{ "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": "2019-11-10T17:36:51.297636Z", "start_time": "2019-11-10T17:36:49.706568Z" }, "attributes": { "classes": [], "id": "", "n": "1" }, "execution": { "iopub.execute_input": "2021-11-07T04:49:47.386239Z", "iopub.status.busy": "2021-11-07T04:49:47.380857Z", "iopub.status.idle": "2021-11-07T04:49:48.431377Z", "shell.execute_reply": "2021-11-07T04:49:48.431000Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Python implementation: CPython\n", "Python version : 3.8.1\n", "IPython version : 7.24.1\n", "\n", "krotov : 1.2.1+dev\n", "matplotlib: 3.4.2\n", "qutip : 4.6.1\n", "numpy : 1.20.3\n", "scipy : 1.6.3\n", "\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", "from functools import partial\n", "%watermark -v --iversions" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$\\newcommand{tr}[0]{\\operatorname{tr}}\n", "\\newcommand{diag}[0]{\\operatorname{diag}}\n", "\\newcommand{abs}[0]{\\operatorname{abs}}\n", "\\newcommand{pop}[0]{\\operatorname{pop}}\n", "\\newcommand{aux}[0]{\\text{aux}}\n", "\\newcommand{opt}[0]{\\text{opt}}\n", "\\newcommand{tgt}[0]{\\text{tgt}}\n", "\\newcommand{init}[0]{\\text{init}}\n", "\\newcommand{lab}[0]{\\text{lab}}\n", "\\newcommand{rwa}[0]{\\text{rwa}}\n", "\\newcommand{bra}[1]{\\langle#1\\vert}\n", "\\newcommand{ket}[1]{\\vert#1\\rangle}\n", "\\newcommand{Bra}[1]{\\left\\langle#1\\right\\vert}\n", "\\newcommand{Ket}[1]{\\left\\vert#1\\right\\rangle}\n", "\\newcommand{Braket}[2]{\\left\\langle #1\\vphantom{#2}\\mid{#2}\\vphantom{#1}\\right\\rangle}\n", "\\newcommand{ketbra}[2]{\\vert#1\\rangle\\!\\langle#2\\vert}\n", "\\newcommand{op}[1]{\\hat{#1}}\n", "\\newcommand{Op}[1]{\\hat{#1}}\n", "\\newcommand{dd}[0]{\\,\\text{d}}\n", "\\newcommand{Liouville}[0]{\\mathcal{L}}\n", "\\newcommand{DynMap}[0]{\\mathcal{E}}\n", "\\newcommand{identity}[0]{\\mathbf{1}}\n", "\\newcommand{Norm}[1]{\\lVert#1\\rVert}\n", "\\newcommand{Abs}[1]{\\left\\vert#1\\right\\vert}\n", "\\newcommand{avg}[1]{\\langle#1\\rangle}\n", "\\newcommand{Avg}[1]{\\left\\langle#1\\right\\rangle}\n", "\\newcommand{AbsSq}[1]{\\left\\vert#1\\right\\vert^2}\n", "\\newcommand{Re}[0]{\\operatorname{Re}}\n", "\\newcommand{Im}[0]{\\operatorname{Im}}\n", "\\newcommand{toP}[0]{\\omega_{12}}\n", "\\newcommand{toS}[0]{\\omega_{23}}$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This example illustrates the use of Krotov's method with a non-Hermitian\n", "Hamiltonian. It considers the same system as the [previous\n", "example](02_example_lambda_system_rwa_complex_pulse.ipynb), a transition\n", "$\\ket{1} \\rightarrow \\ket{3}$ in a three-level system in a\n", "$\\Lambda$-configuration. However, here we add a non-Hermitian decay term to\n", "model loss from the intermediary level $\\ket{2}$. At a technical level, this\n", "examples also illustrates how to use `args` in time-dependent control fields\n", "(\"QuTiP style\"), as opposed to hard-coding parameters or setting them through\n", "a closure, as was done in the previous examples." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## The effective Hamiltonian\n", "\n", "We consider the system as in the following diagram:\n", "\n", "![Lambda system considered in this notebook](energylevels.png)\n", "\n", "with the Hamiltonian\n", "\n", "$$\n", "\\Op{H}_{\\text{lab}} = \\begin{pmatrix}\n", " E_1 & -\\mu_{12} \\epsilon_P(t) & 0 \\\\\n", " -\\mu_{12} \\epsilon_P(t) & E_2 & - \\mu_{23} \\epsilon_S(t) \\\\\n", " 0 & -\\mu_{23} \\epsilon_S(t) & E_2\n", "\\end{pmatrix}\n", "$$\n", "\n", "in the lab frame.\n", "\n", "However, we now also include that the level $\\ket{2}$ decays incoherently. This\n", "is the primary motivation of the STIRAP scheme: through destructive\n", "interference it can keep the dynamics in a \"dark state\" where the population is\n", "transferred from $\\ket{1}$ to $\\ket{3}$ without ever populating the $\\ket{2}$\n", "state. A rigorous treatment would be to include the dissipation as a Lindblad\n", "operator, and to simulate the dynamics and perform the optimization in\n", "Liouville space. The Lindblad operator for spontaneous decay from level\n", "$\\ket{2}$ with decay rate $2\\gamma$ is $\\Op{L} = \\sqrt{2\\gamma} \\ketbra{1}{2}$.\n", "However, this is numerically expensive. For the optimization,\n", "it is sufficient to find a way to penalize population in the $\\ket{2}$ state.