 1. 0. 0.]\n",
       " [1. 0. 0. 0.]]"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-31T03:42:53.360161Z",
     "start_time": "2019-01-31T03:42:53.270658Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/latex": [
       "Quantum object: dims = [[2, 2], [2, 2]], shape = (4, 4), type = oper, isherm = False\\begin{equation*}\\left(\\begin{array}{*{11}c}0.434 & (-0.037-0.002j) & (-0.037+0.002j) & 0.566\\\\(0.037-7.035\\times10^{-04}j) & (-0.054-0.002j) & (-0.077+0.009j) & (-0.037+7.035\\times10^{-04}j)\\\\(0.037+7.035\\times10^{-04}j) & (-0.077-0.009j) & (-0.054+0.002j) & (-0.037-7.035\\times10^{-04}j)\\\\0.566 & (0.037+0.002j) & (0.037-0.002j) & 0.434\\\\\\end{array}\\right)\\end{equation*}"
      ],
      "text/plain": [
       "Quantum object: dims = [[2, 2], [2, 2]], shape = (4, 4), type = oper, isherm = False\n",
       "Qobj data =\n",
       "[[ 0.43385111+0.j         -0.03689713-0.00166021j -0.03689713+0.00166021j\n",
       "   0.56614889+0.j        ]\n",
       " [ 0.03697858-0.00070345j -0.05410633-0.00178289j -0.07692821+0.00852519j\n",
       "  -0.03697858+0.00070345j]\n",
       " [ 0.03697858+0.00070345j -0.07692821-0.00852519j -0.05410633+0.00178289j\n",
       "  -0.03697858-0.00070345j]\n",
       " [ 0.56614889+0.j          0.03689713+0.00166021j  0.03689713-0.00166021j\n",
       "   0.43385111+0.j        ]]"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "L_full_maker(xi_opt, dt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-31T03:42:53.714552Z",
     "start_time": "2019-01-31T03:42:53.362376Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Figure size 576x432 with 2 Axes>,\n",
       " <matplotlib.axes._subplots.AxesSubplot at 0x1a23289cf8>)"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hinton(L_full_maker(xi_opt, dt))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Try2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-31T03:42:53.720033Z",
     "start_time": "2019-01-31T03:42:53.716641Z"
    }
   },
   "outputs": [],
   "source": [
    "total_time = 3*pi/omega_1\n",
    "epsilon = 10**(-6)\n",
    "max_iter = 20#10**4#1000#100#50#20\n",
    "time_steps = 20\n",
    "dt = total_time/time_steps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-31T03:42:55.079820Z",
     "start_time": "2019-01-31T03:42:53.722403Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 37.75150132+0.j, 207.82847072+0.j, 955.80557562+0.j,\n",
       "       553.46393669+0.j, 180.58114193+0.j, 439.87340604+0.j,\n",
       "       873.14596644+0.j, 376.56916437+0.j, 181.62899988+0.j,\n",
       "       809.56553281+0.j,   8.81952599+0.j, 158.90623378+0.j,\n",
       "       179.1560674 +0.j, 126.77723779+0.j, 735.44465353+0.j,\n",
       "       986.33756807+0.j, 172.86957463+0.j, 487.83018464+0.j,\n",
       "       564.83703258+0.j,  31.28685151+0.j])"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xi_opt = terminator(max_iter, time_steps, total_time, epsilon)\n",
    "xi_opt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-31T03:42:55.123417Z",
     "start_time": "2019-01-31T03:42:55.082162Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-3.0924278039056157"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "F(xi_opt, dt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-31T03:42:55.130235Z",
     "start_time": "2019-01-31T03:42:55.125691Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max_iter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-31T03:42:55.139248Z",
