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diff --git a/exercise_4-dark_states/.ipynb_checkpoints/stirap-checkpoint.ipynb b/exercise_4-dark_states/.ipynb_checkpoints/stirap-checkpoint.ipynb
new file mode 100644
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@@ -0,0 +1,135 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Quantum Optics 2 – Exercise 4 – Dark states\n",
+ "\n",
+ "## Stimulated Raman adiabatic passage\n",
+ "\n",
+ "![](\"3level.png\")
\n",
+ "The total Hamiltonian is\n",
+ "$$H = -\\hbar\\bigl(\\omega_{01}|g_1\\rangle\\langle g_1|+\\omega_{02}|g_2\\rangle\\langle g_2|\\bigr)+\\frac{\\hbar\\Omega_1}{2}\\bigl(\\sigma_1e^{i\\omega_1t}+\\sigma_1^\\dagger e^{-i\\omega_1t}\\bigr)+\\frac{\\hbar\\Omega_2}{2}\\bigl(\\sigma_2e^{i\\omega_2t}+\\sigma_2^\\dagger e^{-i\\omega_2t}\\bigr).$$\n",
+ "We apply two pulses with frequencies of $\\omega_1$ and $\\omega_2$ with a Gaussian shape\n",
+ "$$\\Omega_{1,2}(t)=\\Omega_{1,2}\\exp\\biggl(-\\biggl(\\frac{t-t_{1,2}}{T}\\biggr)^2\\biggr).$$"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from qutip import *\n",
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt\n",
+ "%matplotlib inline"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "\"\"\" States \"\"\"\n",
+ "e = basis(3,0)\n",
+ "g1 = basis(3,1)\n",
+ "g2 = basis(3,2)\n",
+ "n_e = e*e.dag()\n",
+ "n_g1 = g1*g1.dag()\n",
+ "n_g2 = g2*g2.dag()\n",
+ "\n",
+ "\"\"\" Energy levels \"\"\"\n",
+ "ωe = 0* 2*np.pi\n",
+ "ωg1 = -1*2*np.pi\n",
+ "ωg2 = -0.8*2*np.pi\n",
+ "\n",
+ "\"\"\" Laser frequencies and detuning \"\"\"\n",
+ "Δ = 0.1* 2*np.pi\n",
+ "ω1 = ωe-ωg1-Δ\n",
+ "ω2 = ωe-ωg2-Δ\n",
+ "\n",
+ "\"\"\" Rabi frequencies \"\"\"\n",
+ "Ω1 = 1e6* 2*np.pi\n",
+ "Ω2 = 1e6* 2*np.pi\n",
+ "θ = np.arctan(Ω2/Ω1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "\"\"\" Pulse parameters \"\"\"\n",
+ "\"\"\" Tweak these parameters to achieve the adiabaticity condition \"\"\"\n",
+ "Δt = 0\n",
+ "T = 1e-6\n",
+ "\n",
+ "def Gauss_1(t):\n",
+ " return np.exp(-(t+Δt)**2/T**2)\n",
+ "def Gauss_2(t): \n",
+ " return np.exp(-(t-Δt)**2/T**2)\n",
+ "def Omega_1(t,args):\n",
+ " return Gauss_1(t)*Ω1/2.0*np.exp(-1j*ω1*t)\n",
+ "def Omega_1_dag(t,args):\n",
+ " return Gauss_1(t)*Ω1/2.0*np.exp(1j*ω1*t)\n",
+ "def Omega_2(t,args):\n",
+ " return Gauss_2(t)*Ω2/2.0*np.exp(-1j*ω2*t)\n",
+ "def Omega_2_dag(t,args):\n",
+ " return Gauss_2(t)*Ω2/2.0*np.exp(1j*ω2*t)\n",
+ "\n",
+ "H0 = ωg1*n_g1+ωg2*n_g2+ωe*n_e\n",
+ "H=[H0,[e*g1.dag(),Omega_1],[g1*e.dag(),Omega_1_dag],[e*g2.dag(),Omega_2],[g2*e.dag(),Omega_2_dag]]\n",
+ "psi0 = g1\n",
+ "\n",
+ "t=np.linspace(-5*T,5*T,1000)\n",
+ "pulse1 = Ω1/(2*np.pi)*Gauss_1(t)\n",
+ "pulse2 = Ω2/(2*np.pi)*Gauss_2(t)\n",
+ "result = mesolve(H,psi0,t,[],[n_g1, n_g2, n_e])\n",
+ "\n",
+ "fig,ax = plt.subplots(2,1,figsize=(10,10),sharex=True,gridspec_kw=dict(hspace=0.1))\n",
+ "ax[0].plot(t,pulse1,label='ω1',lw=3.0)\n",
+ "ax[0].plot(t,pulse2,label='ω2',lw=3.0)\n",
