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Examples of how to solve master equations with time-dependent Hamiltonian and/or collapse-operator/Liouvilllian using QuTiP.
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{
"metadata": {
"name": "qutip-example-mesolve-with-time-dependent-liouvillian"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Examples of how to solve master equations with time-dependent operators in QuTiP\n",
"\n",
"Robert Johansson (robert@riken.jp)\n",
"\n",
"https://code.google.com/p/qutip/"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%pylab inline"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Welcome to pylab, a matplotlib-based Python environment [backend: module://IPython.zmq.pylab.backend_inline].\n",
"For more information, type 'help(pylab)'.\n"
]
}
],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from qutip import *"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import time"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def plot_expectation_values(results, ylabels=[], title=None, show_legend=False,\n",
" fig=None, axes=None, figsize=(8, 4)):\n",
"\n",
" if not isinstance(results, list):\n",
" results = [results]\n",
"\n",
" n_e_ops = max([len(result.expect) for result in results])\n",
"\n",
" if not fig or not axes:\n",
" if not figsize:\n",
" figsize = (12, 3 * n_e_ops)\n",
" fig, axes = plt.subplots(n_e_ops, 1, sharex=True,\n",
" figsize=figsize, squeeze=False)\n",
"\n",
" for r_idx, result in enumerate(results):\n",
" for e_idx, e in enumerate(result.expect):\n",
" axes[e_idx, 0].plot(result.times, e,\n",
" label=\"%s [%d]\" % (result.solver, e_idx))\n",
"\n",
" if title:\n",
" axes[0, 0].set_title(title)\n",
"\n",
" axes[n_e_ops - 1, 0].set_xlabel(\"time\", fontsize=12)\n",
" for n in range(n_e_ops):\n",
" if show_legend:\n",
" axes[n, 0].legend()\n",
" if ylabels:\n",
" axes[n, 0].set_ylabel(ylabels[n], fontsize=12)\n",
"\n",
" return fig, axes"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 4
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Time-independent reference problem"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"N = 15\n",
"\n",
"w0 = 1.0 * 2 * pi\n",
"wd = 0.9 * 2 * pi\n",
"A = 0.5 * 2 * pi\n",
"\n",
"a = destroy(N)\n",
"\n",
"H0 = w0 * a.dag() * a\n",
"Hd = A * (a + a.dag())\n",
"Hrwa = (w0-wd) * a.dag() * a + 0.5 * Hd\n",
"\n",
"psi0 = basis(N, 0)\n",
"\n",
"tlist = linspace(0, 50, 500)\n",
"\n",
"args = {'wd': wd}\n",
"\n",
"c_ops = [sqrt(0.1) * a]\n",
"e_ops = [a.dag() * a, a + a.dag()]"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"t0 = time.time()\n",
"result = mesolve(Hrwa, psi0, tlist, c_ops, e_ops)\n",
"t1 = time.time()\n",
"\n",
"print(\"Elapsed: %.2f\" % (t1 - t0))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Elapsed: 0.20\n"
]
}
],
"prompt_number": 6
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plot_expectation_values(result, ylabels = [r'$\\langle a^\\dagger a\\rangle$', r'$\\langle a + a^\\dagger\\rangle$']);"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "display_data",
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EBGDnzp0YOnQoBg8ebPOxevcGdu4E6u6KAkRERLZz6kV1LBQFaNsW2LUL+MMf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\n"
}
],
"prompt_number": 7
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Hamiltonian with time-dependent coefficients"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def Htd(t, args):\n",
" return args[0] + args[1] * cos(args[2] * t)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 8
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"t0 = time.time()\n",
"result = mesolve(Htd, psi0, tlist, c_ops, e_ops, args=[H0, Hd, wd])\n",
"t1 = time.time()\n",
"\n",
"print(\"Elapsed: %.2f\" % (t1 - t0))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Elapsed: 12.14\n"
]
}
],
"prompt_number": 9
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plot_expectation_values(result, ylabels = [r'$\\langle a^\\dagger a\\rangle$', r'$\\langle a + a^\\dagger\\rangle$']);"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"png": 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8OLW1zK6XiUvNmgmRmUnHNm+mVSGRkUa2fmoqJaVdcolx/WvX0ue6dzeO7dxJ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\ntWOhZxiGYRiizok9z9czDMM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ZhnfqZJeVReds394+1FtWRgOgFi2cvU4ZqmzYkJL57BIO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\nAAAAAElFTkSuQmCC\n"
}
],
"prompt_number": 10
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"L0_data = liouvillian_fast(H0, []).data\n",
"Ld_data = liouvillian_fast(Hd, []).data\n",
"\n",
"def Ltd(t, args):\n",
" return L0_data + Ld_data * cos(wd * t)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 11
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"t0 = time.time()\n",
"result = mesolve(Ltd, psi0, tlist, c_ops, e_ops)\n",
"t1 = time.time()\n",
"\n",
"print(\"Elapsed: %.2f\" % (t1 - t0))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Elapsed: 12.15\n"
]
}
],
"prompt_number": 12