     "start_time": "2019-01-31T03:42:55.132767Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/latex": [
       "Quantum object: dims = [[2, 2], [2, 2]], shape = (4, 4), type = oper, isherm = True\\begin{equation*}\\left(\\begin{array}{*{11}c}0.0 & 0.0 & 0.0 & 1.0\\\\0.0 & 0.0 & 1.0 & 0.0\\\\0.0 & 1.0 & 0.0 & 0.0\\\\1.0 & 0.0 & 0.0 & 0.0\\\\\\end{array}\\right)\\end{equation*}"
      ],
      "text/plain": [
       "Quantum object: dims = [[2, 2], [2, 2]], shape = (4, 4), type = oper, isherm = True\n",
       "Qobj data =\n",
       "[[0. 0. 0. 1.]\n",
       " [0. 0. 1. 0.]\n",
       " [0. 1. 0. 0.]\n",
       " [1. 0. 0. 0.]]"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-31T03:42:55.182419Z",
     "start_time": "2019-01-31T03:42:55.141778Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/latex": [
       "Quantum object: dims = [[2, 2], [2, 2]], shape = (4, 4), type = oper, isherm = False\\begin{equation*}\\left(\\begin{array}{*{11}c}0.434 & (0.036-0.006j) & (0.036+0.006j) & 0.566\\\\(-0.035+0.009j) & (-0.055-0.007j) & (-0.077+0.004j) & (0.035-0.009j)\\\\(-0.035-0.009j) & (-0.077-0.004j) & (-0.055+0.007j) & (0.035+0.009j)\\\\0.566 & (-0.036+0.006j) & (-0.036-0.006j) & 0.434\\\\\\end{array}\\right)\\end{equation*}"
      ],
      "text/plain": [
       "Quantum object: dims = [[2, 2], [2, 2]], shape = (4, 4), type = oper, isherm = False\n",
       "Qobj data =\n",
       "[[ 0.43438508+0.j          0.03592371-0.00581127j  0.03592371+0.00581127j\n",
       "   0.56561492+0.j        ]\n",
       " [-0.03533659+0.00850701j -0.05528344-0.00690386j -0.07722724+0.00447405j\n",
       "   0.03533659-0.00850701j]\n",
       " [-0.03533659-0.00850701j -0.07722724-0.00447405j -0.05528344+0.00690386j\n",
       "   0.03533659+0.00850701j]\n",
       " [ 0.56561492+0.j         -0.03592371+0.00581127j -0.03592371-0.00581127j\n",
       "   0.43438508+0.j        ]]"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "L_full_maker(xi_opt, dt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-31T03:42:55.457078Z",
     "start_time": "2019-01-31T03:42:55.185044Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Figure size 576x432 with 2 Axes>,\n",
       " <matplotlib.axes._subplots.AxesSubplot at 0x1a235c5b38>)"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hinton(L_full_maker(xi_opt, dt))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Try2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-31T03:42:55.462305Z",
     "start_time": "2019-01-31T03:42:55.459236Z"
    }
   },
   "outputs": [],
   "source": [
    "total_time = 3*pi/omega_1\n",
    "epsilon = 10**(-6)\n",
    "max_iter = 100#10**4#1000#100#50#20\n",
    "time_steps = 20\n",
    "dt = total_time/time_steps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-31T03:42:56.824337Z",
     "start_time": "2019-01-31T03:42:55.464750Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([401.58366425+0.j, 743.60212539+0.j, 462.65261428+0.j,\n",
       "       291.61275214+0.j, 997.20136871+0.j, 161.83255664+0.j,\n",
       "       358.71205265+0.j, 397.6299681 +0.j, 115.56934024+0.j,\n",
       "       265.02621297+0.j, 495.14132269+0.j, 681.28181627+0.j,\n",
       "       379.03425381+0.j, 751.750218  +0.j, 868.3165758 +0.j,\n",
       "       208.36118945+0.j, 571.48350418+0.j, 154.54000199+0.j,\n",
       "       969.10320073+0.j, 104.39218721+0.j])"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xi_opt = terminator(max_iter, time_steps, total_time, epsilon)\n",
    "xi_opt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-31T03:42:56.868429Z",
