2017-03-17_mg_no_feedback_driven_setup.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from collections import OrderedDict\n",
    "from copy import copy\n",
    "import single_sided_network\n",
    "import qdyn_model\n",
    "from notebook_plots import display_hamiltonian, show_summary\n",
    "from sympy import Symbol\n",
    "import QDYN\n",
    "from QDYN.pulse import blackman, box\n",
    "from QDYN.shutil import mkdir\n",
    "from qnet.algebra.circuit_algebra import move_drive_to_H"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Circuit"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Single Node"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "circuit1 = single_sided_network.network_circuit(n_nodes=1, topology='driven_open')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "circuit1.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "slh1 = single_sided_network.network_slh(n_cavity=2, n_nodes=1, topology='driven_open', inhom=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\begin{pmatrix}1\\end{pmatrix}$"
      ],
      "text/plain": [
       "[[1]]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "slh1.S"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\begin{pmatrix}\\Omega_{a} + \\sqrt{2} \\sqrt{\\kappa} \\hat{a}^{(c_{1})}\\end{pmatrix}$"
      ],
      "text/plain": [
       "[[Ωₐ + √2⋅√κ * â^(c₁)]]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "slh1.L"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/latex": [
       "\\begin{align}\n",
       "  \\hat{H} &= \\hat{H}_0 + \\hat{H}_{\\text{int}} + \\hat{H}_{d_1} + \\hat{H}_{d_2}\\\\\n",
       "  \\hat{H}_0 &= - \\frac{g_{1}^{2}}{\\Delta_{1}} \\hat{a}^{(c_{1})\\dagger} \\hat{a}^{(c_{1})}\\\\\n",
       "  \\hat{H}_{\\text{int}} &= \\frac{g_{1}^{2}}{\\Delta_{1}} \\hat{\\Pi}_{g}^{(q_{1})} \\otimes \\hat{a}^{(c_{1})\\dagger} \\hat{a}^{(c_{1})}\\\\\n",
       "  \\hat{H}_{d_1} &= - \\frac{i \\Omega_{1} g_{1}}{2 \\Delta_{1}} \\left(\\hat{\\sigma}_{e,g}^{(q_{1})} \\otimes \\hat{a}^{(c_{1})} - \\text{c.c.}\\right)\\\\\n",
       "  \\hat{H}_{d_2} &= - \\frac{i \\Omega_{a}}{2} \\sqrt{2} \\sqrt{\\kappa} \\hat{a}^{(c_{1})\\dagger} + \\text{c.c.}\\\\\n",
       "\\end{align}"
      ],
      "text/plain": [
       "<IPython.core.display.Latex object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display_hamiltonian(slh1.H)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Two Nodes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "circuit2 = single_sided_network.network_circuit(n_nodes=2, topology='driven_open')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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9uLg4Ojrq23t4eHhxcUFlY4zW+vLycgsibhXyp5IkIbPLGJNSlmVJZc651prGSe36Bill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\nFf4HyxNB3TnBWCkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "circuit2.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "slh2 = single_sided_network.network_slh(n_cavity=5, n_nodes=2, topology='driven_open')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\begin{pmatrix}1\\end{pmatrix}$"
      ],
      "text/plain": [
       "[[1]]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "slh2.S"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\begin{pmatrix}\\sqrt{2} \\sqrt{\\kappa} \\hat{a}^{(c_{1})} + \\sqrt{2} \\sqrt{\\kappa} \\hat{a}^{(c_{2})}\\end{pmatrix}$"
      ],
      "text/plain": [
       "[[√2⋅√κ * â^(c₁) + √2⋅√κ * â^(c₂)]]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "slh2.L"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/latex": [
       "\\begin{align}\n",
