diff --git a/awards/teach_me_qiskit_2018/exact_ising_model_simulation/Ising_time_evolution.ipynb b/awards/teach_me_qiskit_2018/exact_ising_model_simulation/Ising_time_evolution.ipynb
index a921a5f..1f3d68e 100644
--- a/awards/teach_me_qiskit_2018/exact_ising_model_simulation/Ising_time_evolution.ipynb
+++ b/awards/teach_me_qiskit_2018/exact_ising_model_simulation/Ising_time_evolution.ipynb
@@ -93,7 +93,40 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 165,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<h3>Version Information</h3><table><tr><th>Qiskit Software</th><th>Version</th></tr><tr><td>Qiskit</td><td>0.19.6</td></tr><tr><td>Terra</td><td>0.14.2</td></tr><tr><td>Aer</td><td>0.5.2</td></tr><tr><td>Ignis</td><td>0.3.3</td></tr><tr><td>Aqua</td><td>0.7.3</td></tr><tr><td>IBM Q Provider</td><td>0.7.2</td></tr><tr><th>System information</th></tr><tr><td>Python</td><td>3.7.6 (default, Jan  8 2020, 20:23:39) [MSC v.1916 64 bit (AMD64)]</td></tr><tr><td>OS</td><td>Windows</td></tr><tr><td>CPUs</td><td>2</td></tr><tr><td>Memory (Gb)</td><td>7.886066436767578</td></tr><tr><td colspan='2'>Wed Aug 12 16:55:27 2020 Hora de Verão de GMT</td></tr></table>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Import dependencies\n",
+    "from matplotlib import pyplot as plt\n",
+    "import numpy as np\n",
+    "from qiskit import Aer\n",
+    "from qiskit import *\n",
+    "import qiskit.tools.jupyter\n",
+    "from mpl_toolkits.axes_grid1 import make_axes_locatable\n",
+    "from qiskit.visualization import *\n",
+    "from qiskit.ignis.mitigation.measurement import complete_meas_cal,CompleteMeasFitter\n",
+    "\n",
+    "%matplotlib inline\n",
+    "%qiskit_version_table"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -155,28 +188,113 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 539.392x144.48 with 1 Axes>"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def fswap():\n",
+    "    circuit = QuantumCircuit(2, name=\"FSWAP\")\n",
+    "    circuit.cx(0, 1)\n",
+    "    circuit.cx(1, 0)\n",
+    "    circuit.cx(0, 1)\n",
+    "    circuit.h(1)\n",
+    "    circuit.cx(0, 1)\n",
+    "    circuit.h(1)\n",
+    "\n",
+    "    return circuit\n",
+    "fswap_circ=fswap()\n",
+    "fswap_circ.draw(output=\"mpl\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "But how do we know that this is in fact the circuit we want? One way is to make all the calculations by hand, the other way is to use Qiskit's `unitary_simulator`. But first we also define a function to help us visualize things:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# CZ (Controlled-Z)\n",
-    "# control qubit: q0\n",
-    "# target qubit: q1\n",
-    "def CZ(qp,q0,q1):\n",
-    "    qp.h(q1)\n",
-    "    qp.cx(q0,q1)\n",
-    "    qp.h(q1)\n",
-    "# f-SWAP\n",
-    "# taking into account the one-directionality of CNOT gates in the available devices\n",
-    "def fSWAP(qp,q0,q1):\n",
-    "    qp.cx(q0,q1)\n",
-    "    qp.h(q0)\n",
-    "    qp.h(q1)\n",
-    "    qp.cx(q0,q1)\n",
-    "    qp.h(q0)\n",
-    "    qp.h(q1)\n",
-    "    qp.cx(q0,q1)\n",
-    "    CZ(qp,q0,q1)"
+    "def get_plot(circ):\n",
+    "    simulator=Aer.get_backend(\"unitary_simulator\")\n",
+    "    unitary=execute(circ,simulator).result().get_unitary()\n",
+    "    unitary_real=np.real(unitary)\n",
+    "    unitary_imag=np.imag(unitary)\n",
+    "\n",
+    "    unitary_real[np.where(np.abs(unitary_real)<1e-10)]=0\n",
+    "    unitary_imag[np.where(np.abs(unitary_imag)<1e-10)]=0\n",
+    "\n",
+    "    fig=plt.figure(figsize=(12,8))\n",
+    "    ax1=fig.add_subplot(121)\n",
+    "    ax2=fig.add_subplot(122)\n",
+    "\n",
+    "    im1=ax1.imshow(unitary_real, cmap=\"Blues\", vmin=-1,vmax=1)\n",
+    "    im2=ax2.imshow(unitary_imag,cmap=\"Blues\",vmin=-1,vmax=1)\n",
+    "    \n",
+    "    divider1 = make_axes_locatable(ax1)\n",
+    "    divider2 = make_axes_locatable(ax2)\n",
+    "    cax1 = divider1.append_axes('right', size='5%', pad=0.05)\n",
+    "    cax2 = divider2.append_axes('right', size='5%', pad=0.05)\n",
+    "    \n",
+    "    fig.colorbar(im1, cax=cax1, orientation='vertical')\n",
+    "    fig.colorbar(im2, cax=cax2, orientation='vertical')\n",
+    "\n",
+    "\n",
+    "    for (j,i),label in np.ndenumerate(unitary_real):\n",
+    "        try:\n",
+    "            if label<0:\n",
+    "                ax1.text(i,j,float(str(label)[:6]),ha='center',va='center')\n",
+    "            else:\n",
+    "                ax1.text(i,j,float(str(label)[:5]),ha='center',va='center')\n",
+    "        except:\n",
+    "            ax1.text(i,j,label,ha='center',va='center')\n",
+    "\n",
+    "    for (j,i),label in np.ndenumerate(unitary_imag):\n",
+    "        try:\n",
+    "            ax2.text(i,j,float(str(label)[:5]),ha='center',va='center')\n",
+    "        except:\n",
+    "            ax2.text(i,j,label,ha='center',va='center')\n",
+    "            \n",