     "start_time": "2019-01-31T03:42:56.826467Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-3.092321460912429"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "F(xi_opt, dt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-31T03:42:56.874993Z",
     "start_time": "2019-01-31T03:42:56.870689Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max_iter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-31T03:42:56.883413Z",
     "start_time": "2019-01-31T03:42:56.877551Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/latex": [
       "Quantum object: dims = [[2, 2], [2, 2]], shape = (4, 4), type = oper, isherm = True\\begin{equation*}\\left(\\begin{array}{*{11}c}0.0 & 0.0 & 0.0 & 1.0\\\\0.0 & 0.0 & 1.0 & 0.0\\\\0.0 & 1.0 & 0.0 & 0.0\\\\1.0 & 0.0 & 0.0 & 0.0\\\\\\end{array}\\right)\\end{equation*}"
      ],
      "text/plain": [
       "Quantum object: dims = [[2, 2], [2, 2]], shape = (4, 4), type = oper, isherm = True\n",
       "Qobj data =\n",
       "[[0. 0. 0. 1.]\n",
       " [0. 0. 1. 0.]\n",
       " [0. 1. 0. 0.]\n",
       " [1. 0. 0. 0.]]"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-31T03:42:56.950583Z",
     "start_time": "2019-01-31T03:42:56.886947Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/latex": [
       "Quantum object: dims = [[2, 2], [2, 2]], shape = (4, 4), type = oper, isherm = False\\begin{equation*}\\left(\\begin{array}{*{11}c}0.458 & (0.064-5.532\\times10^{-04}j) & (0.064+5.532\\times10^{-04}j) & 0.542\\\\(-0.064+1.788\\times10^{-04}j) & (-0.030-0.002j) & (-0.053-0.002j) & (0.064-1.788\\times10^{-04}j)\\\\(-0.064-1.788\\times10^{-04}j) & (-0.053+0.002j) & (-0.030+0.002j) & (0.064+1.788\\times10^{-04}j)\\\\0.542 & (-0.064+5.532\\times10^{-04}j) & (-0.064-5.532\\times10^{-04}j) & 0.458\\\\\\end{array}\\right)\\end{equation*}"
      ],
      "text/plain": [
       "Quantum object: dims = [[2, 2], [2, 2]], shape = (4, 4), type = oper, isherm = False\n",
       "Qobj data =\n",
       "[[ 0.45849233+0.j          0.06352188-0.00055322j  0.06352188+0.00055322j\n",
       "   0.54150767+0.j        ]\n",
       " [-0.06363213+0.00017884j -0.02989221-0.00176813j -0.05292961-0.0020041j\n",
       "   0.06363213-0.00017884j]\n",
       " [-0.06363213-0.00017884j -0.05292961+0.0020041j  -0.02989221+0.00176813j\n",
       "   0.06363213+0.00017884j]\n",
       " [ 0.54150767+0.j         -0.06352188+0.00055322j -0.06352188-0.00055322j\n",
       "   0.45849233+0.j        ]]"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "L_full_maker(xi_opt, dt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-31T03:42:57.239958Z",
     "start_time": "2019-01-31T03:42:56.953108Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Figure size 576x432 with 2 Axes>,\n",
       " <matplotlib.axes._subplots.AxesSubplot at 0x1a2387c080>)"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hinton(L_full_maker(xi_opt, dt))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Try2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-31T03:42:57.245119Z",
     "start_time": "2019-01-31T03:42:57.241845Z"
    }
   },
   "outputs": [],
   "source": [
    "total_time = 3*pi/omega_1\n",
    "epsilon = 10**(-6)\n",
    "max_iter = 100#10**4#1000#100#50#20\n",
    "time_steps = 200\n",
    "dt = total_time/time_steps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-31T03:45:03.620283Z",
     "start_time": "2019-01-31T03:42:57.247359Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "       3.48598600e+02+0.j, 8.54332073e+02+0.j, 1.72209044e+02+0.j,\n",