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plot_expectation_values(result, ylabels = [r'$\\langle a^\\dagger a\\rangle$', r'$\\langle a + a^\\dagger\\rangle$']);"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"png": 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8OLW1zK6XiUvNmgmRmUnHNm+mVSGRkUa2fmoqJaVdcolx/WvX0ue6dzeO7dxJ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\ntWOhZxiGYRiizok9z9czDMM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ZhnfqZJeVReds394+1FtWRgOgFi2cvU4ZqmzYkJL57BIO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\nwUfsgL9wkRNxoHlmhLNTzykX2dtrCoKJ7HXPir2OgXum7B+rykp9Vsl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T3tdeA/74R0q9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\nAAAAAElFTkSuQmCC\n"
}
],
"prompt_number": 13
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Liouvillian with time-dependent operator\n",
"\n",
"We can use a callback function in-place of the hamiltonian. This callback function should return the Hamiltonian (for unitary problems) and the Liouvillian (for dissipative problems). This callback function can be used to calculate arbitrary Liouvillians, not necessarily on Lindblad form."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"Hdx = A * (a + a.dag())\n",
"Hdp = 1j * A * (a - a.dag())"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 14
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# here we precalculate liouvillians and extraxt the sparse matrix data. not necessary \n",
"# but makes the Ltd function more efficient\n",
"L0_data = liouvillian_fast(H0, c_ops).data\n",
"Ldx_data = liouvillian_fast(Hdx, []).data\n",
"Ldp_data = liouvillian_fast(Hdp, []).data\n",
"\n",
"def Ltd(t, args):\n",
" # this function should return the liouvillian in sparse matrix from. From\n",
" # a Qobj instance we can get the sparse matrix from by using Qobj.data\n",
" \n",
" # example: let the operator that the driving field couple to oscillate \n",
" # here we can calculate the liouvillian however we want\n",
" Ld_data = (Ldx_data * cos(0.25 * wd * t)**2 + Ldp_data * sin(0.25 * wd * t)**2)\n",
" return L0_data + Ld_data * cos(wd * t)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 15
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"t0 = time.time()\n",
"result = mesolve(Ltd, ket2dm(psi0), tlist, [], e_ops)\n",
"t1 = time.time()\n",
"\n",
"print(\"Elapsed: %.2f\" % (t1 - t0))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Elapsed: 20.68\n"
]
}
],
"prompt_number": 16
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plot_expectation_values(result, ylabels = [r'$\\langle a^\\dagger a\\rangle$', r'$\\langle a + a^\\dagger\\rangle$']);"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"png": 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A/x9/nIzae++l593EsLFy771CPPUU/bu0lJ41eW9nzKDnw5QWLdyNluJiir6o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\nQSVE5pC6zIPp29fvievE3uGgaNG2bYHLPgvE99V59gCNo5ISYOxY+TX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6e/gCYw3u556H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\nRK5CYII=\n"
}
],
"prompt_number": 17
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Another example of time-dependent Liouvillian: alternate relaxation/dissipation"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"L0_data = liouvillian(H0, []).data # time-independent hamiltonian\n",
"Ld_data = liouvillian(Hd, []).data # driving part of hamiltonian\n",
"\n",
"L_relax_data = liouvillian(None, [sqrt(0.1) * a]).data # relaxation\n",
"L_excite_data = liouvillian(None, [sqrt(0.1) * a.dag()]).data # excitation\n",
"\n",
"def Ltd(t, args):\n",
" return L0_data + Ld_data * cos(wd * t) + (L_relax_data * cos(0.25 * wd * t)**2 + L_excite_data * sin(0.25 * wd * t)**2)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 18
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"t0 = time.time()\n",
"result = mesolve(Ltd, ket2dm(psi0), tlist, [], e_ops)\n",
"t1 = time.time()\n",
"\n",
"print(\"Elapsed: %.2f\" % (t1 - t0))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Elapsed: 25.10\n"
]
}
],
"prompt_number": 19
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plot_expectation_values(result, ylabels = [r'$\\langle a^\\dagger a\\rangle$', r'$\\langle a + a^\\dagger\\rangle$']);"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"png": 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UKVIEtWrVwvnz551KY+dO4N8kiIiIciWvbtmbO336NPbs2YOmTZtaPD927Ni7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\nNGeOXgK0Fcxfe02Pn3lvkz3x8dra/r/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AAQEBiI2NzeYc+Y7Tp09jz549aNq0KY+zmxkM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\nAAAAAElFTkSuQmCC\n"
}
],
"prompt_number": 20
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Software versions"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from qutip.ipynbtools import version_table; version_table()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<table><tr><th>Software</th><th>Version</th></tr><tr><td>Cython</td><td>0.18</td></tr><tr><td>SciPy</td><td>0.11.0</td></tr><tr><td>QuTiP</td><td>2.2.0</td></tr><tr><td>Python</td><td>2.7.3 (default, Nov 17 2012, 19:54:34) \n",
"[GCC 4.2.1 Compatible Apple Clang 4.1 ((tags/Apple/clang-421.11.66))]</td></tr><tr><td>IPython</td><td>0.13.1</td></tr><tr><td>OS</td><td>posix [darwin]</td></tr><tr><td>Numpy</td><td>1.6.2</td></tr><tr><td>matplotlib</td><td>1.2.0</td></tr><tr><td colspan='2'>Sat Apr 27 12:26:29 2013</td></tr></table>"
],
"output_type": "pyout",
"prompt_number": 21,
"text": [
"<IPython.core.display.HTML at 0x10a408b10>"
]
}
],
"prompt_number": 21
}
],
"metadata": {}
}
]
}
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