+    "    plt.tight_layout()\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x576 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "get_plot(fswap_circ)"
    ]
   },
   {
@@ -191,7 +309,7 @@
     "b_{k}&=&\\frac{1}{\\sqrt{n}}\\sum_{j=1}^{n}e^{i\\frac{2\\pi}{n}jk}c_{j}, \\qquad b_{k}^{\\dagger}&=&\\frac{1}{\\sqrt{n}}\\sum_{j=1}^{n}e^{-i\\frac{2\\pi}{n}jk}c_{j}^{\\dagger}, \\qquad k=-\\frac{n}{2}+1,\\cdots,\\frac{n}{2}.\n",
     "\\end{eqnarray}$$\n",
     "\n",
-    "If $n=2^m$ for some integer $m$, it is possible to decompose the Fourier transform into two parallel Fourier transforms between the even and odd sites [[6](https://arxiv.org/abs/1310.7605)]:\n",
+    "If $n=2^m$ for some integer $m$, it is possible to decompose the Fourier transform into two parallel Fourier transforms between the even and odd sites [[6](https://arxiv.org/abs/1310.7605)]. In fact a nice way to look at it (as sugested by [[6](https://arxiv.org/abs/1310.7605)]) is to first recall the classical algorithm for the fast fourier transform, first discovered by Carl Friedrich Gauss in 1805, and later popularized by  James Cooley of IBM and John Tukey of Princeton on a paper published in 1965 reinventing the algorithm and describing how to perform it conveniently on a computer, where we break down the whole fourier transform in terms of 2 qubit gates and add a so-called twiddle factor with a $Z$ rotation. We can then take a closer look:\n",
     "\n",
     "$$ \\frac{1}{\\sqrt{n}}\\sum_{j=0}^{n-1}e^{i\\frac{2\\pi}{n}jk}c_{j} = \\frac{1}{\\sqrt{n/2}}\\sum_{j'=0}^{n/2-1}e^{i\\frac{2\\pi}{n/2}j'k}c_{2j'} + \\frac{e^{i\\frac{2\\pi k}{n}}}{\\sqrt{n/2}}\\sum_{j'=0}^{n/2-1}e^{i\\frac{2\\pi}{n/2}j'k}c_{2j'+1}. $$\n",
     "\n",
@@ -226,36 +344,86 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 900.592x144.48 with 1 Axes>"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def F():\n",
+    "    \"\"\"Here we define the 2qubit fermionic fourier transform\"\"\"\n",
+    "    circuit = QuantumCircuit(2, name=\"F\")\n",
+    "    circuit.cx(1, 0)\n",
+    "    circuit.sdg(1)\n",
+    "    circuit.h(1)\n",
+    "    circuit.tdg(1)\n",
+    "    circuit.cx(0, 1)\n",
+    "    circuit.t(1)\n",
+    "    circuit.h(1)\n",
+    "    circuit.s(1)\n",
+    "    circuit.cx(1, 0)\n",
+    "    circuit.h(0)\n",
+    "    circuit.cx(1, 0)\n",
+    "    circuit.h(0)\n",
+    "\n",
+    "    return circuit\n",
+    "F().draw(output=\"mpl\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x576 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "get_plot(F()) #For the F0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x576 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# CH (Controlled-Haddamard)\n",
-    "# control qubit: q1\n",
-    "# target qubit: q0\n",
-    "def CH2(qp,q0,q1):\n",
-    "    qp.sdg(q0)\n",
-    "    qp.h(q0)\n",
-    "    qp.tdg(q0)\n",
-    "    qp.h(q0)\n",
-    "    qp.h(q1)\n",
-    "    qp.cx(q0,q1)\n",
-    "    qp.h(q0)\n",
-    "    qp.h(q1)\n",
-    "    qp.t(q0)\n",
-    "    qp.h(q0)\n",
-    "    qp.s(q0)\n",
-    "# Fourier transform gates\n",
-    "def F2(qp,q0,q1):\n",
-    "    qp.cx(q0,q1)\n",
-    "    CH2(qp,q0,q1)\n",
-    "    qp.cx(q0,q1)\n",
-    "    CZ(qp,q0,q1) \n",
-    "def F0(qp,q0,q1):\n",
-    "    F2(qp,q0,q1)   \n",
-    "def F1(qp,q0,q1):\n",
-    "    F2(qp,q0,q1)\n",
-    "    qp.sdg(q0)"
+    "qc=F()\n",
+    "qc.sdg(1)\n",
+    "get_plot(qc)"
    ]
   },
   {
@@ -298,37 +466,66 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAArYAAAB7CAYAAACB6sOvAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy86wFpkAAAACXBIWXMAAAsTAAALEwEAmpwYAAAb8klEQVR4nO3deXhUVZrH8W9VdhKWQFgChDVhNQECKIiKiCiCMq5g1O5RRlHE7tFubHt0aFFGdNzGZWz3aWgbUMENbVDZZHEBwiJhM0ASEjDsEJKQvWr+uCwpyFJJqupW3fw+z5PnCefenPtW1eGe994651yb0+l0IiIiIiIS4OxmByAiIiIi4glKbEVERETEEpTYioiIiIglKLEVEREREUtQYisiIiIilqDEVkREREQsQYmtiIiIiFiCElsRERERsQQltiIiIiJiCUpsRURERMQSlNiKiIiIiCUosRURERERS1BiKyIiIiKWoMRWRERERCxBia2IiIiIWIISWxERERGxBCW2IiIiImIJSmxFRERExBKU2IqIiIiIJSixFRERERFLUGIrIiIiIpagxFZERERELEGJrYiIiIhYghJbEREREbEEJbYiIiIiYglKbEVERETEEpTYioiIiIglKLEVEREREUtQYisiIiIilqDEVkREREQsIdjsAEQ8qbQcDuRBSRkE2aFNM4gKNzsqERGxgtJyOJgHxaf7mJim0CzC7KikMiW2EvAKS2BdBqzPgNw8cDpdt7doAklxMCwB2jY3J0YREQlMRaWwPtPoZ349Do7z+phmEXBRR7gsAdpHmxOjnGNzOs9PA0QCg8MJ36fDl5uNq2h3DOkO/5IMEaFeDU1ERAKc02kks59tMO7QuiO5M9wyGCLDvBubVE+JrQSk4jKYtRp25tb9b6ObwKQRENvC42GJiIgFlFXAB9/Dlpy6/23TcLjvSujUyuNhiRuU2ErAKS2Ht5ZDxuH61xEZBr8fpaEJIiLiqrwC3ltZvxsnZ4SHwJSrIa6l5+IS92hVBAk4X22uPal95U7jpzqFJTBrjXECExEROePbrbUntbX1MWe+VSxxc5iceI4SWwkoGYdg1S+eqSv3BCzZ5pm6RLyttBzyi4yvSEXOV+GA/GJjopPU3/7jsNRD/cLRAvjnZs/UJe7TqgiVOBwOXn75Zd5++21ycnLo2bMnr732GpMmTWL48OG88847ZofY6HnqhHPGyp1wVR8I0/8E8VPZR2HZNtiyz5jMEmyH5C4wso+G0ojx7dN3O+CH3cbvAF1i4Mre0L+TubEFouXbL1z1oCF+2AXXJmoymS/pjm0lEydOZMaMGdx///0sXryY8ePHk5KSQkZGBgMHDjQ7vEbvaAHs+NWzdRaXwcYsz9Yp4ilb98Er35xLagHKHcbSdi99DZkNGGcugS+/2GgfS7adS2oB9h41vgbX3cK6KSiGzdmerbPcAWv3eLZOqZkS29Pmzp3L7NmzWbhwIVOnTmXEiBE88cQTDB06lPLy8rOJ7cGDB7nmmmto0qQJ/fr1Y9OmTSZH3njsPgjemOmYfsALlYo0UGEJzF5jJLTnT/F1YgxJ+L9VGifemM1fB0fyLyw