       "       4.57079637e+02+0.j, 8.02568969e+02+0.j, 9.02620514e+02+0.j,\n",
       "       6.78038549e+01+0.j, 1.84803394e+02+0.j, 1.48127488e+02+0.j,\n",
       "       5.96732342e+02+0.j, 3.41553641e+02+0.j, 9.72169741e+02+0.j,\n",
       "       5.50332484e+02+0.j, 1.98387914e+02+0.j, 6.32385303e+02+0.j,\n",
       "       1.91244101e+02+0.j, 3.52025338e+02+0.j, 7.62233798e+02+0.j,\n",
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       "       8.08201449e+02+0.j, 6.14077752e+02+0.j, 7.10091535e+02+0.j,\n",
       "       2.03723961e+02+0.j, 8.96168341e+02+0.j, 9.83373206e+02+0.j,\n",
       "       9.74325825e+02+0.j, 9.19403675e+02+0.j, 2.35612433e+01+0.j,\n",
       "       2.14902965e+02+0.j, 2.90594504e+02+0.j, 5.29367434e+02+0.j,\n",
       "       5.50110390e+02+0.j, 7.45461904e+02+0.j, 1.90490662e+02+0.j,\n",
       "       7.96096840e+02+0.j, 4.15147982e+02+0.j, 7.69375398e+02+0.j,\n",
       "       1.46303540e+02+0.j, 9.16475736e+02+0.j, 3.69942002e+02+0.j,\n",
       "       2.12757326e+02+0.j, 8.28889558e+02+0.j, 5.23405079e+02+0.j,\n",
       "       7.37254539e+02+0.j, 9.68066536e+02+0.j, 8.96294123e+02+0.j,\n",
       "       4.64456206e+02+0.j, 7.58117747e+02+0.j, 1.39068304e+02+0.j,\n",
       "       9.16285458e+02+0.j, 7.78514119e+02+0.j, 3.25439810e+02+0.j,\n",
       "       8.23015879e+02+0.j, 2.44843157e+01+0.j, 8.71339033e+01+0.j,\n",
       "       6.36380021e+02+0.j, 4.91615406e+02+0.j])"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xi_opt = terminator(max_iter, time_steps, total_time, epsilon)\n",
    "xi_opt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-31T03:45:04.000605Z",
     "start_time": "2019-01-31T03:45:03.623156Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-3.08713387614768"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "F(xi_opt, dt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-31T03:45:04.007058Z",
     "start_time": "2019-01-31T03:45:04.002968Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max_iter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-31T03:45:04.016814Z",
     "start_time": "2019-01-31T03:45:04.009178Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/latex": [
       "Quantum object: dims = [[2, 2], [2, 2]], shape = (4, 4), type = oper, isherm = True\\begin{equation*}\\left(\\begin{array}{*{11}c}0.0 & 0.0 & 0.0 & 1.0\\\\0.0 & 0.0 & 1.0 & 0.0\\\\0.0 & 1.0 & 0.0 & 0.0\\\\1.0 & 0.0 & 0.0 & 0.0\\\\\\end{array}\\right)\\end{equation*}"
      ],
      "text/plain": [
       "Quantum object: dims = [[2, 2], [2, 2]], shape = (4, 4), type = oper, isherm = True\n",
       "Qobj data =\n",
       "[[0. 0. 0. 1.]\n",
       " [0. 0. 1. 0.]\n",
       " [0. 1. 0. 0.]\n",
       " [1. 0. 0. 0.]]"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-31T03:45:04.359593Z",
     "start_time": "2019-01-31T03:45:04.018797Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/latex": [
       "Quantum object: dims = [[2, 2], [2, 2]], shape = (4, 4), type = oper, isherm = False\\begin{equation*}\\left(\\begin{array}{*{11}c}0.557 & (0.048-0.001j) & (0.048+0.001j) & 0.443\\\\(-0.045-0.006j) & (0.067+0.023j) & (0.047-0.011j) & (0.045+0.006j)\\\\(-0.045+0.006j) & (0.047+0.011j) & (0.067-0.023j) & (0.045-0.006j)\\\\0.443 & (-0.048+0.001j) & (-0.048-0.001j) & 0.557\\\\\\end{array}\\right)\\end{equation*}"
      ],
      "text/plain": [
       "Quantum object: dims = [[2, 2], [2, 2]], shape = (4, 4), type = oper, isherm = False\n",
       "Qobj data =\n",
       "[[ 0.55715523+0.j          0.04808016-0.00149918j  0.04808016+0.00149918j\n",
