{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Optimization of a dissipative state-to-state transfer in a Lambda system" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "ExecuteTime": { "end_time": "2019-02-25T17:36:59.401589Z", "start_time": "2019-02-25T17:36:58.067052Z" }, "attributes": { "classes": [], "id": "", "n": "1" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "matplotlib 3.0.3\n", "matplotlib.pylab 1.15.4\n", "numpy 1.15.4\n", "scipy 1.2.1\n", "qutip 4.3.1\n", "krotov 0.3.0\n", "CPython 3.6.8\n", "IPython 7.3.0\n" ] } ], "source": [ "# NBVAL_IGNORE_OUTPUT\n", "%load_ext watermark\n", "import os\n", "import qutip\n", "import numpy as np\n", "import scipy\n", "import matplotlib\n", "import matplotlib.pylab as plt\n", "import krotov\n", "import qutip\n", "from qutip import Qobj\n", "import pickle\n", "%watermark -v --iversions" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$\\newcommand{tr}[0]{\\operatorname{tr}}\n", "\\newcommand{diag}[0]{\\operatorname{diag}}\n", "\\newcommand{abs}[0]{\\operatorname{abs}}\n", "\\newcommand{pop}[0]{\\operatorname{pop}}\n", "\\newcommand{aux}[0]{\\text{aux}}\n", "\\newcommand{opt}[0]{\\text{opt}}\n", "\\newcommand{tgt}[0]{\\text{tgt}}\n", "\\newcommand{init}[0]{\\text{init}}\n", "\\newcommand{lab}[0]{\\text{lab}}\n", "\\newcommand{rwa}[0]{\\text{rwa}}\n", "\\newcommand{bra}[1]{\\langle#1\\vert}\n", "\\newcommand{ket}[1]{\\vert#1\\rangle}\n", "\\newcommand{Bra}[1]{\\left\\langle#1\\right\\vert}\n", "\\newcommand{Ket}[1]{\\left\\vert#1\\right\\rangle}\n", "\\newcommand{Braket}[2]{\\left\\langle #1\\vphantom{#2} \\mid\n", "#2\\vphantom{#1}\\right\\rangle}\n", "\\newcommand{Ketbra}[2]{\\left\\vert#1\\vphantom{#2}\n", "\\right\\rangle \\hspace{-0.2em} \\left\\langle #2\\vphantom{#1} \\right\\vert}\n", "\\newcommand{op}[1]{\\hat{#1}}\n", "\\newcommand{Op}[1]{\\hat{#1}}\n", "\\newcommand{dd}[0]{\\,\\text{d}}\n", "\\newcommand{Liouville}[0]{\\mathcal{L}}\n", "\\newcommand{DynMap}[0]{\\mathcal{E}}\n", "\\newcommand{identity}[0]{\\mathbf{1}}\n", "\\newcommand{Norm}[1]{\\lVert#1\\rVert}\n", "\\newcommand{Abs}[1]{\\left\\vert#1\\right\\vert}\n", "\\newcommand{avg}[1]{\\langle#1\\rangle}\n", "\\newcommand{Avg}[1]{\\left\\langle#1\\right\\rangle}\n", "\\newcommand{AbsSq}[1]{\\left\\vert#1\\right\\vert^2}\n", "\\newcommand{Re}[0]{\\operatorname{Re}}\n", "\\newcommand{Im}[0]{\\operatorname{Im}}\n", "\\newcommand{toP}[0]{\\omega_{12}}\n", "\\newcommand{toS}[0]{\\omega_{23}}$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The aim of this example is to test the\n", "behaviour of Krotov's algorithm when\n", "dealing with non-Hermitian Hamiltonians.\n", "This notebook is heavily based on the\n", "example \"Optimization of a state-to-state\n", "transfer in a lambda system with RWA\",\n", "and therefore it is recommended that the\n", "reader become familiar with the system\n", "beforehand. The main change in the\n", "results will be represented by the loss of\n", "norm during the propagation." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Define the Hamiltonian" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We start with the usual 3-level lambda system such that\n", "$E_1 < E_2$ and $E_3 < E_2$. \n", "The states $\\Ket{1}$ and $\\Ket{2}$ are\n", "coupled through a pump\n", "laser with frequency $\\omega_{P}$ and\n", "similarly for the states $\\Ket{2}$ and $\\Ket{3}$ \n", "through a Stokes laser with frequency\n", "$\\omega_{S}$.\n", "\n", "The level $\\Ket{2}$ is assumed to\n", "experience a\n", "spontaneous decay into a reservoir that is out of the Hilbert\n", "space. This can be\n", "reproduced with a simple model that adds a dissipative\n", "coefficient $\\gamma > 0$,\n", "such that the original eigenvalue for the second level\n", "is modified into $E_2\n", "\\rightarrow E_2 - i \\gamma$.\n", "\n", "We define the pulse envelopes the same as we did before: \n", "$\\varepsilon_{P}(t) =\\frac{\\Omega_{P}^{(1)}(t)}{\\mu_{12}} \\cos{(\\omega_P t)} - \\frac{\\Omega_{P}^{(2)}(t)}{\\mu_{12}} \\sin{(\\omega_P t)}$ and \n", "$\\varepsilon_{S}(t) = \\frac{\\Omega_{S}^{(1)}(t)}{\\mu_{23}} \\cos{(\\omega_S t)} - \\frac{\\Omega_{S}^{(2)}(t)}{\\mu_{23}} \\sin{(\\omega_S t)}$, with $\\mu_{ij}$ the $ij^{\\text{th}}$ dipole-transition moment.\n", "\n", "We perform the same\n", "rotating wave approximation as\n", "described in the aforementioned example and\n", "obtain the time independent\n", "Hamiltonian\n", "\n", "\\begin{equation}\n", " \\op{H}_{0} =\n", " \\Delta_{P}\\Ketbra{1}{1} -i\n", "\\gamma \\Ketbra{2}{2} +\\Delta_{S} \\Ketbra{3}{3}\n", "\\end{equation}\n", "with the\n", "detunings $\\Delta_{P}=E_{1} + \\omega_{P} - E_{2}$ and\n", "$\\Delta_{S} = E_{3} +\n", "\\omega_{S} -E_{2}$.\n", "\n", "The control\n", "Hamiltonian is given by \n", "\n", "$$\\op{H}_{1}(t) = \\op{H}_{1,P}(t) +\n", "\\op{H}_{1,S}(t) = -\\frac{1}{2} \\Omega_{P}(t) \\Ketbra{1}{2} -\\frac{1}{2} \\Omega_{S}(t)\\Ketbra{2}{3} +\n", "\\text{h.c.}\\,\\,,$$\n", "\n", "where $\\Omega_{P}(t) = \\Omega_{P}^{(1)}(t) + i\\Omega_{P}^{(2)}(t)$ and \n", "$\\Omega_{S}(t) = \\Omega_{S}^{(1)}(t) + i\\Omega_{S}^{(2)}(t)$.