/016WbINfGjABqrHZc8gY0uFp6mN8S4ntac8++yyjR49m+PDhLuXx8fGEhISQmJgIwOTJk+nVqxdHjx5lypQp3HrrrVRUqGfxhX3HvFNvzlHv1CvSEOsyjOS1uos5p9O4Y5e2z6dhiZ84XghpOTVf7NtsnpuT0Bh4q4/Zd+zCi1PxHi33Bezbt4+4uDjef/99Jk6c6LItJSWFnTt3smnTJvLz84mJiWH//v3ExMQA0KVLF+bMmcOwYcM8EovNZvNIPVY09uFPiR90k0tZTbNSa/LwnHO/l5Wc4q//FtmAyEQ8b9wfv6Rzv+uw24Oq3cfhqGDr8ndYMetBH0Ym/qDn0BRGT5lb636lxfm8eW8zH0QU+EZN+ht9rrjbpcwTfQzA6/8agqNCSyQ0hLvpqu7YYiS2AO3atXMpLyoqYuXKlWeHIezatYtWrVqdTWoBEhMT2b59u++CbcRseCfpt9n030D8j80eVHuLdzqx1ZD4inW5+7nbbGofbvPijSX1M76jueBwNlFNT09nzJgxZ8uff/55cnNzSU5OBqCwsJBmzVyvfJs1a0ZBQYHHYtEN9Op9tBZ+3O1adv5V8RlnrrKr215ZTItwve/idxb9bKynWRN7UDBP/3kSy96f5JugxG8cyIPnvqp5HxvQs2MTnd/c9MVGWLHDtcwTfUx4CJSVlngzb5ZKlNgC3bp1IykpiZkzZ9KyZUs6dOjAggULWLRoEcDZO7aRkZHk57uO1D958iRRUVE+j7kx6hDtnXo76skw4oeGxsOSrdWPobQBocEwqIsPgxK/0a45dGsNmUeqH7/pBC7r4dOwAlpHL/YxSmp9R/fGAbvdzvz58+nbty+TJ0/mnnvuISYmhilTphAcHExSUhIACQkJHDlyhKNHz8022rp1K3369DEr9Eale5vAqlekIaIj4aZBxu/n94ln/p0yFMJCfBmV+JMJl0BEyIXt44z+naB/Z5+GFNC6tan+vWxQva29UKlUS4ntaT169GDFihUUFhaSnZ3NjBkzSEtLo3fv3kRERADQtGlTxo4dy4wZMyguLua9997DZrMxZMgQk6NvHGJbQNeYWnerk2A7DO7q2TpFPOWKnnD3ZRc+iKFTK3jgKi3A39i1bQ6PXAuJca53BKPCYEw/+M0wsOtOoduiI6FXe8/WaQOGdPdsnVIzDUWoQWpq6gVJ65tvvsldd91FdHQ0CQkJfPLJJwQFaXC+r4zoA5mrPFffxd0gKtxz9Yl4Wv/O0K8TPHJ6AvzjN0AbTXKX01o3g4lXQF4RPPmpUfbUzRCk21b1MqK3Zx8E1L8ztNRoRZ9S069GQUEB6enpZyeOndG2bVuWLFlCUVERW7ZsYcCAASZF2DgldoR+cZ6pq3kEXK+PTwJA5btxSmqlKs0jzv2upLb+erSDSzx0hzUyFG7WQ0t9TndsqxEVFaUHL/ghmw1uuxh+PQGHq3jizhm1zVQNtsNvh0GTUI+GJyIiAe7GZOPBPb+eqH6f2voYuw3uvBSaRtS8n3ierusk4ESFw5SroW0971yFBsO9V0L3th4NS0RELCAiFCaPrP+KOcF2uPty6NPBs3GJe5TYSkBq0QT+cB1cXselbLq3gT+NgV6x3olLREQCX9Nw+Pdr4Ko+dVspoVMrmDoGkjw0ZE7qTkMRJGCFBcMtg2FIPKxJhw1ZUFrFEwttQM9YGJYAfTtqlrCIiNQuJAjGDTAmGa9Jh9RMKC6ret/4tsaawYkdNcbZbEpsJeB1iDbWc7x1sPE0nl+Pw5wfjW2/G2VsD9danyIiUg/tmhv9y00D4dBJ2H8c/vGDsW3K1caDHSI0X8Nv6LpCLCPIbiSxg7udK+veRkmtiIg0XJDdWE99UKW1zxPaKqn1N0psRURERMQSlNiKiIiIiCUosRURERERS1BiKyIiIiKWoMRWRERERCxBia2IiIiIWIISWxERERGxBCW2IiIiImIJSmxFRERExBKU2IqIiIiIJSixFRERERFLUGIrIiIiIpagxFZERERELEGJrYiIiIhYghJbEREREbEEJbYiIiIiYglKbEVERETEEpTYioiIiIglBJsdgPhG6JLFphy3dNR1phzXTGWPzjPluCEvpHilXrNej9m89X42JlZoO2oH7rHaec+fqT+vme7YioiIiIglKLEVEREREUvQUAQRET+VVwRpOZB99FzZq99CbHPo1AoS4yAyzLz4xFzlFbBtP2Qdgf3Hz5XPWg0dW0Kf9tA+2rz4RMygO7YiJjhUmEfoi3ew+VCWS/nevMOEvngHO47uNycwk+j9cHU430hOnvoMFqyHdRnntmUehh92w4dr4cnPYN5PRgJsZWofrsoq4Os0mP45/G01rNgB6QfObd+cDV9thucXwWvfum4zkz5H8QXdsRUxQeqBDMKDQ7goJs6lfP2BPTQNjaBny1iTIjOH3g+D0wmr0+HLTUbyUpvyCli7B7bkwG2DIbmL10M0hdrHOTnH4B/fw8GT7u2fcRj+ugwujYcbB0Koib2+PkfxBSW2IibYcDCD/m26EGwPcilPzd1Dctuu2G2N68sUvR9GUvvFRvhuZ93/tqgU/v49nCyCK3t7PjazqX0Ydh+Ed76D0vK6/+0Pu41keNIICDOp59fnKL6gViRigg0HMhjUrtsF5esP7GFgFeVWp/fDSGjrk9RW9vlG2LTXM/H4E7UPOHQS3v2ufkntGXsOwQffGxdRZtDnKL6gxFbEBJsOZjKwXXeXMofTwaaDWWdP/KM/fobYNyYx88fPzAjRp2p7P46cOsnlc/7CyA+fZugH/8nSrDSTIvWOA3nwz8017/PKncZPbeavM+7cWkljbx8OB8z7EUpqSWrdaSNb98H6TM/FVhc674kvKLGtxOFw8OKLL5KQkEB4eDj9+vVj5cqV9OzZk0mTJpkdnrjhcL5rgrDroHl3J6qzP/8YuYUnSG7b1aX850N7KSgr5pLYBADeu+4B/nu4G5lMgHPn/YgOj+K7lOksu/0vfHD9Q0xb/ZFJ0XrHwo1Q7vBMXadKYfEWz9TlD9Q+YONeyDziufq+2OjeGG5Pssp571iB6/+vnbng8LM+prFTYlvJxIkTmTFjBvfffz+LFy9m/PjxpKSkkJGRwcCBA80Oz2ecRUWU3XY7jtVrzpWVlFD+8B8pf/oZnA4P9cAeVOEw7lQ9sxCWbDtX/sZS+J+v/esO1v6CYwC0CGviUv7hjh+4JDaeuGatAOjYtJXPYzODO+9HkN1OkN04XeWXFpPUppPP4/SWI/mw41fP1pmaaYy7tYLG3j4A1qR7tr7CEtjs4yErgX7ecziNC9Cnv4BvKn0h8NZyeGERHC80L7aaBGJ/3lBKbE+bO3cus2fPZuHChUydOpURI0bwxBNPMHToUMrLy88mtk8++SR9+vTBbrezYMECk6P2DltEBPZbb6FizjycTifOigoq/utZCAkh6M9/wmb3v2azcBN8v6vqbTnH4M3lxgxyf9CrZXuahzXhhXULOVFcyLGiAt7ZvJS3Ni/hmSsa3+Mh3X0/Mk8c4sp50xm74FnGxQ8yMWLP2rQXPH3Dp6wC0vZ5uFKTNPb2cbTAWKfW0zZkeb7OmgT6ee/bNFi+o+ptB07A/y6FkjKfhuSWQOzPG0qrIpz27LPPMnr0aIYPH+5SHh8fT0hICImJiQAkJCTw6quvMm3aNDPC9Bn7uBtwLPgU55rvcaxLxXnkCMEvPo8tNMTs0C5wsghW/1L9dieQe8JYEskflkNqFtaEz2+ayuOrPqTr2w/RJDiMfm0689Utj3F5nAWntNfC3feja4s2fJcynay8w4z6aAZjuyebGLXnVH74giflHIOLLTAfp7G3jxwvtg+nE2w279R/vkA+7xWVwrLt1W93YlyApGbCsB4+C8ttgdSfe4ISW2Dfvn1s3bqVRx555IJt2dnZ9O3bl7Aw4/E+d911FwDPPPOMT2P0NVtEOPbbbqHihZchugXBr7yELbJJ7X9ogo17ax/jZMNY5N4fEluAYR17sfKO6WaH4Tdqez9KyssICzZOws1CI4gKjfBRZN53IM9L9Z7wTr1maMztI9dL7aOwBPKLoZkP36pAPe9tyal9TPKZPsYfE9tA6s89QYktRmIL0K5dO5fyoqIiVq5cyZgxY3wWi81Ll88h3y6q3x8WFxM0YTy26Po9l9Fbr6eyS8fPZNANj2GrYQ1EJ7B67c9MHtnf6/GUTp3rkXru+/pt1uXuprSinHW5u/n85kdr3N9b77WnXk9DbDqUyeOrPiTIZqfcUcFLI37j9WP6ou0CTHwth6YtO7qU1TSzvbptD89x/feqNT/y0KhLGxhdw/iq7XizffiqHVRn2ITnGHTDYy5lta184G4bievclZOHs+ofXCVWO+9VNuiGxxg24bka93ECab/sxXZdF6/HY+X+vCZON2eCK7EFYmJiAEhPT3dJYp9//nlyc3NJTrbGV1p14Vi6HMdHH2O79hoqPvsC23XXmt6oq1OUf6TGpBbA4aig6OQhH0XkGe+Ovt/sEPzGkPY9WH77X8wOwysqyoq9Um95qR/NmPQytY/6Kfdi3fXlj+e9ovzaBzkbfcxhH0RTd4HUn3uCElugW7duJCUlMXPmTFq2bEmHDh1YsGABixYZV0W+XBHB3SuSugpdstjtfR3r1lPxv28Q9PR0bAkJlP/2HpyrVmMbfkWdj+ut11PZiVPw1Oc1L+tltwfx5/tG8emz3o+n7NF5Xj9GVbz1Xpv1eszmi7YLxqL72/a7lp1/Zw3O3YWraltVUm68ik9mmrsOkRXajq/aQXU2ZhlPlausujZQlzbSJBQKjuV6bIyt1c57lRWWwF8+NVbfqY7dHsQDtw1i3jTvx2Pl/twTrDcdrh7sdjvz58+nb9++TJ48mXvuuYeYmBimTJlCcHAwSUlJZofoM45t26mY+RxBj/4Re1LiubE5/5jnt8uCtGgCQ7tXv90GxDSF/tZaAUgsIs5Lqxt5q17xLW99jh1b+m7iWKCLDIPhvarfbrNB8wj/m6