       "   0.44284477+0.j        ]\n",
       " [-0.04537223-0.0055094j   0.06742228+0.02267533j  0.04672688-0.01129489j\n",
       "   0.04537223+0.0055094j ]\n",
       " [-0.04537223+0.0055094j   0.04672688+0.01129489j  0.06742228-0.02267533j\n",
       "   0.04537223-0.0055094j ]\n",
       " [ 0.44284477+0.j         -0.04808016+0.00149918j -0.04808016-0.00149918j\n",
       "   0.55715523+0.j        ]]"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "L_full_maker(xi_opt, dt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-31T03:45:04.935440Z",
     "start_time": "2019-01-31T03:45:04.361610Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Figure size 576x432 with 2 Axes>,\n",
       " <matplotlib.axes._subplots.AxesSubplot at 0x1a23861c18>)"
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hinton(L_full_maker(xi_opt, dt))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Try2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-31T03:45:04.941205Z",
     "start_time": "2019-01-31T03:45:04.937868Z"
    }
   },
   "outputs": [],
   "source": [
    "total_time = 3*pi/omega_1\n",
    "epsilon = (0.1*2*pi)/(total_time)#10**(-6)\n",
    "max_iter = 10#10**4#1000#100#50#20\n",
    "time_steps = 20\n",
    "dt = total_time/time_steps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-31T03:45:06.287922Z",
     "start_time": "2019-01-31T03:45:04.943745Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([146.64691443+0.j, 749.78105269+0.j, 889.2844675 +0.j,\n",
       "       558.74868247+0.j, 142.65208002+0.j, 783.26182885+0.j,\n",
       "       255.59747005+0.j, 551.33485118+0.j, 220.74250203+0.j,\n",
       "       885.37234198+0.j, 831.7905435 +0.j, 451.62602883+0.j,\n",
       "       975.73768249+0.j, 723.78784668+0.j, 724.42037836+0.j,\n",
       "       525.14088973+0.j, 196.835981  +0.j, 929.02565631+0.j,\n",
       "       753.85742941+0.j, 608.14419261+0.j])"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xi_opt = terminator(max_iter, time_steps, total_time, epsilon)\n",
    "xi_opt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-31T03:45:06.332738Z",
     "start_time": "2019-01-31T03:45:06.290348Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-3.092700262563744"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "F(xi_opt, dt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-31T03:45:06.339687Z",
     "start_time": "2019-01-31T03:45:06.334913Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max_iter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-31T03:45:06.347974Z",
     "start_time": "2019-01-31T03:45:06.341981Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/latex": [
       "Quantum object: dims = [[2, 2], [2, 2]], shape = (4, 4), type = oper, isherm = True\\begin{equation*}\\left(\\begin{array}{*{11}c}0.0 & 0.0 & 0.0 & 1.0\\\\0.0 & 0.0 & 1.0 & 0.0\\\\0.0 & 1.0 & 0.0 & 0.0\\\\1.0 & 0.0 & 0.0 & 0.0\\\\\\end{array}\\right)\\end{equation*}"
      ],
      "text/plain": [
       "Quantum object: dims = [[2, 2], [2, 2]], shape = (4, 4), type = oper, isherm = True\n",
       "Qobj data =\n",
       "[[0. 0. 0. 1.]\n",
       " [0. 0. 1. 0.]\n",
       " [0. 1. 0. 0.]\n",
       " [1. 0. 0. 0.]]"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-31T03:45:06.393220Z",
     "start_time": "2019-01-31T03:45:06.350497Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/latex": [
