{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Calculation of control fields for Lindbladian dynamics using L-BFGS-B algorithm" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Alexander Pitchford (agp1@aber.ac.uk)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Example to demonstrate using the control library to determine control\n", "pulses using the ctrlpulseoptim.optimize_pulse function.\n", "The (default) L-BFGS-B algorithm is used to optimise the pulse to\n", "minimise the fidelity error, which in this case is given by the\n", "'Trace difference' norm.\n", "\n", "This in an open quantum system example, with a single qubit subject to\n", "an amplitude damping channel. The target evolution is the Hadamard gate.\n", "The user can experiment with the strength of the amplitude damping by\n", "changing the gamma variable value\n", "\n", "The user can experiment with the timeslicing, by means of changing the\n", "number of timeslots and/or total time for the evolution.\n", "Different initial (starting) pulse types can be tried.\n", "The initial and final pulses are displayed in a plot\n", "\n", "This example assumes that the example-control-pulseoptim-Hadamard has already been tried, and hence explanations in that notebook are not repeated here." ] }, { "cell_type": "code", "collapsed": false, "input": [ "%matplotlib inline\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import datetime" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [ "from qutip import Qobj, identity, sigmax, sigmay, sigmaz, tensor\n", "from qutip.qip import hadamard_transform\n", "import qutip.logging as logging\n", "logger = logging.get_logger()\n", "#Set this to None or logging.WARN for 'quiet' execution\n", "log_level = logging.INFO\n", "#QuTiP control modules\n", "import qutip.control.pulseoptim as cpo\n", "\n", "example_name = 'Lindblad'" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Defining the physics" ] }, { "cell_type": "code", "collapsed": false, "input": [ "Sx = sigmax()\n", "Sy = sigmay()\n", "Sz = sigmaz()\n", "Si = identity(2)\n", "\n", "Sd = Qobj(np.array([[0, 1],\n", " [0, 0]]))\n", "Sm = Qobj(np.array([[0, 0],\n", " [1, 0]]))\n", "Sd_m = Qobj(np.array([[1, 0],\n", " [0, 0]]))\n", "Sm_d = Qobj(np.array([[0, 0],\n", " [0, 1]]))\n", "\n", "#Amplitude damping#\n", "#Damping rate:\n", "gamma = 0.1\n", "L0_Ad = gamma*(2*tensor(Sm, Sd.trans()) - \n", " (tensor(Sd_m, Si) + tensor(Si, Sd_m.trans())))\n", "#sigma X control\n", "LC_x = -1j*(tensor(Sx, Si) - tensor(Si, Sx))\n", "#sigma Y control\n", "LC_y = -1j*(tensor(Sy, Si) - tensor(Si, Sy.trans()))\n", "#sigma Z control\n", "LC_z = -1j*(tensor(Sz, Si) - tensor(Si, Sz))\n", "\n", "#Drift\n", "drift = L0_Ad\n", "#Controls\n", "ctrls = [LC_z, LC_x]\n", "# Number of ctrls\n", "n_ctrls = len(ctrls)\n", "\n", "initial = identity(4)\n", "#Target\n", "#Hadamard gate\n", "had_gate = hadamard_transform(1)\n", "target_DP = tensor(had_gate, had_gate)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 3 }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Defining the time evolution parameters" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Number of time slots\n", "n_ts = 200\n", "# Time allowed for the evolution\n", "evo_time = 2" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 4 }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Set the conditions which will cause the pulse optimisation to terminate" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Fidelity error target\n", "fid_err_targ = 1e-10\n", "# Maximum iterations for the optisation algorithm\n", "max_iter = 200\n", "# Maximum (elapsed) time allowed in seconds\n", "max_wall_time = 30\n", "# Minimum gradient (sum of gradients squared)\n", "# as this