wZiP25JyixPa1Hjx6sWLGCwsJCsrOzmTFjBmlpafTu3ZuIiHOj68vKyiguLsbhcJz9PVCuYmrjzMyi4i/TCbr/PuzDzo3Ns4+7HvLycK5abWJ0Nbt5ECR3Nn63nfk5fdJu3QymjIRQfT8hfmiAFy64gu1wUcfa9xP/FxNlJKGeNqCz5+u0suv7wdB44/fz+5joJjDlaogINSu6CwVyf95Q6uprkJqaypAhQ1zK7rvvPmbPng3A6tVGw8jMzKRLly6+Ds/jbF27EPLJxxeWh4cT8rH5E4hqEhwEv70MRvSBtXuMxbLDQ4yTd+/2EKRLOPFTbZtDQlvjKXmeMqCLcZfJ30xd8QEbDmQwoG0XXr7qX122Pf39Ar7YnUp0WCTXxyfz8KCx/H3rSv577UJiI1swKLY7zw2/4+z+jyybzbHiAmaPnVLlscodFdz79dtk5R1iTLdk/nTJuAbtZxabDS5LgA/Xeq7O8BD/WSEmUNjtMOESuKIn/LjbWN4rNBj6xUFinP/1MYHcnzeUEttqFBQUkJ6ezoMPPuhSPmvWLGbNmmVOUFKruJbGjxXszz/GA9+8S17pKYJsdga268aLPlgNwFNqSmKy8g5z2Zxp9GrZgdCgYBbd9h9Vlp3xSuo/+XzXer5LmV6v49VnP1/6l2R4+WvPPJozPATG+OHoqU0HMyksK2ZFypM8tOR9UnP3MCjWdQzR81feycjOiS5lfxx8PROTRriUHSzMI+vkYZrVsKzXl7s30Ktle2aNeZAbP32BA4UnaBfZot77mWlwN1izC/Yd80x9N/SHMD/p/ffnH+Pl9V8xofelTF3xgd+f62JbGN8Qiv/ys2sM/xEVFUVFRQW/+93vzA5FGqlle9NI6TOMb8c/wYqUJzl0Ko+0w9lmh+WWyklMaUU5qbl7LthnZOdElt4+zSWBraqspLyMLYdqft3uHK8u+/lax5ZwbWLN+zw8x71JQTcNhOhIz8TlST/9uourOhkv8qrOiazN3X3BPo+vmsfoj59h86Gss2WvbVjMVR8+zfK9W8+WvbphEVMGXFvj8db+uouRnS8CYHhcH1IPVP1Zu7ufmYLscOdQ45upmrjTRnq2g0sTPBdbQy3bm8bIzol0ahYTkOc68T9KbEVMtjJ7O21ev5erP5xB/Nu/55bPXzLKc3YwLn4Q4cHGwK1gWxBBtSxr5i/cSWJW5mxnxLyneDV1UY1l/5e2gt9cVPMMXneOV5f9zDDqIhhSwyRId4xOhEsaWIe3nCg5RbMw4w5r87AIjhcXumx/KHk0a38zk9dHTeSRZcZwr3Hxg9h493N8NO5hHls5hwqHg2NFBRw5lU98dLsLjnH+8ZqGnjlekwuOV9f9zBbbAiZeboyfrq9OreDuy82bNFbVuW5lzg6uiOtNu8gWAXmuE/+jliNissvjejE4tjtLb5/G5XG9eG3kPTidTk6VlRAVGg7AlsPZHC3Kp09MYMwIqi2JiY1swbaJL7Fkwn+yfO9WthzOrrKsrKKcVTk7GNGpb4OOV9f9zGC3wfhLYEw/CKpj4hEaDOMvhtF+OAThjBZhTThZYqyte7KkiBbhrk8+ahkRBUBCdOy5vwmPxG6z07pJMxKiYzl4Ko/XN37N5AHXuHW8/NNr+Z4sPUWLsKpvY7u7nz/o0wEmj6zfHfkBneHBkeZOcKrqXFf5PAeBd64T/+Mno2xErO9A4Qnu+vJ1l7K2kc156rLxdG3eBoB9+cfo0LQlmw9lkdTGmLZ8rKiAh5fNYu4Nv/d5zLWp7jVd1rFXjUlMWHAIYRiPQB3TfQDbjuSQ1LrTBWUbD2Rwe+/an55VW9JU1/3MYrfBNRfBRR1g4SbYmVv7/olxMG4AtIryTYz1NaR9Au/+vIzbeg1h+d6t/Pa8u/AnS07RLKwJR06dpNxR4VJWVFbK7uMHaB3RlKy8Q0xb/SFF5UbZ/J0/cVOPwRwtKqBtZPOz9V3SPoHl2dsYHBvPd9nbmdDrUsodFW7t58+6t4HHxsI3acYkpuKymvePbQHXJUFSnE/Cq1HGiUMu57rDRSfPnufAv891EjiU2Ir4SLvIFiy9fdoF5Qt3p9KnVUcqHA7sp78jXJqVxtWdL6LcUcHdi97gueF3+N2EFqj+NW06mFljEpNfWnT2698f9qczJfnaKss+S1/PlsNZvPvzMrYf2ccbG7/h/v5XX5CcVJU0VZXE1JZc+Yv20fDAVXA4H37OhpyjcPCk8bz60GCIbW58rdy/s7GOcyAY0LYr4cEhjJj3FEmtOzE41lg76eFls3hl5N38eeVcth3Zh8Pp4JkrUgB4dcNivs38GYfTyaMXjyMkKJi/jTEm9GblHebJNR9zW68h7D5+gBfXfclb19539njXd0/m375+iyvnTWd01/7ERkW7vZ+/Cw8xJhyOToTN2ZB1xJhYdqrUuNhpFWVMou3dHrq29p/1arcf3edyrjtzngP8/lwngUOJrYjJth/Zx5D2CZRUlHHo1ElyC46z8WAmfxg8lo93/kTqgQweX2k81ee/rpjAkPY9TI64dlUlMQcKT/C3tO/4jyE3smbfTqZ/P5+woBAu7dCTi2PjWZyx6YKyi08nPwBXzpvOlORrq0xOqjqeu/v5s9ZN4eqaR2EElKpWoXhl5N0A/PWaey/YNu3SW5h26S1V1tWleeuzS31tOZzNhPPu7IcEBfP3sQ+5lLm7X6AICzHGVPvruOrznX+uW5G9lT8MHgvAgl/WBuS5TvyPzWmVpwtIjeryCD5PKh11nSnHNZMnHi05f+dP3NZrSO07VhLyQkqDj1sVf3ss6qfp64gOj6x13K27+1XHW+9nY+Jvbac+1A7cU5/Puj7nufM1xs9H/XnNdMdWxA819GRvZTf3uNij+4mIOXSeE2/QHVsRERERsQQt9yUiIiIilqDEVkREREQsQYmtiIiIiFiCElsRERERsQQltiIiIiJiCUpsRURERMQSlNiKiIiIiCUosRURERERS1BiKyIiIiKWoMRWRERERCxBia2IiIiIWIISWxERERGxBCW2IiIiImIJSmxFRERExBKU2IqIiIiIJSixFRERERFLUGIrIiIiIpagxFZERERELEGJrYiIiIhYwv8DfKCrpFkuRr4AAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 900.592x144.48 with 1 Axes>"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def bog(lam, k=1):\n",
+    "    a = \"B_\"+str(k)\n",
+    "    circuit = QuantumCircuit(2, name=a)\n",
+    "    n=4\n",
+    "    th = -np.arccos((lam-np.cos(2*np.pi*k/n)) /np.sqrt((lam-np.cos(2*np.pi*k/n))**2+np.sin(2*np.pi*k/n)**2))\n",
+    "\n",
+    "    circuit.x(1)\n",
+    "    circuit.cx(1, 0)\n",
+    "    # controlled RX\n",
+    "    circuit.u1(-np.pi/2, 1)\n",
+    "    circuit.u3(th/2, 0., 0., 1)\n",
+    "    circuit.cx(0, 1)\n",
+    "    circuit.u3(-th/2, 0., 0., 1)\n",
+    "    circuit.cx(0, 1)\n",
+    "    circuit.u1(np.pi/2, 1)\n",
+    "    # ----------\n",
+    "    circuit.cx(1, 0)\n",
+    "    circuit.x(1)\n",
+    "\n",
+    "    return circuit\n",
+    "\n",
+    "bog(0.5,k=1).draw(output=\"mpl\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x576 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "from math import pi\n",
-    "\n",
-    "# ROTATIONAL GATES\n",
-    "def RZ(qp,th,q0):\n",
-    "    qp.u1(-th,q0)\n",
-    "def RY(qp,th,q0):\n",
-    "    qp.u3(th,0.,0.,q0)\n",
-    "def RX(qp,th,q0):\n",
-    "    qp.u3(th,0.,pi,q0)\n",
-    "\n",
-    "# CRX (Controlled-RX)\n",
-    "# control qubit: q0\n",
-    "# target qubit: q1\n",
-    "def CRX(qp,th,q0,q1):\n",
-    "    RZ(qp,pi/2.0,q1)\n",
-    "    RY(qp,th/2.0,q1)\n",
-    "    qp.cx(q0,q1)\n",
-    "    RY(qp,-th/2.0,q1)\n",
-    "    qp.cx(q0,q1)\n",
-    "    RZ(qp,-pi/2.0,q1)\n",
-    "# Bogoliubov B_1\n",
-    "def B(qp,thk,q0,q1):\n",
-    "    qp.x(q1)\n",
-    "    qp.cx(q1,q0)\n",
-    "    CRX(qp,thk,q0,q1)\n",
-    "    qp.cx(q1,q0)\n",
-    "    qp.x(q1)"
+    "get_plot(bog(0.5,k=1))"
    ]
   },
   {