+ "ax[1].plot(t,result.expect[0],label='N_g1',lw=3.0)\n",
+ "ax[1].plot(t,result.expect[1],label='N_g2',lw=3.0)\n",
+ "ax[1].plot(t,result.expect[2],label='N_e',lw=3.0)\n",
+ "ax[0].legend(fontsize=14)\n",
+ "ax[1].legend(fontsize=14)\n",
+ "ax[0].set_ylabel('Pulse ntensity',fontsize=16)\n",
+ "ax[1].set_ylabel('Population',fontsize=16)\n",
+ "ax[1].set_xlabel('Time (s)',fontsize=16);"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "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": "3.7.2"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/exercise_4-dark_states/.ipynb_checkpoints/stirap-solution-checkpoint.ipynb b/exercise_4-dark_states/.ipynb_checkpoints/stirap-solution-checkpoint.ipynb
new file mode 100644
index 0000000..d5369a6
--- /dev/null
+++ b/exercise_4-dark_states/.ipynb_checkpoints/stirap-solution-checkpoint.ipynb
@@ -0,0 +1,148 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Quantum Optics 2 – Exercise 4 – Dark states\n",
+ "\n",
+ "## Stimulated Raman adiabatic passage\n",
+ "\n",
+ "\n",
+ "The total Hamiltonian is\n",
+ "$$H = -\\hbar\\bigl(\\omega_{01}|g_1\\rangle\\langle g_1|+\\omega_{02}|g_2\\rangle\\langle g_2|\\bigr)+\\frac{\\hbar\\Omega_1}{2}\\bigl(\\sigma_1e^{i\\omega_1t}+\\sigma_1^\\dagger e^{-i\\omega_1t}\\bigr)+\\frac{\\hbar\\Omega_2}{2}\\bigl(\\sigma_2e^{i\\omega_2t}+\\sigma_2^\\dagger e^{-i\\omega_2t}\\bigr).$$\n",
+ "We apply two pulses with frequencies of $\\omega_1$ and $\\omega_2$ with a Gaussian shape\n",
+ "$$\\Omega_{1,2}(t)=\\Omega_{1,2}\\exp\\biggl(-\\biggl(\\frac{t-t_{1,2}}{T}\\biggr)^2\\biggr).$$"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from qutip import *\n",
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt\n",
+ "%matplotlib inline"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "\"\"\" States \"\"\"\n",
+ "e = basis(3,0)\n",
+ "g1 = basis(3,1)\n",
+ "g2 = basis(3,2)\n",
+ "n_e = e*e.dag()\n",
+ "n_g1 = g1*g1.dag()\n",
+ "n_g2 = g2*g2.dag()\n",
+ "\n",
+ "\"\"\" Energy levels \"\"\"\n",
+ "ωe = 0* 2*np.pi\n",
+ "ωg1 = -1*2*np.pi\n",
+ "ωg2 = -0.8*2*np.pi\n",
+ "\n",
+ "\"\"\" Laser frequencies and detuning \"\"\"\n",
+ "Δ = 0.1* 2*np.pi\n",
+ "ω1 = ωe-ωg1-Δ\n",
+ "ω2 = ωe-ωg2-Δ\n",
+ "\n",
+ "\"\"\" Rabi frequencies \"\"\"\n",
+ "Ω1 = 1e6* 2*np.pi\n",
+ "Ω2 = 1e6* 2*np.pi\n",
+ "θ = np.arctan(Ω2/Ω1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "\"\"\" Pulse parameters \"\"\"\n",
+ "\"\"\" Tweak these parameters to achieve the adiabaticity condition \"\"\"\n",
+ "Δt = 1e-6\n",
+ "T = 1e-6\n",
+ "\n",
+ "def Gauss_1(t):\n",
+ " return np.exp(-(t+Δt)**2/T**2)\n",
+ "def Gauss_2(t): \n",
+ " return np.exp(-(t-Δt)**2/T**2)\n",
+ "def Omega_1(t,args):\n",
+ " return Gauss_1(t)*Ω1/2.0*np.exp(-1j*ω1*t)\n",
+ "def Omega_1_dag(t,args):\n",
+ " return Gauss_1(t)*Ω1/2.0*np.exp(1j*ω1*t)\n",
+ "def Omega_2(t,args):\n",
+ " return Gauss_2(t)*Ω2/2.0*np.exp(-1j*ω2*t)\n",
+ "def Omega_2_dag(t,args):\n",