\n", "\n", "Motivated by the [Monte-Carlo Wave Function\n", "(MCWF)](https://en.wikipedia.org/wiki/Quantum_jump_method) method, we define\n", "the non-Hermitian *effective Hamiltonian*\n", "\n", "$$\n", "\\Op{H}_{\\text{eff}} = \\Op{H}_{\\text{lab}} - \\frac{i}{2} \\Op{L}^{\\dagger} \\Op{L}\n", "$$\n", "\n", "In explicit form, this is\n", "\n", "$$\n", "\\Op{H}_{\\text{eff}} = \\begin{pmatrix}\n", " E_1 & -\\mu_{12} \\epsilon_P(t) & 0 \\\\\n", " -\\mu_{12} \\epsilon_P(t) & E_2 - i \\gamma & - \\mu_{23} \\epsilon_S(t) \\\\\n", " 0 & -\\mu_{23} \\epsilon_S(t) & E_2\n", "\\end{pmatrix}\n", "$$\n", "\n", "The only change is that the energy of level $\\ket{2}$ now has an imaginary part\n", "$-\\gamma$, which causes an exponential decay of any population amplitude in\n", "$\\ket{2}$, and thus a decay in the norm of the state. In the MCWF, this decay\n", "of the norm is used to track the probability that quantum jump occurs\n", "(otherwise, the state is re-normalized). Here, we do not perform quantum jumps\n", "or renormalize the state. Instead, we use the decay in the norm to steer the\n", "optimization. Using the functional\n", "\n", "$$\n", "J_{T, \\text{re}} = 1 - \\Re{\\Braket{\\Psi(T)}{\\Psi^{\\tgt}}}\n", "$$\n", "\n", "to be minimized, we find that the value of\n", "the functional increases if $\\Norm{\\ket{\\Psi(T)}} < 1$. Thus, population in\n", "$\\ket{2}$ is penalized, without any significant numerical overhead.\n", "\n", "The decay rate $2\\gamma$ does not necessarily need to correspond to the actual\n", "physical lifetime of the $\\ket{2}$ state: we can choose an artificially high\n", "decay rate to put a stronger penalty on the $\\ket{2}$ level. Or, if the\n", "physical decay is so strong that the norm of the state reaches effectively\n", "zero, we could decrease $\\gamma$ to avoid numerical instability. The use of a\n", "non-Hermitian Hamiltonian with artificial decay is generally a useful trick to\n", "penalize population in a subspace.\n", "\n", "The new non-Hermitian decay term remains unchanged when we make the rotating\n", "wave approximation. The RWA Hamiltonian now reads\n", "\n", "$$\n", "\\Op{H}_{\\text{RWA}} = \\begin{pmatrix}\n", " \\Delta_P & -\\frac{1}{2} \\Omega_P(t) & 0 \\\\\n", " -\\frac{1}{2} \\Omega_P^*(t) & -i \\gamma & -\\frac{1}{2} \\Omega_S(t) \\\\\n", " 0 & -\\frac{1}{2} \\Omega_S^*(t) & \\Delta_S\n", "\\end{pmatrix}\\,,\n", "$$\n", "\n", "with complex control fields $\\Omega_P(t)$ and $\\Omega_S(t)$, see the [previous\n", "example](02_example_lambda_system_rwa_complex_pulse.ipynb). Again, we split\n", "these complex pulses into an independent real and imaginary part for the\n", "purpose of optimization.\n", "\n", "The guess controls are" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "ExecuteTime": { "end_time": "2019-11-10T17:36:51.316137Z", "start_time": "2019-11-10T17:36:51.300148Z" }, "execution": { "iopub.execute_input": "2021-11-07T04:49:48.435713Z", "iopub.status.busy": "2021-11-07T04:49:48.435331Z", "iopub.status.idle": "2021-11-07T04:49:48.437149Z", "shell.execute_reply": "2021-11-07T04:49:48.436836Z" } }, "outputs": [], "source": [ "def Omega_P1(t, args):\n", " \"\"\"Guess for the real part of the pump pulse.\n", "\n", " Blackman shape with amplitude `Ω0` from `t0P` to `t0P` + `ΔTP`, with\n", " parameters from `args`.\n", " \"\"\"\n", " t0 = args['t0P']\n", " T = t0 + args['ΔTP']\n", " return args['Ω0'] * krotov.shapes.blackman(t, t_start=t0, t_stop=T)\n", "\n", "\n", "def Omega_P2(t, args):\n", " \"\"\"Guess for the imaginary part of the pump pulse (zero).\"\"\"\n", " return 0.0\n", "\n", "\n", "def Omega_S1(t, args):\n", " \"\"\"Guess for the real part of the Stokes pulse.\n", "\n", " Blackman shape with amplitude `Ω0` from `t0S` to `t0S` + `ΔTS`, with\n", " parameters from `args`.\n", " \"\"\"\n", " t0 = args['t0S']\n", " T = t0 + args['ΔTS']\n", " return args['Ω0'] * krotov.shapes.blackman(t, t_start=t0, t_stop=T)\n", "\n", "\n", "def Omega_S2(t, args):\n", " \"\"\"Guess for the imaginary part of the Stokes pulse (zero).\"\"\"\n", " return 0.0" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "with the Hamiltonian defined as" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "ExecuteTime": { "end_time": "2019-11-10T17:36:51.334402Z", "start_time": "2019-11-10T17:36:51.319722Z" }, "execution": { "iopub.execute_input": "2021-11-07T04:49:48.441978Z", "iopub.status.busy": "2021-11-07T04:49:48.441592Z", "iopub.status.idle": "2021-11-07T04:49:48.443405Z", "shell.execute_reply": "2021-11-07T04:49:48.443027Z" } }, "outputs": [], "source": [ "def hamiltonian(args):\n", " \"\"\"Lambda-system Hamiltonian in the RWA\"\"\"\n", " E1, E2, E3, ΩP, ΩS, γ = (args[key] for key in 'E1 E2 E3 ΩP ΩS γ'.split())\n", "\n", " # detunings\n", " ΔP = E1 + ΩP - E2\n", " ΔS = E3 + ΩS - E2\n", "\n", " H0 = Qobj([[ΔP, 0.0, 0.0], [0.0, -1j * γ, 0.0], [0.0, 0.0, ΔS]])\n", "\n", " HP_re = -0.5 * Qobj([[0.0, 1.0, 0.0], [1.0, 0.0, 0.0], [0.0, 0.0, 0.0]])\n", " HP_im = -0.5 * Qobj([[0.0, 1.0j, 0.0], [-1.0j, 0.0, 0.0], [0.0, 0.0, 0.0]])\n", "\n", " HS_re = -0.5 * Qobj([[0.0, 0.0, 0.0], [0.0, 0.0, 1.0], [0.0, 1.0, 0.0]])\n", " HS_im = -0.5 * Qobj([[0.0, 0.0, 0.0], [0.0, 0.0, 1.0j], [0.0, -1.0j, 0.0]])\n", "\n", " return [\n", " H0,\n", " [HP_re, Omega_P1],\n", " [HP_im, Omega_P2],\n", " [HS_re, Omega_S1],\n", " [HS_im, Omega_S2],\n", " ]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "and using the following physical parameters:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "ExecuteTime": { "end_time": "2019-11-10T17:36:51.343719Z", "start_time": "2019-11-10T17:36:51.337196Z" }, "execution": { "iopub.execute_input": "2021-11-07T04:49:48.446293Z", "iopub.status.busy": "2021-11-07T04:49:48.445914Z", "iopub.status.idle": "2021-11-07T04:49:48.447680Z", "shell.execute_reply": "2021-11-07T04:49:48.447303Z" } }, "outputs": [], "source": [ "ARGS = dict(\n", " Ω0=5.0, # amplitude of both the Pump and the Stokes laser\n", " ΔTP=3.0, # duration of the Pump laser\n", " ΔTS=3.0, # duration of the Stokes laser\n", " t0P=2.0, # starting time for the Pump laser\n", " t0S=0.0, # starting time for the Stokes laser\n", " t_rise=0.3, # switch-on/-off time in update shape\n", " E1=0.0, # energy of level |1⟩\n", " E2=10.0, # energy of intermediary level |2⟩\n", " E3=5.0, # energy of target level |3⟩\n", " ΩP=9.5, # frequency of the Pump laser\n", " ΩS=4.5, # frequency of the Stokes laser\n", " γ=0.5, # decay rate on intermediary level |2⟩\n", " T=5.0, # total process time\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "(cf. the [previous example](02_example_lambda_system_rwa_complex_pulse.ipynb),\n", "where these values were hard-coded).\n", "\n", "The Hamiltonian is now instantiated as" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "ExecuteTime": { "end_time": "2019-11-10T17:36:51.354864Z", "start_time": "2019-11-10T17:36:51.346901Z" }, "execution": { "iopub.execute_input": "2021-11-07T04:49:48.451697Z", "iopub.status.busy": "2021-11-07T04:49:48.451323Z", "iopub.status.idle": "2021-11-07T04:49:48.453144Z", "shell.execute_reply": "2021-11-07T04:49:48.452769Z" }, "lines_to_next_cell": 2 }, "outputs": [], "source": [ "H = hamiltonian(ARGS)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We check the hermiticity of the Hamiltonian:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "ExecuteTime": { "end_time": "2019-11-10T17:36:51.364285Z", "start_time": "2019-11-10T17:36:51.357446Z" }, "attributes": { "classes": [], "id": "", "n": "3" }, "execution": { "iopub.execute_input": "2021-11-07T04:49:48.455970Z", "iopub.status.busy": "2021-11-07T04:49:48.455596Z", "iopub.status.idle": "2021-11-07T04:49:48.457677Z", "shell.execute_reply": "2021-11-07T04:49:48.457303Z" }, "lines_to_next_cell": 2 }, "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": [ "## Define the optimization target\n", "\n", "We optimize for the phase-sensitive transition $\\ket{1} \\rightarrow \\ket{3}$.\n", "As we are working in the rotating frame, the target state must be adjusted with\n", "an appropriate phase factor:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "ExecuteTime": { "end_time": "2019-11-10T17:36:51.375198Z", "start_time": "2019-11-10T17:36:51.366724Z" }, "execution": { "iopub.execute_input": "2021-11-07T04:49:48.462095Z", "iopub.status.busy": "2021-11-07T04:49:48.461723Z", "iopub.status.idle": "2021-11-07T04:49:48.463406Z", "shell.execute_reply": "2021-11-07T04:49:48.463079Z" } }, "outputs": [], "source": [ "ket1 = qutip.Qobj(np.array([1.0, 0.0, 0.0]))\n", "ket2 = qutip.Qobj(np.array([0.0, 1.0, 0.0]))\n", "ket3 = qutip.Qobj(np.array([0.0, 0.0, 1.0]))\n", "\n", "def rwa_target_state(ket3, *, E2, ΩS, T):\n", " return np.exp(1j * (E2 - ΩS) * T) * ket3\n", "\n", "psi_target = rwa_target_state(ket3, **{k: ARGS[k] for k in 'E2 ΩS T'.split()})" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The objective is now instantiated as" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "ExecuteTime": { "end_time": "2019-11-10T17:36:51.388371Z", "start_time": "2019-11-10T17:36:51.381216Z" }, "execution": { "iopub.execute_input": "2021-11-07T04:49:48.467551Z", "iopub.status.busy": "2021-11-07T04:49:48.467182Z", "iopub.status.idle": "2021-11-07T04:49:48.469575Z", "shell.execute_reply": "2021-11-07T04:49:48.469260Z" }, "lines_to_next_cell": 2 }, "outputs": [ { "data": { "text/plain": [ "[Objective[|Ψ₀(3)⟩ to |Ψ₁(3)⟩ via [A₀[3,3], [H₁[3,3], u₁(t)], [H₂[3,3], u₂(t)], [H₃[3,3], u₃(t)], [H₄[3,3], u₄(t)]]]]" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "objectives = [krotov.Objective(initial_state=ket1, target=psi_target, H=H)]\n", "objectives" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Simulate dynamics under the guess field\n", "\n", "We use a time grid with 500 steps between $t=0$ and $T=5$:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "ExecuteTime": { "end_time": "2019-11-10T17:36:51.398777Z", "start_time": "2019-11-10T17:36:51.393811Z" }, "execution": { "iopub.execute_input": "2021-11-07T04:49:48.471989Z", "iopub.status.busy": "2021-11-07T04:49:48.471620Z", "iopub.status.idle": "2021-11-07T04:49:48.473415Z", "shell.execute_reply": "2021-11-07T04:49:48.473033Z" } }, "outputs": [], "source": [ "tlist = np.linspace(0, ARGS['T'], 500)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We propagate once for the population dynamics, and once to obtain the\n", "propagated states for each point on the time grid:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "ExecuteTime": { "end_time": "2019-11-10T17:36:54.720111Z", "start_time": "2019-11-10T17:36:51.401596Z" }, "execution": { "iopub.execute_input": "2021-11-07T04:49:48.502045Z", "iopub.status.busy": "2021-11-07T04:49:48.501735Z", "iopub.status.idle": "2021-11-07T04:49:50.480578Z", "shell.execute_reply": "2021-11-07T04:49:50.479382Z" } }, "outputs": [], "source": [ "proj1 = qutip.ket2dm(ket1)\n", "proj2 = qutip.ket2dm(ket2)\n", "proj3 = qutip.ket2dm(ket3)\n", "\n", "guess_dynamics = objectives[0].propagate(\n", " tlist,\n", " propagator=krotov.propagators.expm,\n", " e_ops=[proj1, proj2, proj3],\n", " args=ARGS,\n", ")\n", "guess_states = objectives[0].propagate(\n", " tlist, propagator=krotov.propagators.expm, args=ARGS,\n", ")" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "ExecuteTime": { "end_time": "2019-11-10T17:36:54.741953Z", "start_time": "2019-11-10T17:36:54.723039Z" }, "execution": { "iopub.execute_input": "2021-11-07T04:49:50.490444Z", "iopub.status.busy": "2021-11-07T04:49:50.489736Z", "iopub.status.idle": "2021-11-07T04:49:50.491903Z", "shell.execute_reply": "2021-11-07T04:49:50.492362Z" } }, "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_xlabel('time')\n", " ax.set_ylabel('population')\n", " plt.show(fig)\n", "\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_xlabel('time')\n", " ax.set_ylabel('state norm')\n", " plt.show(fig)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "ExecuteTime": { "end_time": "2019-11-10T17:36:55.325200Z", "start_time": "2019-11-10T17:36:54.746616Z" }, "attributes": { "classes": [], "id": "", "n": "15" }, "execution": { "iopub.execute_input": "2021-11-07T04:49:50.534689Z", "iopub.status.busy": "2021-11-07T04:49:50.533406Z", "iopub.status.idle": "2021-11-07T04:49:50.721111Z", "shell.execute_reply": "2021-11-07T04:49:50.720791Z" }, "lines_to_end_of_cell_marker": 2 }, "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": [ "The population dynamics and the norm-plot show the effect the non-Hermitian\n", "term in the Hamiltonian, resulting in a 30% loss.\n", "\n", "## Optimize\n", "\n", "For each control, we define the update shape and the $\\lambda_a$ parameter that\n", "determines the magnitude of the update:" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "ExecuteTime": { "end_time": "2019-11-10T17:36:55.332957Z", "start_time": "2019-11-10T17:36:55.327833Z" }, "execution": { "iopub.execute_input": "2021-11-07T04:49:50.723486Z", "iopub.status.busy": "2021-11-07T04:49:50.723183Z", "iopub.status.idle": "2021-11-07T04:49:50.724902Z", "shell.execute_reply": "2021-11-07T04:49:50.724593Z" } }, "outputs": [], "source": [ "S = partial( # Scale the *update* of the pulse value at time t\n", " krotov.shapes.flattop,\n", " t_start=0.0,\n", " t_stop=ARGS['T'],\n", " t_rise=ARGS['t_rise'],\n", " func='sinsq',\n", ")" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "ExecuteTime": { "end_time": "2019-11-10T17:36:55.347468Z", "start_time": "2019-11-10T17:36:55.336694Z" }, "execution": { "iopub.execute_input": "2021-11-07T04:49:50.727470Z", "iopub.status.busy": "2021-11-07T04:49:50.727174Z", "iopub.status.idle": "2021-11-07T04:49:50.728887Z", "shell.execute_reply": "2021-11-07T04:49:50.728586Z" } }, "outputs": [], "source": [ "pulse_options = {\n", " H[1][1]: dict(lambda_a=2.0, update_shape=S, args=ARGS),\n", " H[2][1]: dict(lambda_a=2.0, update_shape=S, args=ARGS),\n", " H[3][1]: dict(lambda_a=2.0, update_shape=S, args=ARGS),\n", " H[4][1]: dict(lambda_a=2.0, update_shape=S, args=ARGS)\n", "}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `args` here are required to plug in in the parameters in the guess\n", "`Omega_P1` and `Omega_S1`. Compare this to the [previous\n", "example](02_example_lambda_system_rwa_complex_pulse.ipynb), where the\n", "parameters were hardcoded in the definition of the guess controls." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We now run the optimization for 40 iterations, printing out the fidelity\n", "\n", "$$\n", "F_{\\text{re}} = \\Re{\\Braket{\\Psi(T)}{\\Psi^{\\tgt}}}\n", "$$\n", "\n", "after each iteration." ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "ExecuteTime": { "end_time": "2019-11-10T17:36:55.357820Z", "start_time": "2019-11-10T17:36:55.351667Z" }, "execution": { "iopub.execute_input": "2021-11-07T04:49:50.731145Z", "iopub.status.busy": "2021-11-07T04:49:50.730816Z", "iopub.status.idle": "2021-11-07T04:49:50.732435Z", "shell.execute_reply": "2021-11-07T04:49:50.732130Z" } }, "outputs": [], "source": [ "def print_fidelity(**kwargs):\n", " F_re = np.average(np.array(kwargs['tau_vals']).real)\n", " print(\" F = %f\" % F_re)\n", " return F_re" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "ExecuteTime": { "end_time": "2019-11-10T17:39:07.238734Z", "start_time": "2019-11-10T17:36:55.360831Z" }, "execution": { "iopub.execute_input": "2021-11-07T04:49:50.734950Z", "iopub.status.busy": "2021-11-07T04:49:50.734366Z", "iopub.status.idle": "2021-11-07T04:51:12.025380Z", "shell.execute_reply": "2021-11-07T04:51:12.025130Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " F = -0.007819\n", " F = 0.055166\n", " F = 0.117611\n", " F = 0.178915\n", " F = 0.238526\n", " F = 0.295951\n", " F = 0.350777\n", " F = 0.402680\n", " F = 0.451424\n", " F = 0.496859\n", " F = 0.538920\n", " F = 0.577612\n", " F = 0.613000\n", " F = 0.645200\n", " F = 0.674362\n", " F = 0.700665\n", " F = 0.724303\n", " F = 0.745478\n", " F = 0.764395\n", " F = 0.781255\n", " F = 0.796250\n", " F = 0.809566\n", " F = 0.821372\n", " F = 0.831829\n", " F = 0.841083\n", " F = 0.849267\n", " F = 0.856501\n", " F = 0.862895\n", " F = 0.868544\n", " F = 0.873538\n", " F = 0.877952\n", " F = 0.881855\n", " F = 0.885310\n", " F = 0.888369\n", " F = 0.891080\n", " F = 0.893485\n", " F = 0.895622\n", " F = 0.897522\n", " F = 0.899214\n", " F = 0.900724\n", " F = 0.902073\n" ] } ], "source": [ "opt_result = krotov.optimize_pulses(\n", " objectives, pulse_options, tlist,\n", " propagator=krotov.propagators.expm,\n", " chi_constructor=krotov.functionals.chis_re,\n", " info_hook=print_fidelity,\n", " iter_stop=40\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We look at the optimized controls and the population dynamics they induce:" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "ExecuteTime": { "end_time": "2019-11-10T17:39:07.796565Z", "start_time": "2019-11-10T17:39:07.240910Z" }, "attributes": { "classes": [], "id": "", "n": "17" }, "execution": { "iopub.execute_input": "2021-11-07T04:51:12.044551Z", "iopub.status.busy": "2021-11-07T04:51:12.041040Z", "iopub.status.idle": "2021-11-07T04:51:12.249937Z", "shell.execute_reply": "2021-11-07T04:51:12.249598Z" } }, "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", " opt_result.optimized_controls[0], opt_result.optimized_controls[1], tlist)\n", "print(\"Stokes pulse amplitude and phase:\")\n", "plot_pulse_amplitude_and_phase(\n", " opt_result.optimized_controls[2], opt_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": "2019-11-10T17:39:09.965861Z", "start_time": "2019-11-10T17:39:07.799328Z" }, "attributes": { "classes": [], "id": "", "n": "18" }, "execution": { "iopub.execute_input": "2021-11-07T04:51:12.254277Z", "iopub.status.busy": "2021-11-07T04:51:12.253986Z", "iopub.status.idle": "2021-11-07T04:51:14.127216Z", "shell.execute_reply": "2021-11-07T04:51:14.126880Z" } }, "outputs": [], "source": [ "opt_dynamics = opt_result.optimized_objectives[0].propagate(\n", " tlist, propagator=krotov.propagators.expm, e_ops=[proj1, proj2, proj3])\n", "opt_states = opt_result.optimized_objectives[0].propagate(\n", " tlist, propagator=krotov.propagators.expm)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "ExecuteTime": { "end_time": "2019-11-10T17:39:10.403930Z", "start_time": "2019-11-10T17:39:09.968140Z" }, "attributes": { "classes": [], "id": "", "n": "19" }, "execution": { "iopub.execute_input": "2021-11-07T04:51:14.140200Z", "iopub.status.busy": "2021-11-07T04:51:14.138931Z", "iopub.status.idle": "2021-11-07T04:51:14.290248Z", "shell.execute_reply": "2021-11-07T04:51:14.289942Z" } }, "outputs": [ { "data": { "image/png": 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\n", 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A8cCmo80gkg5OPaErT914Bv9+zkAeXVjOxb98idWVGkpEjl8yBbEHeMPMfmNmd+6fknjfeGCVu6929xrgQWDKQduUAC8E87P3rw+KJNPdnwdw993uftQlJZIusjNjfHXSEP74+ZPZtqeGj//6ZRasbVWnCaUNSqYg/kriHMTLwIJG05EUA+sbvS4PljW2CLg0mL8EyDOzrsBgYLuZPWpmC83sR8EeyT8xs2lmVmZmZZWVlUlEEkltpw8s4NHrTiM/J4t/+e2r/GOl/l3IsTtsQQS/lK9x93sPnprp598EnGVmC4GzgAqgnsTlt2cG68cBA4BrDn6zu89w91J3Ly0sLGymSCJtW9+uuTx63en0L8jl2pllvLJ6S9SRpI06bEG4ez3QYGbHcldOBdC70etewbLG33+Du1/q7qOBbwXLtpPY23gjODxVR2IvZswxZBBJS11y49z/hZPp3bk9n/vDfJZt2Bl1JGmDkjnEtBtYYmZ3H+U5iPnAIDPrb2Zx4Arg8cYbmFmBme3P8A3gnkbv7WRm+3cLzgGWJfEzRSTQtUM2f/zCyeTnZPGFe+ezaee+qCNJG5NMQTwKfBuYy1Gcgwj+8r8BeBZ4C3jI3Zea2a1mdnGw2URguZmtALqTONexf8/lJmCWmS0BDPjtUXwuEQG6d2zH764uZfveWqbdt4CaOo2WI8lLarjvYA9gcPByubvXhprqGOhGOZFDe3rJe1x3/+tMmzCAb15wUtRxpBU5rhvlzGwisJLEPQ2/AlaY2YTmDCgi4bpgeBGfOqUPM+auZvbbuqVIkpPMIaYfA5Pc/Sx3nwB8DPhpuLFEpLn9x+QSTuyRx9f/vJgdVa3uIIC0QskURJa7L9//wt1XABqYXqSNaZcV447LRrJ1Tw3ff0rXfMiRJVMQZWb2OzObGEy/BXSwX6QNGlacz7QJA3h4QbluopMjSqYg/o3EJaY3BtOyYJmItEFf/Mgg+hfkcstjS3VVkxzWEQvC3avd/SfBDW2XuvtP3b36SO8TkdapXVaM71xYwurNe7j35TVRx5FWLJmrmE43s+fNbIWZrd4/tUQ4EQnH2Sd245wTu/HzWSup3KW/96RpyRxiuhv4CXAGiXGR9k8i0oZ9+8IS9tXW88sXVkYdRVqpZApih7s/4+6b3H3L/in0ZCISqv4FuVxW2ps/vbaO8m0aTV8+LJmCmB0Mt32qmY3ZP4WeTERCd+NHBmJm/GLWqqijSCuUmcQ2JwdfG9+K7SQG0BORNqwoP4erTu7DzHlr+deJJ9C/IDfqSNKKJHMV09lNTCoHkRRx3cSBxGMZ/OzvK6KOIq1MMoeYRCSFFeZl8+lT+/LEog2s36pzEfIBFYSI8LnT+xPLMH73D13BLh9QQYgIPfLbMXVUMf9Ttp6te2qijiOtRDI3yrU3s28HYzBhZoPM7MLwo4lIS5o2YQD7ahuYOW9N1FGklUhmD+L3QDVwavC6Avh+aIlEJBKDuufx0ZO6ce/La6iqqYs6jrQCyRTECe7+Q6AWwN2rSDwCVERSzLQJJ7Ctqpa/LtwQdRRpBZIpiBozyyFx7wNmdgKJPQoRSTHj+nXmxB55zJy3hmQeRyypLZmC+C7wN6C3md0PzAL+f5ihRCQaZsbVp/Xj7Y27KFu7Leo4ErFkbpR7DrgUuAZ4ACh199kh5xKRiEwZ1ZO8dpnMnLc26igSsWSuYpoVDND3lLs/6e6bzWxWS4QTkZbXPp7JZWN788yS99i0c1/UcSRChywIM2tnZl2AAjPrbGZdgqkfUNxiCUWkxX361L7UNTgPvLY+6igSocPtQfw/YAFwYvB1//QY8Mvwo4lIVPoX5HLGwAIeKltPQ4NOVqerQxaEu//c3fsDN7n7AHfvH0wj3V0FIZLiPjmuNxXb9/LSO5ujjiIROeJw3+7+CzMbBpQA7RotnxlmMBGJ1qSS7uTnZPFQWTlnDiqMOo5E4IgFYWa3ABNJFMTTwPnAi4AKQiSFtcuKMXVUTx6Yv57tVTV0ah+POpK0sGTug/gE8BFgo7t/FhgJ5IeaSkRahU+O601NXQOPvaE7q9NRMgWx190bgDoz6whsAnqHG0tEWoOhPfMZ2rMjD5XpaqZ0lExBlJlZJ+C3JK5ieh2YF2YoEWk9Lh/Xm6UbdvJmxY6oo0gLS+ZO6uvcfbu73wWcC1wdHGoSkTQwZWQx8cwMHtZeRNpJ6k7q/fPuvsbdF+tOapH0kd8+i0kl3Xl80QZq6xuijiMtKNQ7qc3sPDNbbmarzOzmJtb3NbNZZrbYzOaYWa+D1nc0s3Iz030XIhG6ZHQx26pqmbuiMuoo0oJCu5PazGLAdBKXxZYAV5pZyUGb3QHMdPcRwK3A7Qet/x4w98gfQ0TCNGFwIZ3bZ/GXhRVRR5EWFOad1OOBVe6+2t1rgAeBKQdtUwK8EMzPbrzezMYC3YHnjuLziEgIsmIZXDSyJ88ve59d+2qjjiMtJJmrmDaaWR6Amf2HmT1qZmOSeF8x0PisVjkfPjS1iMRQ4gCXAHlm1tXMMoAfAzcd7geY2TQzKzOzsspK7fqKhGnq6GKq6xp45s2NUUeRFpJMQXzb3XeZ2RnAR4G7gV8308+/CTjLzBYCZ5F43nU9cB3wtLuXH+7N7j7D3UvdvbSwUEMBiIRpdO9O9O3anr/qMFPaSKYg6oOvk4EZ7v4UkMw99xX88w11vYJlB7j7Bne/1N1HA98Klm0HTgVuMLM1JM5TfMbM/juJnykiITEzpo4qZt7qLby3Y2/UcaQFJFMQFWb2G+By4Gkzy07yffOBQWbW38ziwBXA4403MLOC4HASwDeAewDc/Sp37+Pu/UjsZcx09w9dBSUiLWvq6GLc4XENvZEWkvlF/0ngWeBjwV/3XYCvHelN7l4H3BC89y3gIXdfama3mtnFwWYTgeVmtoLECenbjvoTiEiL6V+Qy6jenXQ1U5ow99R4GEhpaamXlZVFHUMk5d378hpueXwpz3zxTE4q6hh1HDlOZrbA3UubWpfMHoSIyAEXjigiM8P46xvai0h1KggROSpdO2Rz5qACnnhjgx5HmuJUECJy1KaOLmbDjn3MX7M16igSIhWEiBy1c0u6k5MV46+6mimlqSBE5Ki1j2cyaWh3nl7yHjV1GuE1VakgROSYTB1VzI69tcxZvinqKBISFYSIHJMzBhXQJTfOY4t0mClVqSBE5JhkxTK4cEQRf9cIrylLBSEix2zKqMQIr88ufT/qKBICFYSIHLMxfTrRu0sOj+mmuZSkghCRY2ZmTBlZzEurNrNp176o40gzU0GIyHGZOronDQ5PLnov6ijSzFQQInJcBnbLY2jPjjrMlIJUECJy3KaOKmZR+Q7e3bwn6ijSjFQQInLcLhrZEzP0ONIUo4IQkePWI78dp/TvymNvVJAqz5gRFYSINJOpo3uyZksVi8t3RB1FmokKQkSaxXnDiojHMvQgoRSighCRZpGfk8U5J3bjiUXvUVevEV5TgQpCRJrN1NE92by7mpff2RJ1FGkGKggRaTYTh3Qjr12mDjOlCBWEiDSbdlkxzh/Wg2ff3Mjemvqo48hxUkGISLOaOqqYPTX1zHpbI7y2dSoIEWlWJw/oSveO2fx1oR4k1NapIESkWcUyjCmjipmzfBOVu6qjjiPHQQUhIs3uk6W9qGtw/rKwPOoochxUECLS7AZ2y6O0b2cenL9eQ2+0YSoIEQnF5eN6s7pyD2Vrt0UdRY6RCkJEQjF5RBEdsjN58LX1UUeRY6SCEJFQtI9nctHInjy1ZAM799VGHUeOgQpCREJzxbje7Ktt4IlFuuS1LQq1IMzsPDNbbmarzOzmJtb3NbNZZrbYzOaYWa9g+Sgzm2dmS4N1l4eZU0TCMaJXPif2yNNhpjYqtIIwsxgwHTgfKAGuNLOSgza7A5jp7iOAW4Hbg+VVwGfcfShwHvAzM+sUVlYRCYeZ8S8n92FJxQ7eWL896jhylMLcgxgPrHL31e5eAzwITDlomxLghWB+9v717r7C3VcG8xuATUBhiFlFJCSXjulFh+xM7n15TdRR5CiFWRDFQOP9yvJgWWOLgEuD+UuAPDPr2ngDMxsPxIF3QsopIiHqkJ3JJ8b24snFG9i0a1/UceQoRH2S+ibgLDNbCJwFVAAHhoA0syLgPuCz7v6hJ5CY2TQzKzOzssrKypbKLCJH6TOn9qW23nngVZ2LaEvCLIgKoHej172CZQe4+wZ3v9TdRwPfCpZtBzCzjsBTwLfc/ZWmfoC7z3D3UncvLSzUESiR1mpAYQcmDC7k/lfXUlOnp821FWEWxHxgkJn1N7M4cAXweOMNzKzAzPZn+AZwT7A8DvyFxAnsR0LMKCIt5JrT+rJpVzXPvPle1FEkSaEVhLvXATcAzwJvAQ+5+1Izu9XMLg42mwgsN7MVQHfgtmD5J4EJwDVm9kYwjQorq4iEb+LgbpxQmMtd/7ta4zO1EZYq/6NKS0u9rKws6hgichgPl63na48s5vfXjOPsE7tFHUcAM1vg7qVNrYv6JLWIpJEpo4rpmd+OX81ZFXUUSYIKQkRaTDwzg2snDGD+mm3MX7M16jhyBCoIEWlRV4zrQ5fcONNnay+itVNBiEiLyonHuPbMAcxZXqm9iFZOBSEiLe6a0/rRLS+bHzzztq5oasVUECLS4nLiMb740UGUrd3GrLc2RR1HDkEFISKR+GRpb/oX5PKjZ5dT36C9iNZIBSEikciKZfDVSYNZ/v4uHirTGE2tkQpCRCIzeXgR4/t34Qd/e5tte2qijiMHUUGISGTMjO9NGcaufXX88Nm3o44jB1FBiEikhvTI47On9ePB+et5dfWWqONIIyoIEYncl88dTJ8u7bnpkUXsqa6LOo4EVBAiErnc7EzuuGwk5dv28l9PvxV1HAmoIESkVRjXrwvXnjmA+19dx1OL9cyI1kAFISKtxk2ThjCmTye+/sgiVm3aHXWctKeCEJFWI56ZwfSrxpCdFWPafWW69DViKggRaVWK8nO461NjKd+2l2tnlrGvtj7qSGlLBSEirc74/l346SdHUbZ2G9fd/zrVdSqJKKggRKRVmjyiiNsuGcYLb29i2swF2pOIgApCRFqtq07uyw8+Ppy5Kyu5YsYrbNq1L+pIaUUFISKt2uXj+vDrq8awfOMupv7yJV5fty3qSGlDBSEird55w4p4+F9Pxcy47K55/PzvK6mpa4g6VspTQYhImzCsOJ+nv3gmk4cX8dO/r+C8n81l9nI9bChMKggRaTPyc7K488rR/P6z4wD47O/nc/lv5jF7+SY9ujQElir/UUtLS72srCzqGCLSQmrqGrjvlbX8du5qNu7cx+DuHbh0TC+mjOpJUX5O1PHaDDNb4O6lTa5TQYhIW1ZT18Bjb1Twp9fWsXDddsxgeHE+Zw4q4IyBhYzq3YmceCzqmK2WCkJE0sKazXt4fNEG5q6oZOH67dQ3OLEMY2BhB4YWd2Rgtw7065pLny7t6dO1PXnZmZhZ1LEjpYIQkbSzc18tr7yzhSUVO3izYgdvbthJ5a7qf9omOzODgg7ZFHSIU9Ahmy65cXKzM+mQnRl8jdE+npjPiceIxzKIZxpZsQyyYhnEMzOIB/NZMSOemUEsw8iw/RPEMqxVl9DhCiKzpcOIiLSEju2ymDS0B5OG9jiwbNe+WtZtrWLtlirWb61i8+5qNu+uYfPuajbs2MfSDTvZU13H7po6mvtv58ZlEQvKI8OMjIx/LpL9yw0OFItZMGHB18S6/fMlPfP5xZWjmzcwKggRSSN57bIY2jOfoT3zD7udu7O3tp491fWJwqiuo7qunpo6p7a+gZq6hsTX+gZq6/2D13UN1LvT4I471Dck5hsanAaHBnfqD1r3T9u5U9+QWOYQfE288CDXB8uD1w59uoRzUl4FISJyEDOjfTyT9