\n", "\n", "\n", "Once again, we optimize the real and imaginary parts of $\\Omega_{P}$ and $\\Omega_{S}$ separately and we treat them as four independent real controls. " ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "ExecuteTime": { "end_time": "2019-02-25T17:36:59.428292Z", "start_time": "2019-02-25T17:36:59.406753Z" }, "attributes": { "classes": [], "id": "", "n": "2" } }, "outputs": [], "source": [ "T = 5. \n", "\n", "def ham_and_states(T=T):\n", " \"\"\"Lambda-system Hamiltonian\"\"\"\n", " E1 = 0.0\n", " E2 = 10.0\n", " E3 = 5.0\n", " ω_P = 9.5\n", " ω_S = 4.5\n", " gamma = 0.5\n", " Ω_init = 5.0\n", " H0 = Qobj(\n", " [\n", " [E1 + ω_P - E2, 0.0, 0.0],\n", " [0.0, -gamma * 1.0j, 0.0],\n", " [0.0, 0.0, E3 + ω_S - E2],\n", " ]\n", " )\n", "\n", " H1P_re = -0.5*Qobj([[0.0, 1.0, 0.0], [1.0, 0.0, 0.0], [0.0, 0.0, 0.0]])\n", " H1P_im = -0.5*Qobj([[0.0, 1.0j, 0.0], [-1.0j, 0.0, 0.0], [0.0, 0.0, 0.0]])\n", " ΩP_re = lambda t, args: Ω_init\n", " ΩP_im = lambda t, args: Ω_init\n", "\n", " H1S_re = -0.5*Qobj([[0.0, 0.0, 0.0], [0.0, 0.0, 1.0], [0.0, 1.0, 0.0]])\n", " H1S_im = -0.5*Qobj([[0.0, 0.0, 0.0], [0.0, 0.0, 1.0j], [0.0, -1.0j, 0.0]])\n", " ΩS_re = lambda t, args: Ω_init\n", " ΩS_im = lambda t, args: Ω_init\n", "\n", " \"\"\"Initial and target states\"\"\"\n", " psi0 = qutip.Qobj(np.array([1.0, 0.0, 0.0]))\n", " psi1 = qutip.Qobj(np.array([0.0, 0.0, 1.0])) \n", " \n", " #State transformation to the rotating frame\n", " psi1 *= np.exp(1j * (E2 - ω_S) * T)\n", "\n", " return (\n", " [\n", " H0,\n", " [H1P_re, ΩP_re],\n", " [H1P_im, ΩP_im],\n", " [H1S_re, ΩS_re],\n", " [H1S_im, ΩS_im],\n", " ],\n", " psi0,\n", " psi1,\n", " )\n", "\n", "\n", "H, psi0, psi1 = ham_and_states()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We check whether our Hamiltonians are Hermitian:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "ExecuteTime": { "end_time": "2019-02-25T17:36:59.444955Z", "start_time": "2019-02-25T17:36:59.437053Z" }, "attributes": { "classes": [], "id": "", "n": "3" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "H0 is Hermitian: False\n", "H1 is Hermitian: True\n" ] } ], "source": [ "print(\"H0 is Hermitian: \" + str(H[0].isherm))\n", "print(\"H1 is Hermitian: \"+ str(\n", " H[1][0].isherm\n", " and H[2][0].isherm\n", " and H[3][0].isherm\n", " and H[4][0].isherm))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We introduce projectors for each of the three energy levels\n", "$\\op{P}_{i} =\n", "\\Ketbra{i}{i}$" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "ExecuteTime": { "end_time": "2019-02-25T17:36:59.462535Z", "start_time": "2019-02-25T17:36:59.451693Z" }, "attributes": { "classes": [], "id": "", "n": "4" } }, "outputs": [], "source": [ "proj1 = Qobj([[1.,0.,0.],[0.,0.,0.],[0.,0.,0.]])\n", "proj2 = Qobj([[0.,0.,0.],[0.,1.,0.],[0.,0.,0.]])\n", "proj3 = Qobj([[0.,0.,0.],[0.,0.,0.],[0.,0.,1.]])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Define the optimization target\n", "\n", "It is necessary to create a time grid to work\n", "with. In this example we choose a time interval starting at $t_0 = 0$ and ending\n", "at $t_f = 5$ with $n_t = 500$ equidistant time steps." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "ExecuteTime": { "end_time": "2019-02-25T17:36:59.479057Z", "start_time": "2019-02-25T17:36:59.467852Z" }, "attributes": { "classes": [], "id": "", "n": "5" } }, "outputs": [], "source": [ "t0 = 0.\n", "nt = 500\n", "tlist = np.linspace(t0, T, nt)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We define the objective to be a state to state transfer from the initial state\n", "$\\Ket{\\Psi_{\\init}} = \\Ket{1}$ into the final state $\\Ket{\\Psi_{\\tgt}} =\n", "\\Ket{3}$ at the\n", "final time $t_{f}$." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "ExecuteTime": { "end_time": "2019-02-25T17:36:59.489573Z", "start_time": "2019-02-25T17:36:59.483475Z" }, "attributes": { "classes": [], "id": "", "n": "6" } }, "outputs": [], "source": [ "objectives = [ krotov.Objective(initial_state=psi0, target=psi1, H=H) ]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Initial guess shapes" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\"Stimulated Raman adiabatic passage\" (STIRAP) is a\n", "process in which population in $\\Ket{1}$ is transferred into\n", "$\\Ket{3}$ without\n", "having to pass through $\\Ket{2}$, which is a rapidly decaying level in our example.\n", "In order for this process to occur, a temporally finite Stokes pulse of\n", "sufficient amplitude driving the $\\Ket{2} \\leftrightarrow \\Ket{3}$ transition is\n", "applied first, whilst second pump pulse of similar intensity follows some time\n", "later such that the pulses still have a partial temporal overlap.\n", "\n", "In order to\n", "demonstrate the Krotov's optimization method however, we choose an initial guess\n", "consisting of two low intensity and real Blackman pulses which are temporally\n", "disjoint.\n", "\n", "For the real components of the matrix elements, we supply our guess\n", "pulses shaped as Blackman window functions `S(t,offset)`, with an offset\n", "ensuring that the two pulses don't overlap.\n", "The imaginary components are coupled\n", "to pulses that are zero at all times." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "ExecuteTime": { "end_time": "2019-02-25T17:36:59.504225Z", "start_time": "2019-02-25T17:36:59.493376Z" }, "attributes": { "classes": [], "id": "", "n": "7" } }, "outputs": [], "source": [ "def S(t,offset):\n", " \"\"\"Shape envelope function for the field update\"\"\"\n", " return krotov.shapes.blackman(t,1.+offset,4.+offset)\n", "\n", "def shape_field_real(eps,offset):\n", " \"\"\"Applies the total pulse shape to the real part of a guess pulse\"\"\"\n", " field_shaped = lambda t, args: eps(t, args)*S(t,offset)\n", " return field_shaped\n", "\n", "def shape_field_imag(eps,offset):\n", " \"\"\"Initializes the imaginary parts of the guess pulses to zero\"\"\"\n", " field_shaped = lambda t, args: eps(t, args)*0.\n", " return field_shaped\n", "\n", "H[1][1] = shape_field_real(H[1][1],1.)\n", "H[2][1] = shape_field_imag(H[2][1],1.)\n", "H[3][1] = shape_field_real(H[3][1],-1.)\n", "H[4][1] = shape_field_imag(H[4][1],-1.)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We choose an appropriate update factor $\\lambda_{a}$ for the problem at hand and\n", "make sure Krotov considers pulses which start and end with zero amplitude." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "ExecuteTime": { "end_time": "2019-02-25T17:36:59.516313Z", "start_time": "2019-02-25T17:36:59.507855Z" }, "attributes": { "classes": [], "id": "", "n": "8" } }, "outputs": [], "source": [ "def update_shape(t):\n", " \"\"\"Scales the Krotov methods update of the pulse value at the time t\"\"\"\n", " return krotov.shapes.flattop(t,0.,T,0.3,func='sinsq')" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "ExecuteTime": { "end_time": "2019-02-25T17:36:59.527001Z", "start_time": "2019-02-25T17:36:59.519354Z" } }, "outputs": [], "source": [ "opt_lambda = 2.0\n", "pulse_options = {\n", " H[1][1]: dict(lambda_a=opt_lambda, shape=update_shape),\n", " H[2][1]: dict(lambda_a=opt_lambda, shape=update_shape),\n", " H[3][1]: dict(lambda_a=opt_lambda, shape=update_shape),\n", " H[4][1]: dict(lambda_a=opt_lambda, shape=update_shape),\n", "}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It is possible to keep track of the fidelity during optimization by printing it\n", "after every iteration:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "ExecuteTime": { "end_time": "2019-02-25T17:36:59.535937Z", "start_time": "2019-02-25T17:36:59.529418Z" }, "attributes": { "classes": [], "id": "", "n": "10" } }, "outputs": [], "source": [ "def print_fidelity(**args):\n", " F_re = np.average(np.array(args['tau_vals']).real)\n", " print(\" F = %f\" % F_re)\n", " return F_re" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Simulate dynamics of the guess field" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "ExecuteTime": { "end_time": "2019-02-25T17:36:59.549721Z", "start_time": "2019-02-25T17:36:59.540627Z" }, "attributes": { "classes": [], "id": "", "n": "11" } }, "outputs": [], "source": [ "def plot_pulse(pulse, tlist, plottitle=None):\n", " fig, ax = plt.subplots()\n", " if callable(pulse):\n", " pulse = np.array([pulse(t, args=None) for t in tlist])\n", " ax.plot(tlist, pulse)\n", " ax.set_xlabel('time')\n", " ax.set_ylabel('pulse amplitude')\n", " if(isinstance(plottitle, str)):\n", " ax.set_title(plottitle, fontsize = 15)\n", " plt.show(fig)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "ExecuteTime": { "end_time": "2019-02-25T17:37:00.833460Z", "start_time": "2019-02-25T17:36:59.553062Z" }, "attributes": { "classes": [], "id": "", "n": "12" } }, "outputs": [ { "data": { "image/png": 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\n", 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\n", 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plot_pulse(H[1][1], tlist, plottitle='Re($\\Omega_P$)')\n", "plot_pulse(H[2][1], tlist, plottitle='Im($\\Omega_P$)')\n", "plot_pulse(H[3][1], tlist, plottitle='Re($\\Omega_S$)')\n", "plot_pulse(H[4][1], tlist, plottitle='Im($\\Omega_S$)')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "After assuring ourselves that our guess pulses appear as expected, we propagate\n", "the system using our guess. Since the pulses are temporally disjoint, we expect\n", "the first pulse to have no effect, whilst the second merely transfers population\n", "out of $\\Ket{1}$ into $\\Ket{2}$ and back again." ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "ExecuteTime": { "end_time": "2019-02-25T17:37:04.084531Z", "start_time": "2019-02-25T17:37:00.836466Z" }, "attributes": { "classes": [], "id": "", "n": "13" } }, "outputs": [], "source": [ "guess_dynamics = objectives[0].propagate(\n", " tlist, propagator=krotov.propagators.expm, e_ops=[proj1, proj2, proj3]\n", ")\n", "guess_states = objectives[0].propagate(\n", " tlist, propagator=krotov.propagators.expm\n", ")" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "ExecuteTime": { "end_time": "2019-02-25T17:37:04.100900Z", "start_time": "2019-02-25T17:37:04.087376Z" }, "attributes": { "classes": [], "id": "", "n": "14" } }, "outputs": [], "source": [ "def plot_population(result):\n", " fig, ax = plt.subplots()\n", " ax.axhline(y=1.0, color='black', lw=0.5, ls='dashed')\n", " ax.axhline(y=0.0, color='black', lw=0.5, ls='dashed')\n", " ax.plot(result.times, result.expect[0], label='1')\n", " ax.plot(result.times, result.expect[1], label='2')\n", " ax.plot(result.times, result.expect[2], label='3')\n", " ax.legend()\n", " ax.set_title('Expected values', fontsize = 15)\n", " ax.set_xlabel('time')\n", " ax.set_ylabel('population')\n", " plt.show(fig)\n", " \n", "def plot_norm(result):\n", " \n", " state_norm = lambda i: result.states[i].norm()\n", " states_norm=np.vectorize(state_norm)\n", " \n", " fig, ax = plt.subplots()\n", " ax.plot(result.times, states_norm(np.arange(len(result.states))))\n", " ax.set_title('Norm loss', fontsize = 15)\n", " ax.set_xlabel('time')\n", " ax.set_ylabel('state norm')\n", " plt.show(fig) " ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "ExecuteTime": { "end_time": "2019-02-25T17:37:04.620353Z", "start_time": "2019-02-25T17:37:04.107208Z" }, "attributes": { "classes": [], "id": "", "n": "15" } }, "outputs": [ { "data": { "image/png": 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\n", 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plot_population(guess_dynamics)\n", "plot_norm(guess_states)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Optimize" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We now use all the information that we have gathered to initialize\n", "the optimization routine. That is: \n", "\n", "* The `objectives`: transferring population\n", "from $\\ket{1}$ to $\\ket{3}$ at $t_f$. \n", "\n", "* The `pulse_options`: initial pulses\n", "and their shapes restrictions. \n", "\n", "* The `propagator`: in our example we will\n", "choose a simple matrix exponential.\n", "\n", "* The `chi_constructor`: the optimization\n", "functional to use. \n", "\n", "* The `info_hook`: all processes taking place inbetween\n", "iterations, for example printing the fidelity in each step. \n", "\n", "* And the\n", "`iter_stop`: the number of iterations to perform the optimization." ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "ExecuteTime": { "end_time": "2019-02-25T17:38:48.404127Z", "start_time": "2019-02-25T17:37:04.624207Z" }, "attributes": { "classes": [], "id": "", "n": "16" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " F = -0.007812\n", " F = 0.055166\n", " F = 0.117604\n", " F = 0.178902\n", " F = 0.238507\n", " F = 0.295926\n", " F = 0.350749\n", " F = 0.402648\n", " F = 0.451388\n", " F = 0.496822\n", " F = 0.538882\n", " F = 0.577573\n", " F = 0.612961\n", " F = 0.645161\n", " F = 0.674324\n", " F = 0.700629\n", " F = 0.724268\n", " F = 0.745445\n", " F = 0.764364\n", " F = 0.781226\n", " F = 0.796224\n", " F = 0.809541\n", " F = 0.821349\n", " F = 0.831809\n", " F = 0.841064\n", " F = 0.849250\n", " F = 0.856486\n", " F = 0.862881\n", " F = 0.868532\n", " F = 0.873527\n", " F = 0.877942\n", " F = 0.881847\n", " F = 0.885302\n", " F = 0.888362\n", " F = 0.891074\n", " F = 0.893481\n", " F = 0.895618\n", " F = 0.897519\n", " F = 0.899211\n", " F = 0.900721\n", " F = 0.902071\n" ] } ], "source": [ "oct_result = krotov.optimize_pulses(\n", " objectives, pulse_options, tlist,\n", " propagator=krotov.propagators.expm,\n", " chi_constructor=krotov.functionals.chis_re,\n", " info_hook=krotov.info_hooks.chain(print_fidelity),\n", " iter_stop=40\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can check that the algorithm takes into account the non Hermitian behaviour\n", "which leads to a non unitary final state. To improve the fidelity it is\n", "necessary to avoid these dissipative effects as much as possible." ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "ExecuteTime": { "end_time": "2019-02-25T17:38:48.921312Z", "start_time": "2019-02-25T17:38:48.406566Z" }, "attributes": { "classes": [], "id": "", "n": "17" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "pump pulse amplitude and phase:\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Stokes pulse amplitude and phase:\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "def plot_pulse_amplitude_and_phase(pulse_real, pulse_imaginary,tlist):\n", " ax1 = plt.subplot(211)\n", " ax2 = plt.subplot(212)\n", " amplitudes = [np.sqrt(x*x + y*y) for x,y in zip(pulse_real,pulse_imaginary)]\n", " phases = [np.arctan2(y,x)/np.pi for x,y in zip(pulse_real,pulse_imaginary)]\n", " ax1.plot(tlist,amplitudes)\n", " ax1.set_xlabel('time')\n", " ax1.set_ylabel('pulse amplitude')\n", " ax2.plot(tlist,phases)\n", " ax2.set_xlabel('time')\n", " ax2.set_ylabel('pulse phase (π)') \n", " plt.show()\n", " \n", "print(\"pump pulse amplitude and phase:\")\n", "plot_pulse_amplitude_and_phase(\n", " oct_result.optimized_controls[0], oct_result.optimized_controls[1], tlist)\n", "print(\"Stokes pulse amplitude and phase:\")\n", "plot_pulse_amplitude_and_phase(\n", " oct_result.optimized_controls[2], oct_result.optimized_controls[3], tlist)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We check the evolution of the population due to our optimized pulses." ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "ExecuteTime": { "end_time": "2019-02-25T17:38:51.038824Z", "start_time": "2019-02-25T17:38:48.932205Z" }, "attributes": { "classes": [], "id": "", "n": "18" } }, "outputs": [], "source": [ "opt_dynamics = oct_result.optimized_objectives[0].propagate(\n", " tlist, propagator=krotov.propagators.expm, e_ops=[proj1, proj2, proj3])\n", "opt_states = oct_result.optimized_objectives[0].propagate(\n", " tlist, propagator=krotov.propagators.expm)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "ExecuteTime": { "end_time": "2019-02-25T17:38:51.493035Z", "start_time": "2019-02-25T17:38:51.044798Z" }, "attributes": { "classes": [], "id": "", "n": "19" } }, "outputs": [ { "data": { "image/png": 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\n", 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plot_population(opt_dynamics)\n", "plot_norm(opt_states)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As we can see the algorithm takes into account the decay in level $\\ket{2}$ and\n", "minimizes the population in this level during the process.\n", "\n", "However, convergence\n", "is relatively slow, and after 40 iterations, we have only\n", "achieved a 90%\n", "fidelity. As we can see from the population dynamics, we do not\n", "fully transfer\n", "the population in state $\\ket{3}$, and there is still non-\n", "negligible population\n", "in state $\\ket{2}$. If we were to continue up to iteration\n", "2000, the\n", "optimization converges much farther:" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "ExecuteTime": { "end_time": "2019-02-25T19:11:28.324483Z", "start_time": "2019-02-25T17:38:51.495496Z" } }, "outputs": [], "source": [ "dumpfile = \"./non_herm_oct_result.dump\"\n", "if os.path.isfile(dumpfile):\n", " oct_result = krotov.result.Result.load(dumpfile, objectives)\n", "else:\n", " oct_result = krotov.optimize_pulses(\n", " objectives, pulse_options, tlist,\n", " propagator=krotov.propagators.expm,\n", " chi_constructor=krotov.functionals.chis_re,\n", " info_hook=krotov.info_hooks.chain(print_fidelity),\n", " iter_stop=2000,\n", " continue_from=oct_result\n", " )\n", " oct_result.dump(dumpfile)" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "ExecuteTime": { "end_time": "2019-02-25T19:11:28.335319Z", "start_time": "2019-02-25T19:11:28.328930Z" }, "attributes": { "classes": [], "id": "", "n": "21" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Final fidelity: 0.966\n" ] } ], "source": [ "print(\"Final fidelity: %.3f\" % oct_result.info_vals[-1])" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "ExecuteTime": { "end_time": "2019-02-25T19:11:28.351753Z", "start_time": "2019-02-25T19:11:28.339249Z" }, "attributes": { "classes": [], "id": "", "n": "22" } }, "outputs": [], "source": [ "def plot_convergence(result):\n", " fig, ax = plt.subplots()\n", " ax.semilogy(result.iters, 1-np.array(result.info_vals))\n", " ax.set_xlabel('OCT iteration')\n", " ax.set_ylabel('error')\n", " plt.show(fig)" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "ExecuteTime": { "end_time": "2019-02-25T19:11:28.942896Z", "start_time": "2019-02-25T19:11:28.362214Z" }, "attributes": { "classes": [], "id": "", "n": "23" } }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plot_convergence(oct_result)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The dynamics now show a very good population transfer and negligible population\n", "in state $\\ket{2}$." ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "ExecuteTime": { "end_time": "2019-02-25T19:11:32.849633Z", "start_time": "2019-02-25T19:11:28.949451Z" }, "attributes": { "classes": [], "id": "", "n": "24" } }, "outputs": [], "source": [ "opt_dynamics = oct_result.optimized_objectives[0].propagate(\n", " tlist, propagator=krotov.propagators.expm, e_ops=[proj1, proj2, proj3])\n", "opt_states = oct_result.optimized_objectives[0].propagate(\n", " tlist, propagator=krotov.propagators.expm)" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "ExecuteTime": { "end_time": "2019-02-25T19:11:33.619726Z", "start_time": "2019-02-25T19:11:32.852452Z" }, "attributes": { "classes": [], "id": "", "n": "25" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "pump pulse amplitude and phase:\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Stokes pulse amplitude and phase:\n" ] }, { "data": { "image/png": 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hVBDsKi3Zy92nD+Xu04fy5ZIizhn/HVsrbErUeNRsUgg1QfWoaro1SW09qn1+/vbeAnrlZnLxwfluh9PqjO7VjpOHd+HJL5Yn7Axtq7ZUcPpj37Jm6w6e/fX+/Gr/Hnt9znNH9+CpcQUs3lTGhc98T8mO2ghEalqTcOdT2EdERovIYXWL04GZpj0xZQUrtlRw+ymDSU2yViG7c/MJAxGBOz9Y6HYoUbd22w7Of/p7dtT4ePWKAzksgg+Ijx7UkScvHMXSTeWMe24aZVWWGOJJOD2afwN8CXwM3BF6vd3ZsExTVm6p4NEpyzhpWGdrDdKELjnp/O7Ivnw4byOTF292O5yo2VBSyXlPfU9ZVS0vXjrGkaa5Rw7owKPnj2T+uhIumzDDBiOMI+GUFK4F9gdWq+qRwAhgu6NRmUapKrf+dy6pXg9/PWlft8Np9S47rDd98jL5y3/nJcRD56Kyas5/6nu2VtQwwaGEUGfsvh2576zhfLdiKze9OQfr3xofwkkKVapaBSAiqaq6CBjgbFimMe/MXs83y4r54/ED6GAPl5uVmuTlztOGsnZbJQ9/vtTtcBy1o8bHb16YzvqSSp67eH/2657j+DVPHdGVG8b2561Z63jgs/j+800U4SSFtSKSA/wX+FRE3gFWOxuW2Z2SHbX844MFDO+ew3ljerodTsw4oHd7zhjZjfFfrmDJpjK3w3GEP6D8fuIs5q4r4eFzR7J/fvT6rPzuqL6cOaobD/5vKW8Wro3adY0zwml9dJqqblfV24G/AM8ApzodmPm5uyYtZNuOWu46bUjC91xuqVt+MYg2aUnc8vbcuJu6U1W54735fLZwM7efMpix+3aM6vVFhLtOG8pBfdpz01tzmL5qa1SvbyKrRd0SVfULVX1XVa2BcpRNWbyZV2es4bJDezO4i43p01LtMlP48wmDmL5qG6/OWON2OBH19FcrmTB1NZcf1