       "Quantum object: dims = [[2, 2], [2, 2]], shape = (4, 4), type = oper, isherm = False\\begin{equation*}\\left(\\begin{array}{*{11}c}0.547 & (-0.060+6.379\\times10^{-05}j) & (-0.060-6.379\\times10^{-05}j) & 0.453\\\\(0.060-2.277\\times10^{-04}j) & (0.058-1.555\\times10^{-04}j) & (0.035+1.935\\times10^{-04}j) & (-0.060+2.277\\times10^{-04}j)\\\\(0.060+2.277\\times10^{-04}j) & (0.035-1.935\\times10^{-04}j) & (0.058+1.555\\times10^{-04}j) & (-0.060-2.277\\times10^{-04}j)\\\\0.453 & (0.060-6.379\\times10^{-05}j) & (0.060+6.379\\times10^{-05}j) & 0.547\\\\\\end{array}\\right)\\end{equation*}"
      ],
      "text/plain": [
       "Quantum object: dims = [[2, 2], [2, 2]], shape = (4, 4), type = oper, isherm = False\n",
       "Qobj data =\n",
       "[[ 0.54690923+0.00000000e+00j -0.05967857+6.37856630e-05j\n",
       "  -0.05967857-6.37856630e-05j  0.45309077+0.00000000e+00j]\n",
       " [ 0.05968888-2.27716642e-04j  0.0584485 -1.55517210e-04j\n",
       "   0.03539348+1.93499291e-04j -0.05968888+2.27716642e-04j]\n",
       " [ 0.05968888+2.27716642e-04j  0.03539348-1.93499291e-04j\n",
       "   0.0584485 +1.55517210e-04j -0.05968888-2.27716642e-04j]\n",
       " [ 0.45309077+0.00000000e+00j  0.05967857-6.37856630e-05j\n",
       "   0.05967857+6.37856630e-05j  0.54690923+0.00000000e+00j]]"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "L_full_maker(xi_opt, dt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-31T03:45:06.670998Z",
     "start_time": "2019-01-31T03:45:06.399544Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Figure size 576x432 with 2 Axes>,\n",
       " <matplotlib.axes._subplots.AxesSubplot at 0x1a235ecf98>)"
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hinton(L_full_maker(xi_opt, dt))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Try2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-31T03:45:06.676320Z",
     "start_time": "2019-01-31T03:45:06.672844Z"
    }
   },
   "outputs": [],
   "source": [
    "total_time = 3*pi/omega_1\n",
    "epsilon = (0.1*2*pi)/(total_time)#10**(-6)\n",
    "max_iter = 500#10#10**4#1000#100#50#20\n",
    "time_steps = 20\n",
    "dt = total_time/time_steps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-31T03:45:08.078790Z",
     "start_time": "2019-01-31T03:45:06.678556Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([272.1332443 +0.j,  62.49435768+0.j, 478.31559503+0.j,\n",
       "       642.3570882 +0.j, 247.40237979+0.j, 858.88589277+0.j,\n",
       "       699.57879076+0.j, 738.46010145+0.j, 458.97876102+0.j,\n",
       "       690.72686689+0.j, 609.13766172+0.j, 913.046857  +0.j,\n",
       "       842.45228207+0.j,  75.29612458+0.j, 518.10094757+0.j,\n",
       "       609.6485885 +0.j, 380.96604687+0.j,  18.42243984+0.j,\n",
       "       179.91927543+0.j, 531.91130855+0.j])"
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xi_opt = terminator(max_iter, time_steps, total_time, epsilon)\n",
    "xi_opt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-31T03:45:08.122960Z",
     "start_time": "2019-01-31T03:45:08.081155Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-3.0936564131157467"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "F(xi_opt, dt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-31T03:45:08.129807Z",
     "start_time": "2019-01-31T03:45:08.125209Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "500"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max_iter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-31T03:45:08.139853Z",
     "start_time": "2019-01-31T03:45:08.133332Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/latex": [