vFMCvOyo44TGd0HISIiTQq1IMzsPDNbbmarzOzmJtb3NbNZZrbYzOaYWa9G6642s5XBdHWYOUVE5MNCKwgziwHTgfOBEuBKMys5aLM7gJnuPgK4Fbg9eG8X4BbgZGA8cIuZdQ4rq4iIfFiYexDjgVXuvtrda4AHgSkHbVMCvBDMz260/mPA8+6+1d23Ac8D54WYVUREDhJmQRQD6xu9Lg+WNbYIuDSYvwTIM7OuSb4XM5tmZmVmVlZZWdlswUVEJPqT1DcBZ5nZQuAsoAJI+rFR7j7D3UvdvbSwsDCsjCIiaSnMy1wrgN6NXvcKlh3g7hsI9iDMrAPwcXffbmYVwMSD3jsnxKwiInKQMPcg5gODzKy/mcWBK4DHG29gZgVmtj/DN4B7gvlngUlm1jk4OT0pWCYiIi0ktD0Id68zsxtI/GKPAfe4+1IzuxUoc/fHSewl3G5mDswFrg/eu9XMvkeiZABudfeth/t5CxYs2Gxma48jcgGw+Tje3xal22dOt88L+szp4ng+c99DrUiZwfqOl5mVHWrAqlSVbp853T4v6DOni7A+c9QnqUVEpJVSQYiISJNUEB+YEXWACKTbZ063zwv6zOkilM+scxAiItIk7UGIiEiTVBAiItKktC+IIw1JnmrM7B4z22Rmb0adpaWYWW8zm21my8xsqZl9MepMYTOzdmb2mpktCj7zf0adqSWYWczMFprZk1FnaSlmtsbMlpjZG2ZW1qzfO53PQQRDkq8AziUxIOB84Ep3XxZpsBCZ2QRgN4lh1odFnaclmFkRUOTur5tZHrAAmJri/58NyHX33WaWBbwIfNHdX4k4WqjM7CtAKdDR3S+MOk9LMLM1QKm7N/vNgem+B5HMkOQpxd3nAoe9Kz3VuPt77v56ML8LeIsmRgdOJZ6wO3iZFUwp/ddg8MCxycDvos6SKtK9IJIaVlxSh5n1A0YDr0YcJXTB4ZY3gE0knq+S6p/5Z8DXgYaIc7Q0B54zswVmNq05v3G6F4SkkWDE4D8DX3L3nVHnCZu717v7KBKjIY83s5Q9pGhmFwKb3H1B1FkicIa7jyHx9M7rg8PIzSLdC+KIQ5JLagiOw/8ZuN/dH406T0ty9+0kntiYyk9lPB24ODge/yBwjpn9MdpILcPdK4Kvm4C/kDh03izSvSCOOCS5tH3BCdu7gbfc/SdR52kJZlZoZp2C+RwSF2K8HWmoELn7N9y9l7v3I/Hv+AV3/1TEsUJnZrnBhReYWS6JRyM02xWKaV0Q7l4H7B+S/C3gIXdfGm2qcJnZA8A8YIiZlZvZ56PO1AJOBz5N4q/KN4LpgqhDhawImG1mi0n8IfS8u6fNpZ9ppDvwopktAl4DnnL3vzXXN0/ry1xFROTQ0noPQkREDk0FISIiTVJBiIhIk1QQIiLSJBWEiIg0SQUhcozMrJOZXRfM9zSzR6LOJNKcdJmryDEKxnV6Ml1GxZX0kxl1AJE27L+BE4IB8VYCJ7n7MDO7BpgK5AKDgDuAOImb9aqBC9x9q5mdAEwHCoEq4Fp3T9m7naXt0SEmkWN3M/BOMCDe1w5aNwy4FBgH3AZUuftoEnexfybYZgbw7+4+FrgJ+FVLhBZJlvYgRMIxO3j2xC4z2wE8ESxfAowIRpY9DXg4MVQUANktH1Pk0FQQIuGobjTf0Oh1A4l/dxnA9mDvQ6RV0iEmkWO3C8g7ljcGz6N418wug8SIs2Y2sjnDiRwvFYTIMXL3LcBLZvYm8KNj+BZXAZ8PRuJcSoo/7lbaHl3mKiIiTdIehIiINEkFISIiTVJBiIhIk1QQIiLSJBWEiIg0SQUhIiJNUkGIiEiT/g+Q5ITwijCK0QAAAABJRU5ErkJggg==\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": [ "These dynamics show that the non-Hermitian Hamiltonian has the desired effect:\n", "The population is steered out of the decaying $\\ket{2}$ state, with the\n", "resulting loss in norm down to 10% from the 30% loss of the guess pulses.\n", "Indeed, these 10% are exactly the value of the error $1 - F_{\\text{re}}$,\n", "indicating that avoiding population in the $\\ket{2}$ part is the difficult part\n", "of the optimization. Convergence towards this goal is slow, so we continue the\n", "optimization up to iteration 2000." ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "ExecuteTime": { "end_time": "2019-11-10T17:39:10.423760Z", "start_time": "2019-11-10T17:39:10.406834Z" }, "execution": { "iopub.execute_input": "2021-11-07T04:51:14.292941Z", "iopub.status.busy": "2021-11-07T04:51:14.292620Z", "iopub.status.idle": "2021-11-07T04:51:14.308349Z", "shell.execute_reply": "2021-11-07T04:51:14.308049Z" } }, "outputs": [], "source": [ "dumpfile = \"./non_herm_opt_result.dump\"\n", "if os.path.isfile(dumpfile):\n", " opt_result = krotov.result.Result.load(dumpfile, objectives)\n", "else:\n", " opt_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=opt_result\n", " )\n", " opt_result.dump(dumpfile)" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "ExecuteTime": { "end_time": "2019-11-10T17:39:10.432245Z", "start_time": "2019-11-10T17:39:10.426406Z" }, "attributes": { "classes": [], "id": "", "n": "21" }, "execution": { "iopub.execute_input": "2021-11-07T04:51:14.310425Z", "iopub.status.busy": "2021-11-07T04:51:14.310132Z", "iopub.status.idle": "2021-11-07T04:51:14.311993Z", "shell.execute_reply": "2021-11-07T04:51:14.311698Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Final fidelity: 0.966\n" ] } ], "source": [ "print(\"Final fidelity: %.3f\" % opt_result.info_vals[-1])" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "ExecuteTime": { "end_time": "2019-11-10T17:39:10.444000Z", "start_time": "2019-11-10T17:39:10.437302Z" }, "attributes": { "classes": [], "id": "", "n": "22" }, "execution": { "iopub.execute_input": "2021-11-07T04:51:14.314237Z", "iopub.status.busy": "2021-11-07T04:51:14.313947Z", "iopub.status.idle": "2021-11-07T04:51:14.315498Z", "shell.execute_reply": "2021-11-07T04:51:14.315205Z" } }, "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": "markdown", "metadata": {}, "source": [ "To get a feel for the convergence, we can plot the optimization error over the iteration number:" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "ExecuteTime": { "end_time": "2019-11-10T17:39:11.043915Z", "start_time": "2019-11-10T17:39:10.451812Z" }, "attributes": { "classes": [], "id": "", "n": "23" }, "execution": { "iopub.execute_input": "2021-11-07T04:51:14.327742Z", "iopub.status.busy": "2021-11-07T04:51:14.326937Z", "iopub.status.idle": "2021-11-07T04:51:14.888743Z", "shell.execute_reply": "2021-11-07T04:51:14.888437Z" }, "lines_to_next_cell": 0 }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plot_convergence(opt_result)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We have used here that the return value of the routine `print_fidelity` that was passed to the `optimize_pulses` routine as an `info_hook` is automatically accumulated in `result.info_vals`.