ptxB+a7EkNKkofHzx9F930yuOLFQtZsTcxmwPHA+qrHgNKqWm5+ay79OrThumP6uR1OzDpzVDfG9GrHXR8sZENJpdvhRMTnizZx14cLOXFoJ9cnVmqbkczTFxXg8wf4zQszrKlqjLKkEAP+8f4CNpVWce9Zw22kyr3g8Qj/d+YwfAHl5rfmxnxrmWWby7l24mz27ZzN/Wft1yrm4+6d14bHLxjFsqJyrn1lNv44q6pLBOF2XuspIseE1tNFJCuMz3QXkckiskBE5ovItbs55ggRKRGR2aHlry3/CvFtyuLNvDZjLVcc3icqrUniXc/2mfzp+AFMWVzEGzH8ULSkspbLJ8wgJcnD+HEFpKe0npuFg/vmcvspg/l80Wb++WHidRyMdeF0XrsMeAN4MrSrG8GWSM3xATeo6r7AAcDVIrK7hvVfqep+oeVvYcadEEoqd1YbXXu0VRtFyrgD8xmd346/vb+AjSWxN0ubP6Bc98osfty6g8cvGEXXnHS3Q/qZCw/oyUUH9uSpr1by6vQf3Q7HtEA4JYWrgYOBUgBVXQp0aO5DqrpBVWeG1suAhUDXPQ81sagqf357LpvLqq3aKMI8HuGeM4dR6w9wy9uxV410/yeLmby4iNtOGczoXq13uPS/nLQvh/bL5db/zuO7FcVuh2PCFE5SqG7Y2khEkoAW/S8SkXyCPaG/383bB4rIDyLyoYgMbuTzl4vIDBGZUVRU1JJLx6zXZ6zlgzkbuH5sf6s2ckCv3Ez+cNxA/rdoM6/FUGuk9+es57Epyzl3dHcuGLP3A9w5Kcnr4ZHzRtK9XQZXvlTIj8XWIikWhJMUvhCRPwPpIjIWeB14L9wLiEgb4E3gOlX96ZDbM4GeqjoceJhGqqVUdbyqFqhqQV5e/I/1s2xzObe9O5+D+rTnt4f3cTucuHXxQfkc1Kc9d7y3gJVbKtwOp1nz15fwh9fnUNBzH+44ZUhMzCrXNj2ZZy/an4DCpS9Mp9RaJLV64SSFm4AiYC5wBTAJuDWck4tIMsGE8LKqvvXT91W1VFXLQ+uTgGQRyQ0z9rhU7fPz+4mzSEv28O9f7Wed1Bzk8Qj3nz2cZK+H616ZRa2/9Q7qVlxezeUTCmmbnsxjF4wkJSl2Gg7m52by+AUjWbmlgmv+MwtfK/5zNuH1aA6o6lOqehZwOfC9hlEJK8HbmGeAhar6r0aO6RQ6DhEZHYonoSsfb393Pgs2lHLvmcNt4pwo6Nw2nX+ePpQf1pbwYCsdu6fWH+Dq/8ykqLya8eNG0SEr9v5dHNQnl7+fOoQvlhRx16RFbodjmtDsFEwiMgU4JXRsIbBZRL5V1f/XzEcPBi4E5orI7NC+PwM9AFT1CeBM4EoR8QGVwDnhJJx4NXHaj0yctoarjujDMVEeqiCRnTC0M2eN6sajU5ZxaL9cxvRu73ZIu7jzg4V8t2Ir/zp7OMO6xe7zpXNH92DppnKe/WYlfTu04bxW/kwkUYUzL19bVS0NzaswQVVvE5E5zX1IVb8Gmqz7UNVHgEfCCzW+zfpxG7e9M59D++Vyw7E2CG203XbKYKat2sp1r87mg98fSrvMFLdDAuDV6T/y/LeruOzQXpw+spvb4ey1P584kBVbyvnrO/PIb5/BQX0Tura4VQqnYjJJRDoDZwPvOxxPQtpcVsWVL82kQ3YqD50zwp4juKBNahIPnzuC4vIarn9tdqsYNK9w9VZu/e88Du2Xy59cHsIiUpK8Hh46dwS9cjO5bMIMZtjgea1OOEnhbwRnW1umqtNFpDfQOitfY1BwDPwZlFTW8uSFo9inldyhJqJh3XL4y0mDmLK4iMe/WO5qLBtKKrnixZl0yUnn4XNHkOSNnQfLzclOS+al34yhQ3Yav35uOjN/3OZ2SKaBcB40v66qw1T1qtD2ClU9w/nQ4p/PH+Ca/8xi3roSHj53hI1+2gpccEBPTh7ehfs/WczU5e60eaiq9XPFi4VU1vh4alwBORnxd6PQMTuNiZcdQPs2KVz0zDRmr7HJHFsLaey5rog8TBOd1FT1904F1ZSCggKdMWOGG5eOKFXlL+/M46XvfuTvvxzMhS4NeWx+rrzaxymPfE1ZlY8Pfn9IVFv7qCrXv/YDb89ax/gLR3Hs4E5Ru7Yb1m+v5Jzx31FcXs3jF4ziMJtz3DEiUqiqBc0d11RJYQbB1kaNLWYvPPi/pbz03Y9ccXhvSwitTJvUJB4/fxRlVbVc9dLMqE5K/8zXK3l71jquH9s/7hMCQJecdF7/7YF0b5fBJc9P5+1ZsTtIYbxotKTQWsVDSeHRycu49+PFnDmqG/93xrBWMeSx+bn3fljPNRNnce7o7tx12lDHexB/saSIi5+bxnGDO/HoeSMT6t9FaVUtV0woZOqKYq46og83HDvAGlxEWLglhXD6KUxmN9VIqnrUHsaW0J74Yjn3fryY00d05R5LCK3aycO7sGhjKY9OXs6gztmOzmq2eGMZV788k/4ds7jvrOEJ9+8iOy2Z5y/Zn9vemc9jU5Yzd10JD50zwhpeuCCcfgo3NlhPA84gOCy2aQFV5dHJy7jvkyWcPLwL95413O6EYsANYwewaEMZd7y3gH4dsjiwT+Q7tm0uq+KS56eTkeLl2V/vT2ZqOP8t409qkpd/njGM4d1zuO2d+Rz/4Jf835nDOdyeM0RVOK2PChss36jq9cARzocWP/wB5bZ353PfJ0s4bURX/n22JYRY4fEID5yzH71yM7nq5cjPPVxV6+eyCYVsrajhmYv2p0srnBsh2s4d3YO3rjqI7LRkLnp2Gre8PZeSShtIL1rCmWSnXYMlV0SOA6ztZJiqav1cM3EmE6au5orDenP/WcPjqs15IshKS+apcQX4AxrRkT4DAeX612YzZ+12HjhnP4Z2s/9WdYZ0bct71xzCbw7pxX+m/chR903htelrWkWnwngXzq9TITtbIk0FbgAudTKoeLFueyW/enIqk+Zu5NZfDOLmEwclXF1xvOiVm8njF4xiRVEFV75UGJEWSfd9sphJczfy5xMGcVwCtDRqqbRkL7eetC/v/e4Q8nMz+eObczjhwa/4YM4GSw4OstZHDvlm2RaumTiLGl+A+88ebv/p48QbhWu58fUfOGNkN+47a9get0iaOO1Hbn5rLueO7sFdp8XG3AhuUlXe/WE9D/5vKSuKKujfsQ2XHdqbk4d3sVkJwxRu66Nmk4KIpAFXAYcQbIX0FfCEqroyuW1rTwq1/gCPfL6Mhz9fSp+8Njxx4Sj65LVxOywTQQ98toQHPlvKdcf047pj+rf48x/P38iVLxVyWP88nhpXQLJVJ4bNH1Den7OeRz5fxtLN5WSnJXHmqO6cOaobgzpnWXJtQiSTwmtAGfBSaNd5QE5ofoWoa81JYdHGUm547Qfmry/ltBFd+cepQxK2JUk8U1X+8MYc3ihcy31nDefMUeGPXjp91VYuePp7BnbOZuJlY8hIsX8fe0JV+X7lVl7+/kc+mreBWr/SKzeTE4Z04oQhnRncJduqan8ikklhgaru29y+aGmNSWFHjY8nvljB41OWkZ2WzJ2nDeX4IVZdFM9qfAEufn4a36/YyvhxozhqYPPzXyzZVMaZj39LbptU3rjyoFYzPHesKy6v5pMFm5g0dwPfLi/GH1DaZ6ZwQJ/2HNwnl4P7tqdHu4yEL0VEMim8BDyiqt+FtscAV6vquIhE2kKtKSnU1XP+88NFbCip4uThXbj95H1p3ybV7dBMFJRV1XLeU9+zZFMZL1wymgOamJxn/fZKznj8W3wB5a0rD6J7u4woRpo4tlXU8PmizXyzbAvfLN/CptJqAPKyUhneLYf9urdlePcchnXNoW1GssvRRlckk8JCYADwY2hXD2AxwQ5sqqrD9jLWFmkNSSEQUD5duIlHPl/G3HUlDOmazW0nD2b//HauxmWib2tFDWc/OZWNJVX857Ixu50ZbXNpFb8a/x1byqp59YoD2bdLtguRJh5VZXlRBVOXb2HWmu38sGY7y4sq6t/vmpPOgE5ZwaVj8LV3XiapSfH54DqSSaFnU++r6uoWxrZX3EwKNb4AH87bwONTlrNoYxk922fwuyP7csbIblZ/mcA2llRx5hPfUlHt47UrDqRfx6z697aUV3PO+O9Yv72SFy8dzaieduPgppLKWuatK2H2mu0s3ljGkk1lLC8qp9Yf/B30eoReuZnkt8+kV24GvXLbkJ+bQa/cTDpmpcX0//OIJYW9DOJ44EHACzytqv/8yfupwARgFFAM/EpVVzV1TjeSwuayKl6ZtoaXvlvN5rJqeudlcs1RfTl5WBfriGYAWLWlgrOenEogoDx/8WiGdmvL2m07uOT56fy4dQfPX9x09ZJxT40vwKriChZtLGNJKFGsKq5gVfGOXfqjpCV7yG+fSc/2GXTNyaBLThpdc9LpElpy26S06ucWricFEfECS4CxwFpgOnCuqi5ocMxVwDBV/a2InAOcpqq/auq80UgKqsqPW3fwzbJiPpi7nqnLiwkoHN4/j18fnM/h/fJi+o7BOGNFUTkXPjON4opqfjm8K58t3ESNP8CTF4yyuYhjUCCgbCitYmVRBSuLK1i1JbQUV7B+exWVtf5djk9J8tC5bRq5bVJpl5lC+8yU4Gub1Pr1hku0+1e0hqRwIHC7qh4X2r4ZQFXvbnDMx6FjpopIErARyNMmgtrTpOAPKJW1fmp8AWp8AWr9Aap9frbvqKW4oobi8hpWF1ewvKiCeetK2Fga7IaR3z6DU4Z34dQRXelt/Q1MMzaXVXH7u/OZvKiIoV3bctfpQ+nbwf7dxBtVpaSylnXbK1m/vYr12ytD65UUl9ewtaKG4ooatu2owd9I7+uMFO+uiSIj+LpPg4RSt+yTkUJykocUr4eUpD2rnYjY0Nl7oSuwpsH2WmBMY8eoqk9ESoD2wJZIBzNp7gaumTiryWNSvB565Wayf692jO7VjgN6taNvhzatukhoWpcOWWk8dv4ot8MwDhMRcjJSyMlIaXIa3UBAKa0K3nhubWIpLq9h6aZytlbU/KwE0tBvD+/DTScMdOIr1YuJnjMicjlwOUCPHj326Bz7dsnmlhMHkewVUpK8oVcPORk7s3LH7DQbvdQYEzEez87k0SfMEcAra/xs3VHD1vKa4GtFNdsqavEHlGFRGDShh9INAAAXi0lEQVTRyaSwDujeYLtbaN