       "Quantum object: dims = [[2, 2], [2, 2]], shape = (4, 4), type = oper, isherm = True\\begin{equation*}\\left(\\begin{array}{*{11}c}0.0 & 0.0 & 0.0 & 1.0\\\\0.0 & 0.0 & 1.0 & 0.0\\\\0.0 & 1.0 & 0.0 & 0.0\\\\1.0 & 0.0 & 0.0 & 0.0\\\\\\end{array}\\right)\\end{equation*}"
      ],
      "text/plain": [
       "Quantum object: dims = [[2, 2], [2, 2]], shape = (4, 4), type = oper, isherm = True\n",
       "Qobj data =\n",
       "[[0. 0. 0. 1.]\n",
       " [0. 0. 1. 0.]\n",
       " [0. 1. 0. 0.]\n",
       " [1. 0. 0. 0.]]"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-31T03:45:08.180964Z",
     "start_time": "2019-01-31T03:45:08.141977Z"
    }
   },
   "outputs": [],
   "source": [
    "myU = L_full_maker(xi_opt, dt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-31T03:45:08.189380Z",
     "start_time": "2019-01-31T03:45:08.183348Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/latex": [
       "Quantum object: dims = [[2, 2], [2, 2]], shape = (4, 4), type = oper, isherm = False\\begin{equation*}\\left(\\begin{array}{*{11}c}0.738 & (-0.009+0.002j) & (-0.009-0.002j) & 1.0\\\\(0.010-0.009j) & (-0.111-0.006j) & (-0.151-0.006j) & (-0.010+0.009j)\\\\(0.010+0.009j) & (-0.151+0.006j) & (-0.111+0.006j) & (-0.010-0.009j)\\\\1.0 & (0.009-0.002j) & (0.009+0.002j) & 0.738\\\\\\end{array}\\right)\\end{equation*}"
      ],
      "text/plain": [
       "Quantum object: dims = [[2, 2], [2, 2]], shape = (4, 4), type = oper, isherm = False\n",
       "Qobj data =\n",
       "[[ 0.73806034+0.j         -0.00925049+0.00168568j -0.00925049-0.00168568j\n",
       "   1.        +0.j        ]\n",
       " [ 0.00977569-0.00900906j -0.1113746 -0.00635185j -0.15145284-0.00637646j\n",
       "  -0.00977569+0.00900906j]\n",
       " [ 0.00977569+0.00900906j -0.15145284+0.00637646j -0.1113746 +0.00635185j\n",
       "  -0.00977569-0.00900906j]\n",
       " [ 1.        +0.j          0.00925049-0.00168568j  0.00925049+0.00168568j\n",
       "   0.73806034+0.j        ]]"
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "myU/myU[0, 3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-31T03:45:08.434753Z",
     "start_time": "2019-01-31T03:45:08.191375Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Figure size 576x432 with 2 Axes>,\n",
       " <matplotlib.axes._subplots.AxesSubplot at 0x1a23c55240>)"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hinton(myU)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-31T03:45:08.772590Z",
     "start_time": "2019-01-31T03:45:08.436674Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Figure size 576x432 with 2 Axes>,\n",
       " <matplotlib.axes._subplots.AxesSubplot at 0x1a23e56b70>)"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hinton(L_full_maker(xi_opt, dt))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Try2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-31T03:45:08.777491Z",
     "start_time": "2019-01-31T03:45:08.774562Z"
    }
   },
   "outputs": [],
   "source": [
    "total_time = 3*pi/omega_1\n",
    "epsilon = (0.1*2*pi)/(total_time)#10**(-6)\n",
    "max_iter = 500#10#10**4#1000#100#50#20\n",
    "time_steps = 500\n",
    "dt = total_time/time_steps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-31T03:57:58.410528Z",
     "start_time": "2019-01-31T03:45:08.779893Z"
    }
   },
   "outputs": [],
   "source": [
    "xi_opt = terminator(max_iter, time_steps, total_time, epsilon)\n",
    "#xi_opt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-31T03:57:59.269761Z",