\n", "\n", "We also look at optimized controls and the dynamics they induce:" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "ExecuteTime": { "end_time": "2019-11-10T17:39:11.630393Z", "start_time": "2019-11-10T17:39:11.048493Z" }, "attributes": { "classes": [], "id": "", "n": "25" }, "execution": { "iopub.execute_input": "2021-11-07T04:51:14.911953Z", "iopub.status.busy": "2021-11-07T04:51:14.902900Z", "iopub.status.idle": "2021-11-07T04:51:15.117430Z", "shell.execute_reply": "2021-11-07T04:51:15.117122Z" } }, "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", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "print(\"pump pulse amplitude and phase:\")\n", "plot_pulse_amplitude_and_phase(\n", " opt_result.optimized_controls[0], opt_result.optimized_controls[1], tlist)\n", "print(\"Stokes pulse amplitude and phase:\")\n", "plot_pulse_amplitude_and_phase(\n", " opt_result.optimized_controls[2], opt_result.optimized_controls[3], tlist)" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "ExecuteTime": { "end_time": "2019-11-10T17:39:14.461895Z", "start_time": "2019-11-10T17:39:11.633126Z" }, "execution": { "iopub.execute_input": "2021-11-07T04:51:15.121656Z", "iopub.status.busy": "2021-11-07T04:51:15.121362Z", "iopub.status.idle": "2021-11-07T04:51:16.992198Z", "shell.execute_reply": "2021-11-07T04:51:16.991774Z" }, "lines_to_next_cell": 2 }, "outputs": [], "source": [ "opt_dynamics = opt_result.optimized_objectives[0].propagate(\n", " tlist, propagator=krotov.propagators.expm, e_ops=[proj1, proj2, proj3])\n", "opt_states = opt_result.optimized_objectives[0].propagate(\n", " tlist, propagator=krotov.propagators.expm)" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "ExecuteTime": { "end_time": "2019-11-10T17:39:14.988468Z", "start_time": "2019-11-10T17:39:14.463937Z" }, "attributes": { "classes": [], "id": "", "n": "26" }, "execution": { "iopub.execute_input": "2021-11-07T04:51:17.005623Z", "iopub.status.busy": "2021-11-07T04:51:17.005160Z", "iopub.status.idle": "2021-11-07T04:51:17.160333Z", "shell.execute_reply": "2021-11-07T04:51:17.160024Z" } }, "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": [ "In accordance with the lower optimization error, the population dynamics now show\n", "a reasonably efficient transfer, and a significantly reduced population in\n", "state $\\ket{2}$.\n", "\n", "Finally, we can convert the complex-valued $\\Omega_P$ and $\\Omega_S$ functions to the\n", "physical electric fields $\\epsilon_{P}$ and $\\epsilon_{S}$:" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "ExecuteTime": { "end_time": "2019-11-10T17:39:15.431996Z", "start_time": "2019-11-10T17:39:14.994569Z" }, "execution": { "iopub.execute_input": "2021-11-07T04:51:17.186196Z", "iopub.status.busy": "2021-11-07T04:51:17.173692Z", "iopub.status.idle": "2021-11-07T04:51:17.346121Z", "shell.execute_reply": "2021-11-07T04:51:17.345820Z" } }, "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", "text/plain": [ "
" ] }, "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'$\\mu_{12}\\,\\epsilon_{P}$')\n", " elif case == 'stokes':\n", " ax.set_ylabel(r'$ \\mu_{23}\\,\\epsilon_{S}$')\n", " plt.show()\n", "\n", "\n", "print('Physical electric pump pulse in the lab frame:')\n", "plot_physical_field(\n", " opt_result.optimized_controls[0], opt_result.optimized_controls[1], tlist, case = 'pump')\n", "\n", "\n", "print('Physical electric Stokes pulse in the lab frame:')\n", "plot_physical_field(\n", " opt_result.optimized_controls[2], opt_result.optimized_controls[3], tlist, case = 'stokes')" ] } ], "metadata": { "hide_input": false, "jupytext": { "formats": "" }, "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", "pygments_lexer": "ipython3", "version": "3.8.1" }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": true, "sideBar": true, "skip_h1_title": false, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": {}, "toc_section_display": true, "toc_window_display": false }, "varInspector": { "cols": { "lenName": 16, "lenType": 16, "lenVar": 40 }, "kernels_config": { "python": { "delete_cmd_postfix": "", "delete_cmd_prefix": "del ", "library": "var_list.py", "varRefreshCmd": "print(var_dic_list())" }, "r": { "delete_cmd_postfix": ") ", "delete_cmd_prefix": "rm(", "library": "var_list.r", "varRefreshCmd": "cat(var_dic_list()) " } }, "types_to_exclude": [ "module", "function", "builtin_function_or_method", "instance", "_Feature" ], "window_display": false } }, "nbformat": 4, "nbformat_minor": 2 }