+ " return Gauss_2(t)*Ω2/2.0*np.exp(1j*ω2*t)\n",
+ "\n",
+ "H0 = ωg1*n_g1+ωg2*n_g2+ωe*n_e\n",
+ "H=[H0,[e*g1.dag(),Omega_1],[g1*e.dag(),Omega_1_dag],[e*g2.dag(),Omega_2],[g2*e.dag(),Omega_2_dag]]\n",
+ "psi0 = g1\n",
+ "\n",
+ "t=np.linspace(-5*T,5*T,1000)\n",
+ "pulse1 = Ω1/(2*np.pi)*Gauss_1(t)\n",
+ "pulse2 = Ω2/(2*np.pi)*Gauss_2(t)\n",
+ "result = mesolve(H,psi0,t,[],[n_g1, n_g2, n_e])\n",
+ "\n",
+ "fig,ax = plt.subplots(2,1,figsize=(10,10),sharex=True,gridspec_kw=dict(hspace=0.1))\n",
+ "ax[0].plot(t,pulse1,label='ω1',lw=3.0)\n",
+ "ax[0].plot(t,pulse2,label='ω2',lw=3.0)\n",
+ "ax[1].plot(t,result.expect[0],label='N_g1',lw=3.0)\n",
+ "ax[1].plot(t,result.expect[1],label='N_g2',lw=3.0)\n",
+ "ax[1].plot(t,result.expect[2],label='N_e',lw=3.0)\n",
+ "ax[0].legend(fontsize=14)\n",
+ "ax[1].legend(fontsize=14)\n",
+ "ax[0].set_ylabel('Pulse ntensity',fontsize=16)\n",
+ "ax[1].set_ylabel('Population',fontsize=16)\n",
+ "ax[1].set_xlabel('Time (s)',fontsize=16);"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "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": "3.7.2"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/exercise_4-dark_states/3level.png b/exercise_4-dark_states/3level.png
new file mode 100644
index 0000000..afdca46
Binary files /dev/null and b/exercise_4-dark_states/3level.png differ
diff --git a/exercise_4-dark_states/stirap.ipynb b/exercise_4-dark_states/stirap.ipynb
new file mode 100644
index 0000000..637f108
--- /dev/null
+++ b/exercise_4-dark_states/stirap.ipynb
@@ -0,0 +1,135 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Quantum Optics 2 – Exercise 4 – Dark states\n",
+ "\n",
+ "## Stimulated Raman adiabatic passage\n",
+ "\n",
+ "\n",
+ "The total Hamiltonian is\n",
+ "$$H = -\\hbar\\bigl(\\omega_{01}|g_1\\rangle\\langle g_1|+\\omega_{02}|g_2\\rangle\\langle g_2|\\bigr)+\\frac{\\hbar\\Omega_1}{2}\\bigl(\\sigma_1e^{i\\omega_1t}+\\sigma_1^\\dagger e^{-i\\omega_1t}\\bigr)+\\frac{\\hbar\\Omega_2}{2}\\bigl(\\sigma_2e^{i\\omega_2t}+\\sigma_2^\\dagger e^{-i\\omega_2t}\\bigr).$$\n",
+ "We apply two pulses with frequencies of $\\omega_1$ and $\\omega_2$ with a Gaussian shape\n",
+ "$$\\Omega_{1,2}(t)=\\Omega_{1,2}\\exp\\biggl(-\\biggl(\\frac{t-t_{1,2}}{T}\\biggr)^2\\biggr).$$"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from qutip import *\n",
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt\n",
+ "%matplotlib inline"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "\"\"\" States \"\"\"\n",
+ "e = basis(3,0)\n",
+ "g1 = basis(3,1)\n",
+ "g2 = basis(3,2)\n",
+ "n_e = e*e.dag()\n",
+ "n_g1 = g1*g1.dag()\n",
+ "n_g2 = g2*g2.dag()\n",
+ "\n",
+ "\"\"\" Energy levels \"\"\"\n",
+ "ωe = 0* 2*np.pi\n",
+ "ωg1 = -1*2*np.pi\n",
+ "ωg2 = -0.8*2*np.pi\n",
+ "\n",
+ "\"\"\" Laser frequencies and detuning \"\"\"\n",
+ "Δ = 0.1* 2*np.pi\n",
+ "ω1 = ωe-ωg1-Δ\n",
+ "ω2 = ωe-ωg2-Δ\n",
+ "\n",
+ "\"\"\" Rabi frequencies \"\"\"\n",
+ "Ω1 = 1e6* 2*np.pi\n",