       "  \\hat{H} &= \\hat{H}_0 + \\hat{H}_{\\text{int}} + \\hat{H}_{d_1} + \\hat{H}_{d_2} + \\hat{H}_{d_3}\\\\\n",
       "  \\hat{H}_0 &= - \\frac{g_{1}^{2}}{\\Delta_{1}} \\hat{a}^{(c_{1})\\dagger} \\hat{a}^{(c_{1})} - \\frac{g_{2}^{2}}{\\Delta_{2}} \\hat{a}^{(c_{2})\\dagger} \\hat{a}^{(c_{2})}\\\\\n",
       "  \\hat{H}_{\\text{int}} &= \\frac{g_{1}^{2}}{\\Delta_{1}} \\hat{\\Pi}_{g}^{(q_{1})} \\otimes \\hat{a}^{(c_{1})\\dagger} \\hat{a}^{(c_{1})} + i \\kappa \\hat{a}^{(c_{1})\\dagger} \\otimes \\hat{a}^{(c_{2})} + \\text{c.c.} + \\frac{g_{2}^{2}}{\\Delta_{2}} \\hat{\\Pi}_{g}^{(q_{2})} \\otimes \\hat{a}^{(c_{2})\\dagger} \\hat{a}^{(c_{2})}\\\\\n",
       "  \\hat{H}_{d_1} &= - \\frac{i \\Omega_{1} g_{1}}{2 \\Delta_{1}} \\left(\\hat{\\sigma}_{e,g}^{(q_{1})} \\otimes \\hat{a}^{(c_{1})} - \\text{c.c.}\\right)\\\\\n",
       "  \\hat{H}_{d_2} &= - \\frac{i \\Omega_{2} g_{2}}{2 \\Delta_{2}} \\left(\\hat{\\sigma}_{e,g}^{(q_{2})} \\otimes \\hat{a}^{(c_{2})} - \\text{c.c.}\\right)\\\\\n",
       "  \\hat{H}_{d_3} &= - \\sqrt{2} i \\Omega_{a} \\sqrt{\\kappa} \\hat{a}^{(c_{1})\\dagger} + \\text{c.c.} - \\sqrt{2} i \\Omega_{a} \\sqrt{\\kappa} \\hat{a}^{(c_{2})\\dagger} + \\text{c.c.}\\\\\n",
       "\\end{align}"
      ],
      "text/plain": [
       "<IPython.core.display.Latex object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display_hamiltonian(slh2.H)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Numerical Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#from algebra import generate_num_vals_code\n",
    "#generate_num_vals_code(slh.all_symbols())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "Delta =  5000.0  # MHz\n",
    "g     =    50.0  # MHz\n",
    "kappa =     0.5  # MHz\n",
    "E0    =    70.0  # MHz\n",
    "alpha =  100.0 # MHZ\n",
    "t0    = -4    # microsec\n",
    "T     =  4    # microsec\n",
    "nt    = 2001\n",
    "num_vals = {\n",
    "    Symbol('Delta_1', real=True):   Delta,\n",
    "    Symbol('Delta_2', real=True):   Delta,\n",
    "    Symbol('g_2', positive=True):   g,\n",
    "    Symbol('g_1', positive=True):   g,\n",
    "    Symbol('kappa', positive=True): kappa,\n",
    "    Symbol('alpha', real=True): alpha,\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Time Grid and Guess Pulse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tgrid = QDYN.pulse.pulse_tgrid(t0=t0, T=T, nt=nt) # microsec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "p = QDYN.pulse.Pulse(\n",
    "    tgrid, amplitude=(E0 * blackman(tgrid, t0, T)),\n",
    "    time_unit='microsec', ampl_unit='MHz')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pzero = QDYN.pulse.Pulse(\n",
    "    tgrid, amplitude=(0.0 * blackman(tgrid, t0, T)),\n",
    "    time_unit='microsec', ampl_unit='MHz')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "controls = OrderedDict([\n",
    "    (Symbol('Omega_1'), pzero.copy()),\n",
    "    #(Symbol('Omega_2'), pzero.copy()),\n",
    "    (Symbol('Omega_a'), p.copy()),\n",
    "])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## OCT for qubit excitation (single node)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analytical Pulse Sequence "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import matplotlib.pylab as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "tgrid = QDYN.pulse.pulse_tgrid(t0=t0, T=T, nt=nt) # microsec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "pa = QDYN.pulse.Pulse(\n",
    "    tgrid, amplitude=(0.2*box(tgrid, -0.1, 0.0)),\n",
    "    time_unit='microsec', ampl_unit='sqrt_MHz')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "p1 = QDYN.pulse.Pulse(\n",