@@ -380,206 +577,567 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 111,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"word-wrap: normal;white-space: pre;background: #fff0;line-height: 1.1;font-family: &quot;Courier New&quot;,Courier,monospace\">        ┌──────┐┌────┐┌───────────┐ ░           ┌────┐           ░ ┌─┐         \n",
+       "q_0: |0>┤0     ├┤0   ├┤ RZ(-pi/2) ├─░───────────┤0   ├───────────░─┤M├─────────\n",
+       "        │  B_1 ││  F │└───────────┘ ░ ┌────────┐│  F │┌────────┐ ░ └╥┘┌─┐      \n",
+       "q_1: |0>┤1     ├┤1   ├──────────────░─┤0       ├┤1   ├┤0       ├─░──╫─┤M├──────\n",
+       "        └──────┘├────┤              ░ │  FSWAP │├────┤│  FSWAP │ ░  ║ └╥┘┌─┐   \n",
+       "q_2: |0>────────┤0   ├──────────────░─┤1       ├┤0   ├┤1       ├─░──╫──╫─┤M├───\n",
+       "         ┌───┐  │  F │              ░ └────────┘│  F │└────────┘ ░  ║  ║ └╥┘┌─┐\n",
+       "q_3: |0>─┤ X ├──┤1   ├──────────────░───────────┤1   ├───────────░──╫──╫──╫─┤M├\n",
+       "         └───┘  └────┘              ░           └────┘           ░  ║  ║  ║ └╥┘\n",
+       " c: 0 4/════════════════════════════════════════════════════════════╩══╩══╩══╩═\n",
+       "                                                                    0  1  2  3 </pre>"
+      ],
+      "text/plain": [
+       "        ┌──────┐┌────┐┌───────────┐ ░           ┌────┐           ░ ┌─┐         \n",
+       "q_0: |0>┤0     ├┤0   ├┤ RZ(-pi/2) ├─░───────────┤0   ├───────────░─┤M├─────────\n",
+       "        │  B_1 ││  F │└───────────┘ ░ ┌────────┐│  F │┌────────┐ ░ └╥┘┌─┐      \n",
+       "q_1: |0>┤1     ├┤1   ├──────────────░─┤0       ├┤1   ├┤0       ├─░──╫─┤M├──────\n",
+       "        └──────┘├────┤              ░ │  FSWAP │├────┤│  FSWAP │ ░  ║ └╥┘┌─┐   \n",
+       "q_2: |0>────────┤0   ├──────────────░─┤1       ├┤0   ├┤1       ├─░──╫──╫─┤M├───\n",
+       "         ┌───┐  │  F │              ░ └────────┘│  F │└────────┘ ░  ║  ║ └╥┘┌─┐\n",
+       "q_3: |0>─┤ X ├──┤1   ├──────────────░───────────┤1   ├───────────░──╫──╫──╫─┤M├\n",
+       "         └───┘  └────┘              ░           └────┘           ░  ║  ║  ║ └╥┘\n",
+       " c: 0 4/════════════════════════════════════════════════════════════╩══╩══╩══╩═\n",
+       "                                                                    0  1  2  3 "
+      ]
+     },
+     "execution_count": 111,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# This circuit can be implemented in ibmqx5 using qubits (q0,q1,q2,q3)=(6,7,11,10)\n",
-    "# It can also be implemented between other qubits or in ibqmx2 and ibqmx4 using fermionic SWAPS\n",
-    "# For instance, the lines commented correspond to the implementations:\n",
-    "# ibmqx2 (q0,q1,q2,q3)=(4,2,0,1)\n",
-    "# ibmqx4 (q0,q1,q2,q3)=(3,2,1,0)\n",
-    "def Udisg(qc,lam,q0,q1,q2,q3):\n",
-    "    k=1\n",
-    "    n=4\n",
-    "    th1=-np.arccos((lam-np.cos(2*pi*k/n))/np.sqrt((lam-np.cos(2*pi*k/n))**2+np.sin(2*pi*k/n)**2))\n",
-    "    B(Udis,th1,q0,q1)\n",
-    "    F1(Udis,q0,q1)\n",
-    "    F0(Udis,q2,q3)\n",
-    "    #fSWAP(Udis,q2,q1) # for ibmqx2\n",
-    "    #fSWAP(Udis,q1,q2) # for ibmqx4\n",
-    "    F0(Udis,q0,q2)\n",
-    "    F0(Udis,q1,q3)\n",
-    "    #fSWAP(Udis,q2,q1) # for ibmqx2\n",
-    "    #fSWAP(Udis,q1,q2) # for ibmqx4\n",
-    "\n",
-    "def Initial(qc,lam,q0,q1,q2,q3):\n",
-    "    if lam <1:\n",
-    "        qc.x(q3)\n",
-    "\n",
-    "def Ising(qc,ini,udis,mes,lam,q0,q1,q2,q3,c0,c1,c2,c3):\n",
-    "    Initial(ini,lam,q0,q1,q2,q3)\n",
-    "    Udisg(udis,lam,q0,q1,q2,q3)\n",
-    "    mes.measure(q0,c0)\n",
-    "    mes.measure(q1,c1)\n",
-    "    mes.measure(q2,c2)\n",
-    "    mes.measure(q3,c3)\n",
-    "    qc.add_circuit(\"Ising\",ini+udis+mes)"
+    "def get_circ(lam, barriers=True, with_initial=True):\n",
+    "    circuit = QuantumCircuit(4, 4)\n",
+    "    \n",
+    "    if with_initial:\n",
+    "        if lam < 1:\n",
+    "            circuit.x(3)\n",
+    "\n",
+    "    circuit.append(bog(lam, k=1), [0, 1])\n",
+    "\n",
+    "    circuit.append(F(), [0, 1])\n",
+    "    circuit.rz(-2*np.pi/4*1, 0)  # twiddle factor\n",
+    "    circuit.append(F(), [2, 3])\n",
+    "    if barriers:\n",
+    "        circuit.barrier()\n",
+    "    circuit.append(fswap(), [1, 2])\n",
+    "    circuit.append(F(), [0, 1])\n",
+    "    circuit.append(F(), [2, 3])\n",
+    "    circuit.append(fswap(), [1, 2])\n",
+    "    \n",
+    "    if barriers:\n",
+    "        circuit.barrier()\n",
+    "    circuit.measure(0, 0)\n",
+    "    circuit.measure(1, 1)\n",
+    "    circuit.measure(2, 2)\n",
+    "    circuit.measure(3, 3)\n",
+    "\n",
+    "    return circuit\n",
+    "\n",
+    "\n",
+    "get_circ(0.25).draw(output=\"text\", fold=2000, vertical_compression=\"high\", initial_state=True, cregbundle=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We are now ready to simulate our system. We start with the qasm simulator:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 8,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Done\n"
+     ]
+    }
+   ],
    "source": [