+ "Ω2 = 1e6* 2*np.pi\n",
+ "θ = np.arctan(Ω2/Ω1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "\"\"\" Pulse parameters \"\"\"\n",
+ "\"\"\" Tweak these parameters to achieve the adiabaticity condition \"\"\"\n",
+ "Δt = 0\n",
+ "T = 1e-6\n",
+ "\n",
+ "def Gauss_1(t):\n",
+ " return np.exp(-(t+Δt)**2/T**2)\n",
+ "def Gauss_2(t): \n",
+ " return np.exp(-(t-Δt)**2/T**2)\n",
+ "def Omega_1(t,args):\n",
+ " return Gauss_1(t)*Ω1/2.0*np.exp(-1j*ω1*t)\n",
+ "def Omega_1_dag(t,args):\n",
+ " return Gauss_1(t)*Ω1/2.0*np.exp(1j*ω1*t)\n",
+ "def Omega_2(t,args):\n",
+ " return Gauss_2(t)*Ω2/2.0*np.exp(-1j*ω2*t)\n",
+ "def Omega_2_dag(t,args):\n",
+ " return Gauss_2(t)*Ω2/2.0*np.exp(1j*ω2*t)\n",
+ "\n",
+ "H0 = ωg1*n_g1+ωg2*n_g2+ωe*n_e\n",
+ "H=[H0,[e*g1.dag(),Omega_1],[g1*e.dag(),Omega_1_dag],[e*g2.dag(),Omega_2],[g2*e.dag(),Omega_2_dag]]\n",
+ "psi0 = g1\n",
+ "\n",
+ "t=np.linspace(-5*T,5*T,1000)\n",
+ "pulse1 = Ω1/(2*np.pi)*Gauss_1(t)\n",
+ "pulse2 = Ω2/(2*np.pi)*Gauss_2(t)\n",
+ "result = mesolve(H,psi0,t,[],[n_g1, n_g2, n_e])\n",
+ "\n",
+ "fig,ax = plt.subplots(2,1,figsize=(10,10),sharex=True,gridspec_kw=dict(hspace=0.1))\n",
+ "ax[0].plot(t,pulse1,label='ω1',lw=3.0)\n",
+ "ax[0].plot(t,pulse2,label='ω2',lw=3.0)\n",
+ "ax[1].plot(t,result.expect[0],label='N_g1',lw=3.0)\n",
+ "ax[1].plot(t,result.expect[1],label='N_g2',lw=3.0)\n",
+ "ax[1].plot(t,result.expect[2],label='N_e',lw=3.0)\n",
+ "ax[0].legend(fontsize=14)\n",
+ "ax[1].legend(fontsize=14)\n",
+ "ax[0].set_ylabel('Pulse ntensity',fontsize=16)\n",
+ "ax[1].set_ylabel('Population',fontsize=16)\n",
+ "ax[1].set_xlabel('Time (s)',fontsize=16);"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "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": "3.7.2"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/exercise_5-atom-cavity/exercise5.ipynb b/exercise_5-atom-cavity/exercise5.ipynb
new file mode 100644
index 0000000..1d67ec8
--- /dev/null
+++ b/exercise_5-atom-cavity/exercise5.ipynb
@@ -0,0 +1,178 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# %matplotlib widget\n",
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt\n",
+ "import qutip as qt"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Section 2 : Spectrum of atom-cavity system"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def low_sat_I_c(I_d,C,Δ,κ,Γ) :\n",
+ " n_0 = ##### FILL IN\n",
+ " δ_at = ##### FILL IN\n",
+ " δ_c = ##### FILL IN\n",
+ " return ##### FILL IN"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "Δ = np.linspace(-10,10,1000) #detuning of the incident field on the cavity, Δ=Δ_c=Δ_at\n",
+ "κ = 1.\n",
+ "Γ= 1. #For 1st figure, then modify κ and Γ as you wish\n",
+ "\n",
+ "plt.close('spectrum')\n",
+ "plt.figure('spectrum')\n",
+ "for C in np.arange(0.1,2.01,0.1) :\n",
+ " plt.plot(Δ,low_sat_I_c(1,C,Δ,κ,Γ))\n",
+ " plt.xlim(-10,10)\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Section 3 : vacuum Rabi oscillations\n",