     "start_time": "2019-01-31T03:57:58.413345Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-3.0955282624024227"
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "F(xi_opt, dt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-31T03:57:59.276102Z",
     "start_time": "2019-01-31T03:57:59.272052Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "500"
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max_iter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-31T03:57:59.285152Z",
     "start_time": "2019-01-31T03:57:59.278775Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/latex": [
       "Quantum object: dims = [[2, 2], [2, 2]], shape = (4, 4), type = oper, isherm = True\\begin{equation*}\\left(\\begin{array}{*{11}c}0.0 & 0.0 & 0.0 & 1.0\\\\0.0 & 0.0 & 1.0 & 0.0\\\\0.0 & 1.0 & 0.0 & 0.0\\\\1.0 & 0.0 & 0.0 & 0.0\\\\\\end{array}\\right)\\end{equation*}"
      ],
      "text/plain": [
       "Quantum object: dims = [[2, 2], [2, 2]], shape = (4, 4), type = oper, isherm = True\n",
       "Qobj data =\n",
       "[[0. 0. 0. 1.]\n",
       " [0. 0. 1. 0.]\n",
       " [0. 1. 0. 0.]\n",
       " [1. 0. 0. 0.]]"
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-31T03:58:00.156777Z",
     "start_time": "2019-01-31T03:57:59.287673Z"
    }
   },
   "outputs": [],
   "source": [
    "myU = L_full_maker(xi_opt, dt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-31T03:58:00.166563Z",
     "start_time": "2019-01-31T03:58:00.158974Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/latex": [
       "Quantum object: dims = [[2, 2], [2, 2]], shape = (4, 4), type = oper, isherm = False\\begin{equation*}\\left(\\begin{array}{*{11}c}1.031 & (-0.153+0.009j) & (-0.153-0.009j) & 1.0\\\\(0.152+0.008j) & (0.036+0.008j) & (-0.010-0.017j) & (-0.152-0.008j)\\\\(0.152-0.008j) & (-0.010+0.017j) & (0.036-0.008j) & (-0.152+0.008j)\\\\1.0 & (0.153-0.009j) & (0.153+0.009j) & 1.031\\\\\\end{array}\\right)\\end{equation*}"
      ],
      "text/plain": [
       "Quantum object: dims = [[2, 2], [2, 2]], shape = (4, 4), type = oper, isherm = False\n",
       "Qobj data =\n",
       "[[ 1.03075523+0.j         -0.15286451+0.00891654j -0.15286451-0.00891654j\n",
       "   1.        +0.j        ]\n",
       " [ 0.15159851+0.00838859j  0.0359865 +0.00756527j -0.00967651-0.01738289j\n",
       "  -0.15159851-0.00838859j]\n",
       " [ 0.15159851-0.00838859j -0.00967651+0.01738289j  0.0359865 -0.00756527j\n",
       "  -0.15159851+0.00838859j]\n",
       " [ 1.        +0.j          0.15286451-0.00891654j  0.15286451+0.00891654j\n",
       "   1.03075523+0.j        ]]"
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "myU/myU[0, 3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-31T03:58:00.472644Z",
     "start_time": "2019-01-31T03:58:00.168812Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Figure size 576x432 with 2 Axes>,\n",
       " <matplotlib.axes._subplots.AxesSubplot at 0x1a23cf8588>)"
      ]
     },
     "execution_count": 209,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hinton(myU)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-01-31T03:58:01.542956Z",
     "start_time": "2019-01-31T03:58:00.474515Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Figure size 576x432 with 2 Axes>,\n",
       " <matplotlib.axes._subplots.AxesSubplot at 0x1a23f8e8d0>)"
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hinton(L_full_maker(xi_opt, dt))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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",
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