-    "#import sys  \n",
-    "#sys.path.append(\"../../\")   \n",
-    "# importing the QISKit\n",
-    "from qiskit import QuantumCircuit,QuantumProgram\n",
-    "#import Qconfig  \n",
-    "# useful additional packages\n",
-    "import matplotlib.pyplot as plt\n",
-    "%matplotlib inline\n",
-    "import numpy as np\n",
-    "from scipy import linalg as la"
+    "mag_sim=[]\n",
+    "lam_values=np.linspace(0,2,15)\n",
+    "qasm=Aer.get_backend(\"qasm_simulator\")\n",
+    "shots=2048\n",
+    "for lam in lam_values:\n",
+    "    qc = get_circ(lam)\n",
+    "    result = execute(qc, backend=qasm, shots=shots).result()\n",
+    "    res = result.get_counts()\n",
+    "    r1 = list(res.keys())\n",
+    "    r2 = list(res.values())\n",
+    "    M = 0\n",
+    "    for j in range(0, len(r1)):\n",
+    "        M = M+(4-2*digit_sum(r1[j]))*r2[j]/shots\n",
+    "    mag_sim.append(M/4)\n",
+    "print(\"Done\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 9,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# Simulator\n",
-    "shots = 1024\n",
-    "backend ='ibmqx_qasm_simulator'  \n",
-    "coupling_map = None\n",
-    "mag_sim = []\n",
-    "for i in range(8):\n",
-    "    Isex = QuantumProgram()\n",
-    "    q = Isex.create_quantum_register(\"q\",4)\n",
-    "    c = Isex.create_classical_register(\"c\", 4)\n",
-    "    Udis = Isex.create_circuit(\"Udis\", [q], [c])\n",
-    "    ini = Isex.create_circuit(\"ini\",[q],[c])\n",
-    "    mes = Isex.create_circuit(\"mes\",[q],[c])\n",
+    "def exact(lam):\n",
+    "    if lam < 1:\n",
+    "        return lam/(2*np.sqrt(1+lam**2))\n",
+    "    if lam > 1:\n",
+    "        return 1/2+lam/(2*np.sqrt(1+lam**2))\n",
+    "    return None\n",
+    "\n",
+    "\n",
+    "vexact = np.vectorize(exact)\n",
+    "l = np.arange(0.0, 2.0, 0.01)\n",
     "\n",
-    "    lam=i*0.25\n",
-    "    Ising(Isex,ini,Udis,mes,lam,q[0],q[1],q[2],q[3],c[0],c[1],c[2],c[3])\n",
+    "fig=plt.figure(figsize=(10,6))\n",
+    "ax=fig.add_subplot(111)\n",
     "\n",
-    "#    Isex.set_api(Qconfig.APItoken, Qconfig.config[\"url\"]) # set the APIToken and API url   \n",
     "\n",
-    "    result = Isex.execute([\"Ising\"], backend=backend,\n",
-    "                    coupling_map=coupling_map, shots=shots,timeout=240000)\n",
-    "    res=result.get_counts(\"Ising\")\n",
-    "    r1=list(res.keys())\n",
-    "    r2=list(res.values())\n",
-    "    M=0\n",
-    "    for j in range(0,len(r1)):\n",
-    "        M=M+(4-2*digit_sum(r1[j]))*r2[j]/shots\n",
-    "    #print(\"$\\lambda$: \",lam,\", $<\\sigma_{z}>$: \",M/4)\n",
-    "    mag_sim.append(M/4)"
+    "ax.plot(l, vexact(l), linestyle=\"solid\", color=\"black\", label=\"Exact\")\n",
+    "ax.scatter(lam_values, mag_sim, marker=\"^\", s=80, c=\"red\", label=\"QASM simulator\")\n",
+    "\n",
+    "ax.set_xlabel(r'$\\lambda$')\n",
+    "ax.set_ylabel(r'$\\langle \\sigma_z \\rangle$')\n",
+    "ax.set_title('Magnetization of the ground state of n=4 Ising spin chain')\n",
+    "ax.legend()\n",
+    "ax.grid()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Real device"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we turn our attention to a real device. By the time of writing the available devices are:\n",
+    "\n",
+    "1. ibmq_5_yorktown (formerly known as ibmqx2) -- 5 qubits -- QV 8\n",
+    "2. ibmq_16_melbourne -- 15 qubits -- QV 8\n",
+    "3. ibmq_vigo -- 5 qubits -- QV 16\n",
+    "4. ibmq_ourense -- 5 qubits -- QV 8\n",
+    "5. ibmq_burlington -- 5 qubits -- QV 8 (Retiring on Aug. 31)\n",
+    "6. ibmq_essex -- 5 qubits -- QV 8 (Retiring on Aug. 31)\n",
+    "7. ibmq_london -- 5 qubits -- QV 8 (Retiring on Aug. 31)\n",
+    "8. ibmq_armonk -- 1 qubit -- Pulse access\n",
+    "9. ibmq_valencia -- 5 qubits -- QV 16 \n",
+    "10. ibmq_santiago -- 5 qubits -- QV 32 (Access starting Aug. 7)\n",
+    "\n",
+    "Therefore, to motivate our study on Quantum Computation, we will make use of the device with the highest quantum volume, so we can expect great results for a NISQ device."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 8,
    "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<IBMQSimulator('ibmq_qasm_simulator') from IBMQ(hub='ibm-q', group='open', project='main')>,\n",
+       " <IBMQBackend('ibmqx2') from IBMQ(hub='ibm-q', group='open', project='main')>,\n",
+       " <IBMQBackend('ibmq_16_melbourne') from IBMQ(hub='ibm-q', group='open', project='main')>,\n",
+       " <IBMQBackend('ibmq_vigo') from IBMQ(hub='ibm-q', group='open', project='main')>,\n",
+       " <IBMQBackend('ibmq_ourense') from IBMQ(hub='ibm-q', group='open', project='main')>,\n",
+       " <IBMQBackend('ibmq_valencia') from IBMQ(hub='ibm-q', group='open', project='main')>,\n",
+       " <IBMQBackend('ibmq_london') from IBMQ(hub='ibm-q', group='open', project='main')>,\n",
+       " <IBMQBackend('ibmq_burlington') from IBMQ(hub='ibm-q', group='open', project='main')>,\n",
+       " <IBMQBackend('ibmq_essex') from IBMQ(hub='ibm-q', group='open', project='main')>,\n",