/ujlkbqj5qS/CB8y5UdTwwHoLVR3sSTJ+8NjbchDGm1UtP8dI1JZ2uLg2j7mTTmelAPxHpJSIpwDnAuz855l3gotD6mcDnTT1PMMYY4yzHSgqhZwS/Az4m2CT1WVWdLyJ/A2ao6rvAM8CLIrIM2EowcRhjjHFJzA2dLSJFwJ52mMvFgYfYrZx958Rg3zkx7M137qmqzT7ZiLmksDdEZEY4TbLiiX3nxGDfOTFE4ztbd1xjjDH1LCkYY4ypl2hJYbzbAbjAvnNisO+cGBz/zgn1TMEYY0zTEq2kYIwxpgmWFIwxxtRLmKQgIseLyGIRWSYiN7kdj9NE5FkR2Swi89yOJVpEpLuITBaRBSIyX0SudTsmp4lImohME5EfQt/5DrdjigYR8YrILBF53+1YokFEVonIXBGZLSKOzh2QEM8UwpnbId6IyGFAOTBBVYe4HU80iEhnoLOqzhSRLKAQODXO/54FyFTVchFJBr4Grq2bUz1eicj1QAGQraonuR2P00RkFVCgqo531kuUksJoYJmqrlDVGuAV4Jcux+QoVf2S4NAhCUNVN6jqzNB6GbCQ4PDscUuDykObyaElru/0RKQb8AvgabdjiUeJkhR2N7dDXP9YJDoRyQdGAN+7G4nzQlUps4HNwKeqGu/f+QHgj0CguQPjiAKfiEhhaCoBxyRKUjAJRETaAG8C16lqqdvxOE1V/aq6H8Hh6UeLSNxWF4rIScBmVS10O5YoO0RVRwInAFeHqocdkShJIZy5HUwcCNWrvwm8rKpvuR1PNKnqdmAycLzbsTjoYOCUUB37K8BRIvKSuyE5T1XXhV43A28TrBJ3RKIkhXDmdjAxLvTQ9Rlgoar+y+14okFE8kQkJ7SeTrAxxSJ3o3KOqt6sqt1UNZ/g/+PPVfUCl8NylIhkhhpOICKZwLGAY60KEyIpqKoPqJvbYSHwmqrOdzcqZ4nIRGAqMEBE1orIpW7HFAUHAxcSvHucHVpOdDsoh3UGJovIHII3P5+qakI000wgHYGvReQHYBrwgap+5NTFEqJJqjHGmPAkREnBGGNMeCwpGGOMqWdJwRhjTL0ktwNoqdzcXM3Pz3c7DGOMiSmFhYVbwpmj2fWkICLPAnUdUprtdJOfn8+MGY6OB2WMMXFHRFaHc1xrqD56nvjubGOMMTHD9aQQrYHbNpVW8dmCTZRX+5y+lDHGxCzXk0I4RORyEZkhIjOKior26BzTV23lNxNmsH57ZYSjM8aY+BETSUFVx6tqgaoW5OU1+5xkt7wiAPgD1lnPGGMaExNJIRK8HksKxhjTHEsKxhhj6rmeFKI1cFtdUvBZUjDGmEa53k9BVc+NxnXqkkLABgA0xphGuV5SiBarPjLGmOYlTlKw1kfGGNOssKuPRGQfoAtQCaxS1ZiaNDvJa0nBGGOa02RSEJG2wNXAuUAKUASkAR1F5DvgMVWd7HiUEeCxkoIxxjSruZLCG8AE4NDQpOD1RKQAuEBEeqvqM04FGClJnmBNmSUFY4xpXJNJQVXHNvHeDCBmhisN5QRrkmqMMU0I60GziNz/k+0kEXFs4mgn1JUUrEmqMcY0LtzWR9eKSGndAmwGqh2MK+K8VlIwxphmhdv6aK6qjnA0Eod560oKlhSMMaZR4SYFT6hJqjTcqaqOz4MQKXX9FKykYIwxjQs3KQwECtk1KSjQO+IROcQb6qdgJQVjjGlcuEnhr6p6j6OROMxKCsYY07wmHzSLSD5AYwlBgrpFPqzIq2uS6rfWR8YY06jmSgr3iogHeIdg9VFdj+a+wJHA0cBtwFong4yE+s5r/pgancMYY6Kquc5rZ4nIvsD5wCVAZ2AHsBCYBNypqlWORxkB9QPiWUHBGGMa1ewzBVVdANwShVgc5a0fEM9KCsYY05gEHDrb5UCMMaYVS5yk4LGSgjHGNCcBk4LLgRhjTCsW7oB4IiIXiMhfQ9s9RGS0s6FFVignWEnBGGOaEG5J4THgQIKT7QCUAY86EpFDRASvR6yfgjHGNCHcHs1jVHWkiMwCUNVtIpLiYFyO8HrEejQbY0wTwi0p1IqIl+B4R4hIHhBz9TBeERv7yBhjmhBuUngIeBvoICJ3Al8DdzkWlUOSrKRgjDFNCqv6SFVfFpFCgsNaCHCqqi50NDIHeDxWUjDGmKaE2/qoD7BSVR8F5gFjRSTH0cgcYCUFY4xpWrjVR28CfhHpCzwJdAf+41hUDvF4xOZoNsaYJoSbFAKq6gNOBx5R1T8QHBwvpiR5BJ+NiGeMMY1qSeujc4FxwPuhfcnOhOQcj1g/BWOMaUq4SeFigp3X7lTVlSLSC3jRubCckeQV/PZMwRhjGhVu66MFwO8bbK8EYm56Tq9YUjDGmKaElRREpB9wN7AvwZnXAFDV3g7F5QiPx5KCMcY0Jdzqo+eAxwEfwWk4JwAvORWUU5IsKRhjTJPCTQrpqvo/QFR1tareDvzCubCc4bHqI2OMaVK4A+JVi4gHWCoivwPWAW2cC8sZSV5rfWSMMU0Jt6RwLZBB8GHzKOBC4CKngnKKlRSMMaZp4bY+mh5aLSfYPDWiROR44EHACzytqv+M9DXAnikYY0xzwm191B/4A9Cz4WdU9ai9DSA0JPejwFhgLTBdRN4NNYONKI+NfWSMMU0K95nC68ATwFOAP8IxjAaWqeoKABF5BfglEPGkkOQRNpdV8/H8jdT4AtT4AlT5/JRV+Sirqg29BtdLq3yUVgb37ajx4fUEZ25L8nhI8gbXU7weUpI8pHg9JNeth7ZTkjwkeyX0GtxODR2X5PXgkWB1loRePRKcBEhE6rc9Ing8ssuxAKqgKKoQUFDV4EQXof3BfTuP0dCHGh5bv64QCL1XNy5UILBze+fxDY75yWc0dK5Ag/2gBAI7tzUYHB6P4A19L6+HneuhmfF2eV+Cx+zyvgjJSR6SPRL6swy+1q2neD0keYQkb/DvIfi+1P+5J3uFZM+un6ubv9uY1uybZVvokpNOr9xMR68TblLwqerjDsXQFVjTYHstMKbhASJyOXA5QI8ePfb4Qm3Tk/l2eTFXvFj4s/e8HiErLSm4pCaTlZZEt30yyE5LIiPVS0DB71d8AcUfCFDrV2r8AWr9weRS6w+wY4ePGr9S4/MH3/cFgsf4AlSHjotF9QlKBGTXbYFgYvPsTGbQIKlJcCrUuoQWCCh+VfyhhOEPaIN9unNfFAt0HoGMlCQyU71kpibRJjWJzJQkMlOTyG2TQoesVDpkp9G5bRr9OmTRbZ90PJZITJRd/Px0Ljm4FzedMNDR6zSZFESkXWj1PRG5iuBEO9V176vqVgdjq6eq44HxAAUFBXv8c3HPmcO47LDepHg9pIbu6tOSvWSlJZGe7EXE2f/oqnVJRXe50254Zx5ocPfuD+x6J+8PKKHf5VCJAoSdP7jSoERRtz/0G93gB3zXH+qGpRLYdVtCx7lBG3znuj8Lvyp+v1IbCODzK75QYvYFAtT6du6vDSXr+vWAUusLHRfaV//Z0HZFjY+Kah8VNf7ga7WPtdt28MPa7Wwpr6Zho7WMFC+jeu7DEQM6cMrwLuRlpbryZ2QSiz+geMNtGrQXmispFBKsfaj7ZfhDg/cUiESP5nUEh+Ku0y20L+Ky05IZ2WMfJ04dFpG6qgzXQogZIoI3VKXmNp8/QHFFDWu3VbJ0UxkLN5TyzfJi/v7+Au75cBEXHtiTG48dQHqK/cUaZ2joxsjrcT4rNJkUVLWX4xHAdKBfaJC9dcA5wHlRuK4xYUnyeuiYnUbH7DRG9dx5U7G8qJzxX6zgma9X8vXSLbz0mzFWajCOqKtO9Uah5B7uzGtpInK9iLwlIm+KyHUiktb8J5sXmqfhd8DHwELgNVWdH4lzG+OkPnltuOfMYbxwyWh+3LqDq14utCbPxhG+QPB5ZJK3lSQFgmMdDQYeBh4JrUds6GxVnaSq/VW1j6reGanzGhMNh/fP4++nDmH6qm28Ubim+Q8Y00KhnFD/7M9J4SaFIap6qapODi2XEUwMxhjgjJFdGdatLeO/XIHaUComwupLClF4xhZuUpgpIgfUbYjIGGCGMyEZE3tEhAvG9GR5UQWz12x3OxwTZ+pLCq0oKYwCvhWRVSKyCpgK7C8ic0VkjmPRGRNDxu7bERH4eukWt0MxcSaaJYVwO68d72gUxsSBfTJTGNQpm2+XF3PN0f3cDsfEkbrRnaPRRDvcAfFWOx2IMfGgIH8f3pq5DlV1reOfiT91rdqikRSi0D/OmMTRr0Mbyqt9bCqtbv5gY8JkScGYGNW3QxYASzeXuRyJiSf1SaEVNUlFRHqKyDGh9XQRyXIuLGNiU98OwQkJl20udzkSE0/qkkKr6bwmIpcBbwBPhnZ1A/7rVFDGxKrcNimkJ3tZt63S7VBMHKlLCq2p89rVwMFAKYCqLgU6OBWUMbFKROjUNo0NpVVuh2LiSF3ro9bUea1aVWvqNkQkidDcLcaYXXXMTmVTiSUFEzk+f6ik0IqSwhci8mcgXUTGEpyJ7T3nwjImdnXKTmOjlRRMBAVaYUnhJqAImAtcAUwCbnUqKGNiWce2aWwurbYxkEzE1M0tH42SQrid1wIE52d+KjQbWze1f/HG7Fan7DRq/AG2VtTQvo3Nr2D2XiDQykoKIjJFRLJDCaGQYHL4t7OhGRObOmUHpxqxKiQTKb5W2E+hraqWAqcDE1R1DHC0c2EZE7s6tg0mhU2WFEyEBFphj+YkEekMnA2872A8xsS8+pJCiQ11YSLD1wqTwt8ITpe5TFWni0hvYKlzYRkTu/KyUhGx6iMTOa1xlNTXCTZDrdteAZzhVFDGxLJkr4fcNtZXwUSO399KkoKIPEwTndRU9fcRj8iYOGB9FUwktaaSgk25acwe6JidxtptO9wOw8SJaA6d3WRSUNUXHI/AmDjUqW0qM1ZvdTsMEyf8UeynENYzBRGZzG6qkVT1qIhHZEwc6JSdxvYdtVTV+klL9rodjolx0RwlNdw5mm9ssJ5G8CGzL/LhGBMfOtY3S60iPzfT5WhMrNtZUnB+XrRwWx8V/mTXNyIyzYF4jIkLndru7NVsScHsrfqSQhTmygx3mIt2DZZcETkOaOtwbMbErG77ZADw41Z72Gz23s75FFpJSYHgeEcKCMFqo5XApU4FZUys69Eug7RkD4s32lzNZu/5olhSCLf6qJfTgRgTT7weYUDHLBZtLHU7FBMHAq3tmYKIpAFXAYcQLDF8BTyhqtY7x5hGDOyUzScLNhIIaFTGwTfxqzWOkjoBGAw8DDwSWn/RqaCMiQdjerdj245aFmyw0oLZO/WjpHpbT5PUIaq6b4PtySKywImAjIkXh/bLA+DzRZsZ0tXaZZg91xpLCjNF5IC6DREZgw2BYUyT8rJSOahPe16dvgafP+B2OCaGBaI49lG4SWEU8K2IrBKRVcBUYH8RmSsicxyLzpgYd8nBvVi3vZKXvlvtdigmhvlayyipDRzvaBTGxKmjB3XgsP553PXhInrlteHw/nluh2RiUF0/hWi0VwirpKCqq5tanA7SmFglIjzwq/3onZvJxc9N4+4PF7KtosbtsEyM8QcCeD2CtKKxj4wxe6hdZgpvXnkQf31nPuO/XMEzX63koL65HNYvl8Fd2jKocxZt05Oj8h/exCZ/IDpVR+ByUhCRs4DbgUHAaFW1h9cmLmWmJnH/2cO54vDevFm4lo/mb+QfS4p2vp/ipXNOOp2y02ibkUx2WjLZ6UnB17QkUpO9pCZ5SEv2krbLuofUpOB2SmhJTfKQ4vVYkokj/kAgKi2PwP2SwjzgdOBJl+MwJir6d8zi5hMHcfOJg9hSXs389aUs2VjG+pJKNmyvYmNpFetLKimt9FFaWUvNXrRaSvE2SBJ1y8/2eUnxekhN9pDq3fW41GQPKV7vrsmmQdIJrnt385mGxwbfj9ZdbqT5A0qNL0CNL0C131+/XuMP7Fz3BahusF1dv9+/y3HV/gBosErR6wkOgy0ieEVITfaQlZZEVloyWWlJ5KQn0zE7jbysVJK9HvyB6MylAC4nBVVdCNgdjUlIuW1SObx/XqMPn1WVal+AsiofVbV+qn1+qmoDP3utqvXX/1BV14ZeG/5g+fy7/JA1fK+ksnbXY35ynrrROfdWkkd+lihSf5ZsvLs9JrXBMc39VgQCGvye/gC1PqXG76fWp9T6gz/KtaHvV/vTH/GGP/IN1n0R+v4AKUkePAKBQLCJqV8Vbeb0IsF/J7X+QNR6xbtdUjDGNEJE6quL3LLLnbLPv8sPaMPkUhO6i65usH93iam64XYo+dTt276jZpfPVofutuvWWyIlKVjySQ6VYJKThGTvzhJO3XpGRtLOkpD35yWqlJ8mqSaOqSsh7VJ6qru+d/cPiVWVgEJVrZ+yKh9lVbWUVvnYvqOGTaXVbCytYlNJsATZv2ObSP21NsnxpCAinwGddvPWLar6TpjnuBy4HKBHjx4RjM4Y0xSvR0hP8ZKe4gWSXYtDVan1K/rzCSARdv7YigRLJbFS+xCsPgo+c8pMTaqfh8NNjicFVT0mAucYD4wHKCgoiFx5zhgTE0SElKTY+KGPdVEYndsYY0yscDUpiMhpIrIWOBD4QEQ+djMeY4xJdKLNPf5uZUSkCNjTXtS5wJYIhhML7DsnBvvOiWFvvnNPVW12nJWYSwp7Q0RmqGqB23FEk33nxGDfOTFE4zvbMwVjjDH1LCkYY4ypl2hJYbzbAbjAvnNisO+cGBz/zgn1TMEYY0zTEq2kYIwxpgkJkxRE5HgRWSwiy0TkJrfjcZqIPCsim0VkntuxRIuIdBeRySKyQETmi8i1bsfkNBFJE5FpIvJD6Dvf4XZM0SAiXhGZJSLvux1LNISmQp4rIrNFxNEpBhKi+khEvMASYCywFpgOnKuqC1wNzEEichhQDkxQ1SFuxxMNItIZ6KyqM0UkCygETo3zv2cBMlW1XESSga+Ba1X1O5dDc5SIXA8UANmqepLb8ThNRFYBBarqeL+MRCkpjAaWqeoKVa0BXgF+6XJMjlLVL4GtbscRTaq6QVVnhtbLgIVAV3ejcpYGlYc2k0NLXN/piUg34BfA027HEo8SJSl0BdY02F5LnP9YJDoRyQdGAN+7G4nzQlUps4HNwKeqGu/f+QHgj8Cez0AUexT4REQKQ6NGOyZRkoJJICLSBngTuE5VS92Ox2mq6lfV/YBuwGgRidvqQhE5CdisqoVuxxJlh6jqSOAE4OpQ9bAjEiUprAO6N9juFtpn4kyoXv1N4GVVfcvteKJJVbcDk4Hj3Y7FQQcDp4Tq2F8BjhKRl9wNyXmqui70uhl4m2CVuCMSJSlMB/qJSC8RSQHOAd51OSYTYaGHrs8AC1X1X27HEw0ikiciOaH1dIKNKRa5G5VzVPVmVe2mqvkE/x9/rqoXuByWo0QkM9RwAhHJBI4lOL+9IxIiKaiqD/gd8DHBh4+vqep8d6NylohMBKYCA0RkrYhc6nZMUXAwcCHBu8fZoeVEt4NyWGdgsojMIXjz86mqJkQzzQTSEfhaRH4ApgEfqOpHTl0sIZqkGmOMCU9ClBSMMcaEx5KCMcaYepYUjDHG1LOkYIwxpp4lBWOMMfUsKRjTDBHJEZGrQutdROQNt2MyxinWJNWYZoTGUXo/UUabNYktye0AjIkB/wT6hAadWwoMUtUhIvJr4FQgE+gH3AekEOxAVw2cqKpbRaQP8CiQB+wALlPVuO11bGKbVR8Z07ybgOWhQef+8JP3hgCnA/sDdwI7VHUEwd7k40LHjAeuUdVRwI3AY1GJ2pg9YCUFY/bO5NDcDWUiUgK8F9o/FxgWGrH1IOD14NBMAKRGP0xjwmNJwZi9U91gPdBgO0Dw/5cH2B4qZRjT6ln1kTHNKwOy9uSDofkcVorIWRAcyVVEhkcyOGMiyZKCMc1Q1WLgGxGZB9y7B6c4H7g0NMrlfOJ8KlgT26xJqjHGmHpWUjDGGFPPkoIxxph6lhSMMcbUs6RgjDGmniUFY4wx9SwpGGOMqWdJwRhjTD1LCsYYY+r9f/t0xkftUcFnAAAAAElFTkSuQmCC\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "print(\"pump pulse amplitude and phase:\")\n", "plot_pulse_amplitude_and_phase(\n", " oct_result.optimized_controls[0], oct_result.optimized_controls[1], tlist)\n", "print(\"Stokes pulse amplitude and phase:\")\n", "plot_pulse_amplitude_and_phase(\n", " oct_result.optimized_controls[2], oct_result.optimized_controls[3], tlist)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can finally convert the $\\Omega _P$ and $\\Omega _S$ functions to the physical electric fields $\\varepsilon_{P}$ and $\\varepsilon_{S}$:" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "ExecuteTime": { "end_time": "2019-02-25T19:11:34.284639Z", "start_time": "2019-02-25T19:11:33.622363Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Physical electric pump pulse in the lab frame:\n" ] }, { "data": { "image/png": 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\n", 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "def plot_physical_field(pulse_re, pulse_im, tlist, case=None):\n", "\n", " if case == 'pump':\n", " w = 9.5\n", " elif case == 'stokes':\n", " w = 4.5\n", " else: \n", " print('Error: selected case is not a valid option')\n", " return\n", " \n", " ax = plt.subplot(111) \n", " ax.plot(tlist,pulse_re*np.cos(w*tlist)-pulse_im*np.sin(w*tlist), 'r')\n", " ax.set_xlabel('time', fontsize = 16)\n", " if case == 'pump':\n", " ax.set_ylabel(r'$\\varepsilon_{P} * \\mu_{12}$', fontsize = 16)\n", " elif case == 'stokes':\n", " ax.set_ylabel(r'$\\varepsilon_{S} * \\mu_{23}$', fontsize = 16)\n", " plt.show()\n", " \n", "print('Physical electric pump pulse in the lab frame:')\n", "plot_physical_field(\n", " oct_result.optimized_controls[0], oct_result.optimized_controls[1], tlist, case = 'pump')\n", "\n", "\n", "print('Physical electric Stokes pulse in the lab frame:')\n", "plot_physical_field(\n", " oct_result.optimized_controls[2], oct_result.optimized_controls[3], tlist, case = 'stokes')" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "ExecuteTime": { "end_time": "2019-02-25T19:11:34.987825Z", "start_time": "2019-02-25T19:11:34.290384Z" }, "attributes": { "classes": [], "id": "", "n": "26" } }, "outputs": [ { "data": { "image/png": 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\n", 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