+ "\n",
+ "Let's do this one in SI units !"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "\"\"\"\n",
+ "Define the experimental parameters\n",
+ "\"\"\"\n",
+ "π=np.pi\n",
+ "\n",
+ "Δc = 2*π *0. # detuning pump-cavity\n",
+ "Δat = 2*π *0. # detuning pump-atom\n",
+ "\n",
+ "g = 2*π * 2.56e6 # coupling constant\n",
+ "κ = 2*π*125e3 # cavity dissipation rate\n",
+ "Γ = 2*π* 6e6 # atom dissipation rate\n",
+ "\n",
+ "N = 15 # number of cavity fock states\n",
+ "\n",
+ "\"\"\"\n",
+ "Define some useful operators\n",
+ "\"\"\"\n",
+ "a = qt.tensor(qt.destroy(N), qt.qeye(2)) #cavity annihilation\n",
+ "sm = qt.tensor(qt.qeye(N), qt.destroy(2)) #atom σ-\n",
+ "\n",
+ "\"\"\"\n",
+ "Define the different components of the Hamiltonian and the Linblad operators\n",
+ "\"\"\"\n",
+ "# Hamiltonian\n",
+ "H_cav = Δc * a.dag() * a\n",
+ "H_at = ##### FILL IN\n",
+ "H_int = g * ( ) ##### FILL IN\n",
+ "H_tot = H_cav + H_at + H_int\n",
+ "\n",
+ "#Linblad operators\n",
+ "L_1 = np.sqrt(κ)*a\n",
+ "L_2 = ##### FILL IN\n",
+ "\n",
+ "L_ops = [L_1,L_2]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "\"\"\"\n",
+ "Choose the initial state of the system and define the times at which you want the result of evolution\n",
+ "\"\"\"\n",
+ "psi0 = qt.tensor(qt.basis(N,0), qt.basis(2,1)) # start with an excited atom and no photons\n",
+ "\n",
+ "tlist = np.linspace(0,0.5e-6,1001)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "\"\"\"\n",
+ "Use the mesolve function (master equation solver) which takes as arguments\n",
+ "the Hamiltonian, the initial state, the time list, the Linblad operators, optionally the operators you want the expectation values of.\n",
+ "\"\"\"\n",
+ "output = qt.mesolve(H_tot, psi0, tlist, L_ops, [a.dag() * a, sm.dag() * sm])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "\"\"\"\n",
+ "Extract and plot the results\n",
+ "\"\"\"\n",
+ "n_c = output.expect[0] # number of photons in the cavity\n",
+ "n_a = output.expect[1] # occupation of the atomic excited state\n",
+ "\n",
+ "plt.close('Vacuum oscill')\n",
+ "fig, axes = plt.subplots(1, 1, figsize=(10,6),num='Vacuum oscill')\n",
+ "\n",
+ "axes.plot(tlist*1e6, n_c, label=\"Cavity\")\n",
+ "axes.plot(tlist*1e6, n_a, label=\"Atomic excited state\")\n",
+ "# axes.plot(tlist*1e6,np.exp(-(Γ+κ)*tlist/2.), label=r'envelope $e^{-(\\Gamma+\\kappa)t/2}$')\n",
+ "axes.legend(loc='upper right')\n",
+ "axes.set_xlabel('Time (μs)')\n",
+ "axes.set_ylabel('Occupation probability')\n",
+ "axes.set_title('Vacuum Rabi oscillations')\n",
+ "plt.show()"
+ ]
+ }
+ ],
+ "metadata": {
+ "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": "3.7.6"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/requirements.txt b/requirements.txt
index e558052..4fc4fec 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,4 +1,5 @@
numpy
matplotlib
scipy
-plotly
\ No newline at end of file
+plotly
+qutip
\ No newline at end of file