+       " <IBMQBackend('ibmq_armonk') from IBMQ(hub='ibm-q', group='open', project='main')>,\n",
+       " <IBMQBackend('ibmq_santiago') from IBMQ(hub='ibm-q', group='open', project='main')>]"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "provider=IBMQ.load_account()\n",
+    "provider.backends()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
    "outputs": [],
    "source": [
-    "# Real device\n",
-    "shots = 1024\n",
-    "#backend ='ibmqx5'  \n",
-    "max_credits = 5\n",
-    "mag=[]\n",
-    "for i in range(8):\n",
-    "    Isex = QuantumProgram()\n",
-    "    q = Isex.create_quantum_register(\"q\",12)\n",
-    "    c = Isex.create_classical_register(\"c\", 4)\n",
-    "    Udis = Isex.create_circuit(\"Udis\", [q], [c])\n",
-    "    ini = Isex.create_circuit(\"ini\",[q],[c])\n",
-    "    mes = Isex.create_circuit(\"mes\",[q],[c])\n",
-    "\n",
-    "    lam=i*0.25\n",
-    "    Ising(Isex,ini,Udis,mes,lam,q[6],q[7],q[11],q[10],c[0],c[1],c[2],c[3])\n",
-    "\n",
-    "#    Isex.set_api(Qconfig.APItoken, Qconfig.config[\"url\"]) # set the APIToken and API url\n",
+    "device=provider.get_backend(\"ibmq_santiago\")\n",
+    "# device"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Below you can see the qubit coupling map for our device. Remember that the system we started with assumed periodic boundary conditions, therefore the best possible qubit configuration is \"ring-like\". However, as can be seen below, our device does not have that coupling, however due to the high gate fidelity and/or low error rates it's a worthy trade off.\n",
     "\n",
-    "    result = Isex.execute([\"Ising\"], backend=backend,\n",
-    "                    max_credits=max_credits, wait=10, shots=shots,timeout=240000)\n",
-    "    res=result.get_counts(\"Ising\")\n",
-    "    r1=list(res.keys())\n",
-    "    r2=list(res.values())\n",
-    "    M=0\n",
-    "    for j in range(0,len(r1)):\n",
-    "        M=M+(4-2*digit_sum(r1[j]))*r2[j]/shots\n",
-    "    #print(\"$\\lambda$: \",lam,\", $<\\sigma_{z}>$: \",M/4)\n",
-    "    mag.append(M/4)"
+    "Note: Currently a device that would satisfy the ideal configuration would be IBMQ 16 Melbourne."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7efe4afbb080>"
+       "<Figure size 216x216 with 1 Axes>"
       ]
      },
+     "execution_count": 17,
      "metadata": {},
-     "output_type": "display_data"
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "\n",
-    "\n",
-    "# As it is a system of only 4 particles, we can easily compute the exact result\n",
-    "def exact(lam):\n",
-    "    if lam <1:\n",
-    "        return lam/(2*np.sqrt(1+lam**2))\n",
-    "    if lam >1:\n",
-    "        return 1/2+lam/(2*np.sqrt(1+lam**2))\n",
-    "    return None\n",
-    "vexact = np.vectorize(exact)\n",
-    "l=np.arange(0.0,2.0,0.01)\n",
-    "l1=np.arange(0.0,2.0,0.25)\n",
-    "plt.figure(figsize=(9,5))\n",
-    "plt.plot(l,vexact(l),'k',label='exact')\n",
-    "plt.plot(l1, mag_sim, 'bo',label='simulation')\n",
-    "plt.plot(l1, mag, 'r*',label='ibmqx5')\n",
-    "plt.xlabel('$\\lambda$')\n",
-    "plt.ylabel('$<\\sigma_{z}>$')\n",
-    "plt.legend()\n",
-    "plt.title('Magnetization of the ground state of n=4 Ising spin chain')\n",
-    "plt.show()"
+    "plot_gate_map(device, figsize=(3,3))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "First let's unroll our circuit:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 79,
+   "execution_count": 164,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Operations:  OrderedDict([('cx', 28), ('h', 20), ('sdg', 4), ('tdg', 4), ('t', 4), ('s', 4), ('measure', 4), ('u1', 3), ('u3', 3), ('x', 2)])\n",
+      "Total number of gates:  76\n",
+      "Depth:  46\n"
+     ]
+    },
     {
      "data": {
-      "image/png": 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999zD8uXL6d69O/379w953ptvvplhw4bRpk0bUlNT6dChAwCff/45ixYtYu7c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\nc+6kDy6M6eSlJJnZYudcWqTjKEkqY+l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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fe58439f7f0>"
+       "<Figure size 1683.19x1047.48 with 1 Axes>"
       ]
      },
+     "execution_count": 164,
      "metadata": {},
-     "output_type": "display_data"
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "#This was the result when the real device is used:"
+    "qc=get_circ(0.25, barriers=False)\n",
+    "print(\"Operations: \", qc.decompose().count_ops())\n",
+    "print(\"Total number of gates: \", qc.decompose().size())\n",
+    "print(\"Depth: \", qc.decompose().depth())\n",
+    "qc.decompose().draw(output=\"mpl\",vertical_compression=\"high\", initial_state=True, cregbundle=True)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The jump in the magnetization is due to the finite size effect. If we consider more and more spins, this jump decrease it until desappear in the limit $n\\rightarrow \\infty$."