tends to 0 -> local minima has been found\n", "min_grad = 1e-20" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 5 }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Set the initial pulse type" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# pulse type alternatives: RND|ZERO|LIN|SINE|SQUARE|SAW|TRIANGLE|\n", "p_type = 'RND'" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 6 }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Give an extension for output files" ] }, { "cell_type": "code", "collapsed": false, "input": [ "#Set to None to suppress output files\n", "f_ext = \"{}_n_ts{}_ptype{}.txt\".format(example_name, n_ts, p_type)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 7 }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Run the optimisation" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Note that this call will take the defaults\n", "# dyn_type='GEN_MAT'\n", "# This means that matrices that describe the dynamics are assumed to be\n", "# general, i.e. the propagator can be calculated using:\n", "# expm(combined_dynamics*dt)\n", "# prop_type='FRECHET'\n", "# and the propagators and their gradients will be calculated using the\n", "# Frechet method, i.e. an exact gradent\n", "# fid_type='TRACEDIFF'\n", "# and that the fidelity error, i.e. distance from the target, is give\n", "# by the trace of the difference between the target and evolved operators \n", "result = cpo.optimize_pulse(drift, ctrls, initial, target_DP, n_ts, evo_time, \n", " fid_err_targ=fid_err_targ, min_grad=min_grad, \n", " max_iter=max_iter, max_wall_time=max_wall_time, \n", " out_file_ext=f_ext, init_pulse_type=p_type, \n", " log_level=log_level, gen_stats=True)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stderr", "text": [ "INFO:qutip.control.pulseoptim:System configuration:\n", "Drift dynamics generator:\n", "[[-0.2+0.j 0.0+0.j 0.0+0.j 0.0+0.j]\n", " [ 0.0+0.j -0.1+0.j 0.0+0.j 0.0+0.j]\n", " [ 0.0+0.j 0.0+0.j -0.1+0.j 0.0+0.j]\n", " [ 0.2+0.j 0.0+0.j 0.0+0.j 0.0+0.j]]\n", "Control 1 dynamics generator:\n", "[[ 0.+0.j 0.+0.j 0.+0.j 0.+0.j]\n", " [ 0.+0.j 0.-2.j 0.+0.j 0.+0.j]\n", " [ 0.+0.j 0.+0.j 0.+2.j 0.+0.j]\n", " [ 0.+0.j 0.+0.j 0.+0.j 0.+0.j]]\n", "Control 2 dynamics generator:\n", "[[ 0.+0.j 0.+1.j 0.-1.j 0.+0.j]\n", " [ 0.+1.j 0.+0.j 0.+0.j 0.-1.j]\n", " [ 0.-1.j 0.+0.j 0.+0.j 0.+1.j]\n", " [ 0.+0.j 0.-1.j 0.+1.j 0.+0.j]]\n", "Initial operator:\n", "[[ 1.+0.j 0.+0.j 0.+0.j 0.+0.j]\n", " [ 0.+0.j 1.+0.j 0.+0.j 0.+0.j]\n", " [ 0.+0.j 0.+0.j 1.+0.j 0.+0.j]\n", " [ 0.+0.j 0.+0.j 0.+0.j 1.+0.j]]\n", "Target operator:\n", "[[ 0.5+0.j 0.5+0.j 0.5+0.j 0.5+0.j]\n", " [ 0.5+0.j -0.5+0.j 0.5+0.j -0.5+0.j]\n", " [ 0.5+0.j 0.5+0.j -0.5+0.j -0.5+0.j]\n", " [ 0.5+0.j -0.5+0.j -0.5+0.j 0.5+0.j]]\n", "INFO:qutip.control.pulseoptim:Initial amplitudes output to file: ctrl_amps_initial_Lindblad_n_ts200_ptypeRND.txt\n", "INFO:qutip.control.optimizer:Optimising pulse using L-BFGS-B\n", "INFO:qutip.control.pulseoptim:Final amplitudes output to file: ctrl_amps_final_Lindblad_n_ts200_ptypeRND.txt\n" ] } ], "prompt_number": 8 }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Report the results" ] }, { "cell_type": "code", "collapsed": false, "input": [ "result.stats.report()\n", "print(\"Final evolution\\n{}\\n\".format(result.evo_full_final))\n", "print(\"********* Summary *****************\")\n", "print(\"Final fidelity error {}\".format(result.fid_err))\n", "print(\"Final gradient normal {}\".format(result.grad_norm_final))\n", "print(\"Terminated due to {}\".format(result.termination_reason))\n", "print(\"Number of iterations {}\".format(result.num_iter))\n", "print(\"Completed in {} HH:MM:SS.US\".format(datetime.timedelta(seconds=result.wall_time)))" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "\n", "------------------------------------\n", "---- Control optimisation stats ----\n", "**** Timings (HH:MM:SS.US) ****\n", "Total wall time elapsed during optimisation: 0:00:30.118382\n", "Wall time computing Hamiltonians: 0:00:00.626220 (2.08%)\n", "Wall time computing propagators: 0:00:26.829948 (89.08%)\n", "Wall time computing forward propagation: 0:00:00.098895 (0.33%)\n", "Wall time computing onward propagation: 0:00:00.096445 (0.32%)\n", "Wall time computing gradient: 0:00:02.407970 (8.00%)\n", "\n", "**** Iterations and function calls ****\n", "Number of iterations: 128\n", "Number of fidelity function calls: 168\n", "Number of times fidelity is computed: 168\n", "Number of gradient function calls: 168\n", "Number of times gradients are computed: 168\n", "Number of times timeslot evolution is recomputed: 168\n", "\n", "**** Control amplitudes ****\n", "Number of control amplitude updates: 167\n", "Mean number of updates per iteration: 1.3046875\n", "Number of timeslot values changed: 33400\n", "Mean number of timeslot changes per update: 200.0\n", "Number of amplitude values changed: 66800\n", "Mean number of amplitude changes per update: 400.0\n", "------------------------------------\n", "Final evolution\n", "Quantum object: dims = [[4], [4]], shape = [4, 4], type = oper, isherm = False\n", "Qobj data =\n", "[[ 0.49311703 -1.03216105e-16j 0.37516863 -3.77560294e-03j\n", " 0.37516863 +3.77560294e-03j 0.50421905 -5.03070375e-17j]\n", " [ 0.38301167 -8.20510888e-03j -0.39243479 -1.28423923e-02j\n", " 0.38198219 +5.64382365e-03j -0.39095475 +1.11005674e-02j]\n", " [ 0.38301167 +8.20510888e-03j 0.38198219 -5.64382365e-03j\n", " -0.39243479 +1.28423923e-02j -0.39095475 -1.11005674e-02j]\n", " [ 0.50688297 +1.03215974e-16j -0.37516863 +3.77560294e-03j\n", " -0.37516863 -3.77560294e-03j 0.49578095 +3.64291220e-17j]]\n", "\n", "********* Summary *****************\n", "Final fidelity error 0.02068059184705504\n", "Final gradient normal 6.0807607767135144e-05\n", "Terminated due to Max wall time exceeded\n", "Number of iterations 128\n", "Completed in 0:00:30.118382 HH:MM:SS.US\n" ] } ], "prompt_number": 9 }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Plot the initial and final amplitudes" ] }, { "cell_type": "code", "collapsed": false, "input": [ "t = result.time[:n_ts]\n", "\n", "fig1 = plt.figure()\n", "ax1 = fig1.add_subplot(2, 1, 1)\n", "ax1.set_title(\"Initial Control amps\")\n", "ax1.set_xlabel(\"Time\")\n", "ax1.set_ylabel(\"Control amplitude\")\n", "for j in range(n_ctrls):\n", " amps = result.initial_amps[:, j]\n", " ax1.plot(t, amps)\n", "\n", "ax2 = fig1.add_subplot(2, 1, 2)\n", "ax2.set_title(\"Optimised Control Amplitudes\")\n", "ax2.set_xlabel(\"Time\")\n", "ax2.set_ylabel(\"Control amplitude\")\n", "for j in range(n_ctrls):\n", " amps = result.final_amps[:, j]\n", " ax2.plot(t, amps)\n", "\n", "plt.show()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": "iVBORw0KGgoAAAANSUhEUgAAAY0AAAEZCAYAAABrUHmEAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nEhoKhUKhsBslNBQKhUJhN/8foVFCPTmGzFwAAAAASUVORK5CYII=\n", "text": [ "" ] } ], "prompt_number": 10 }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Versions" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from qutip.ipynbtools import version_table\n", "\n", "version_table()" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "
SoftwareVersion
QuTiP3.1.0
Numpy1.9.1
matplotlib1.4.2
Python3.4.0 (default, Apr 11 2014, 13:05:11) \n", "[GCC 4.8.2]
IPython2.3.1
Cython0.21.2
OSposix [linux]
SciPy0.14.1
Tue Jan 13 13:32:19 2015 JST
" ], "metadata": {}, "output_type": "pyout", "prompt_number": 11, "text": [ "" ] } ], "prompt_number": 11 } ], "metadata": {} } ] }