    "    tgrid, amplitude=(500*box(tgrid, 0, 0.1)),\n",
    "    time_unit='microsec', ampl_unit='MHz')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "controls = OrderedDict([\n",
    "    (Symbol('Omega_1'), p1),\n",
    "    (Symbol('Omega_a'), pa),\n",
    "])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Propagation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "model = qdyn_model.make_qdyn_oct_single_node_model(\n",
    "    slh1, num_vals, controls, energy_unit='MHz',\n",
    "    mcwf=False, non_herm=True, oct_target='excite_qubit',\n",
    "    lambda_a=1e-4, nodiss=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "model.write_to_runfolder(\"./NO_FB/qb_exc_single_diss_oct_hs001\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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46enBTkdJU4ArgG7VujFl8xTOJ553Ok6mTJzo7qxs187pJCIici3UYSlOeOGF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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa6ad27eba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_summary(\"./NO_FB/qb_exc_single_diss_oct_hs001\", pulses='pulse*.dat', single_node=True, range=(-0.2, 0.2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def plot_ex():\n",
    "    dat = './NO_FB/qb_exc_single_diss_oct_hs001/excitation.dat'\n",
    "    t, q, cav = np.genfromtxt(dat, unpack=True, usecols=(0,1,2))\n",
    "    fig = plt.figure()\n",
    "    ax = fig.add_subplot(111)\n",
    "    ax.plot(t, q)\n",
    "    ax.set_xlim(0,4)\n",
    "    plt.show(fig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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1VaKl2CDntdkg57XZIOe12SDnOdhTknI3xLuANzjnXuucezbwXuA64P0Azrm3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\nKIqiKCvz/wOpGuPp4ujjeQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa6ad35e390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_ex()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## OCT for qubit excitation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "model = qdyn_model.make_qdyn_oct_model(\n",
    "    slh, num_vals, controls, energy_unit='MHz',\n",
    "    mcwf=True, non_herm=True, oct_target='excite_first_qubit',\n",
    "    lambda_a=1e-4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "model.write_to_runfolder(\"./NO_FB/qb_exc1_diss_oct_hs001\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example optimization commands"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*   density matrix optimization (`mcwf=False, non_herm=False`)\n",
    "\n",
    "        OMP_NUM_THREADS=1 qdyn_optimize --internal-units=GHz_units.txt --rho --debug --J_T=J_T_re .\n",
    "        \n",
    "*   trajectory optimization (`mcwf=True, non_herm=True|False`)\n",
    "\n",
    "        OMP_NUM_THREADS=1 qdyn_optimize --internal-units=GHz_units.txt --n-trajs=20 --J_T=J_T_sm ."
   ]
  },
  {
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   "source": [
    "## Example propagation commands"
   ]
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    "* density matrix propagation"
   ]
  },
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   "source": [
    "```\n",
    "OMP_NUM_THREADS=1 qdyn_prop_traj --internal-units=GHz_units.txt --rho --use-oct-pulses --state-label=10 .\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "* trajectory propagation"
   ]
  },
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   },
   "source": [
    "```\n",
    "OMP_NUM_THREADS=1 qdyn_prop_traj --internal-units=GHz_units.txt --n-trajs=10 --use-oct-pulses --state-label=10 .\n",
    "```"
   ]
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