+    "Note the high amount of gates and depth that our \"raw\" circuit has. Can we make it better?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 163,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Operations:  OrderedDict([('u2', 16), ('cx', 12), ('u3', 10), ('measure', 4), ('u1', 1)])\n",
+      "Total number of gates:  43\n",
+      "Depth:  18\n",
+      "Number of gates removed:  32\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1878.24x806.68 with 1 Axes>"
+      ]
+     },
+     "execution_count": 163,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "circuit_transpiled = transpile(get_circ(0.25, barriers=False), backend=device, optimization_level=3)\n",
+    "print(\"Operations: \", circuit_transpiled.count_ops())\n",
+    "print(\"Total number of gates: \", circuit_transpiled.size())\n",
+    "print(\"Depth: \", circuit_transpiled.depth())\n",
+    "print(\"Number of gates removed: \", qc.decompose().size()-circuit_transpiled.size())\n",
+    "circuit_transpiled.draw(output=\"mpl\",vertical_compression=\"high\", initial_state=True, cregbundle=True)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Time evolution\n",
-    "\n",
+    "Having optimized our circuit, we move on to the next part: error mitigation. When running on an actual device, errors are inevitable, however we can try to mitigate some of the measurement errors as is explained [here](https://qiskit.org/textbook/ch-quantum-hardware/measurement-error-mitigation.html). Notice also that it requires $2^N$ gates to get a calibration matrix. Since here we only have 4 qubits it is fairly easy to do so, but for greater systems it may be expensive."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "26661db0214e46779ef433f507d54025",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Accordion(children=(VBox(layout=Layout(max_width='710px', min_width='710px')),), layout=Layout(max_height='500…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/javascript": [
+       "$('div.job_widget')\n",
+       "        .detach()\n",
+       "        .appendTo($('#header'))\n",
+       "        .css({\n",
+       "            'z-index': 999,\n",
+       "             'position': 'fixed',\n",
+       "            'box-shadow': '5px 5px 5px -3px black',\n",
+       "            'opacity': 0.95,\n",
+       "            'float': 'left,'\n",
+       "        })\n",
+       "        "
+      ],
+      "text/plain": [
+       "<IPython.core.display.Javascript object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#get the mitigation matrix\n",
+    "%qiskit_job_watcher\n",
+    "meas_calibs, state_labels = complete_meas_cal(qr=QuantumRegister(4)) #first we get the measurement calibration circuits: one for each basis state->2^n circuits\n",
+    "cal_results = execute(meas_calibs, \n",
+    "                      backend=device, \n",
+    "                      shots=4096).result()\n",
+    "\n",
+    "meas_fitter = CompleteMeasFitter(cal_results, state_labels)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x720 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def get_plot_matrix(matrix):\n",
+    "    \n",
+    "    fig=plt.figure(figsize=(10,10))\n",
+    "    ax1=fig.add_subplot(111)\n",
+    "    \n",
+    "    divider = make_axes_locatable(ax1)\n",
+    "    cax = divider.append_axes('right', size='5%', pad=0.05)\n",
+    "    im = ax1.imshow(matrix, cmap=\"Blues\", vmin=0,vmax=1)\n",
+    "    fig.colorbar(im, cax=cax, orientation='vertical')\n",
+    "\n",
+    "    for (j,i),label in np.ndenumerate(matrix):\n",
+    "        try:\n",
+    "            if label<0:\n",
+    "                ax1.text(i,j,float(str(label)[:5]),ha='center',va='center')\n",
+    "            else:\n",
+    "                ax1.text(i,j,float(str(label)[:4]),ha='center',va='center')\n",
+    "        except:\n",
+    "            ax1.text(i,j,label,ha='center',va='center')\n",
+    "\n",
+    "\n",
+    "    plt.show()\n",
+    "    \n",
+    "get_plot_matrix(meas_fitter.cal_matrix)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Notice how the calibration matrix is almost diagonal! This demonstrates the quality of measurement of the device."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "circuits=[]\n",
+    "lam=np.linspace(0,2,20)\n",
+    "for i in lam:\n",
+    "    circuits.append(transpile(get_circ(i,barriers=False), backend=device, optimization_level=3))\n",
+    "    \n",
+    "# jobs_dev=execute(circuits,backend=device,shots=2048)\n",
+    "jobs_dev=device.retrieve_job(\"5f327675252dfd001a34a3b7\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Done\n"
+     ]
+    }
+   ],
+   "source": [
+    "def get_M(res):\n",
+    "    r1 = list(res.keys())\n",
+    "    r2 = list(res.values())\n",
+    "    shots=np.sum(r2)\n",
+    "    M = 0\n",
+    "    for j in range(0, len(r1)):\n",
+    "        M = M+(4-2*digit_sum(r1[j]))*r2[j]/shots\n",
+    "    return M\n",
+    "\n",
+    "N = 20\n",
+    "\n",
+    "mag_sim=[]\n",
+    "mag_dev = np.zeros((2,N))\n",
+    "lam = np.linspace(0, 2, N)\n",
+    "\n",
+    "for i in range(len(lam)):\n",
+    "    lam_value = lam[i]\n",
+    "    qc = get_circ(lam_value)\n",
+    "    result = execute(qc, backend=Aer.get_backend(\"qasm_simulator\"), shots=2048).result().get_counts()\n",
+    "    result_dev=jobs_dev.result().get_counts(i)\n",
+    "    result_mitigated = meas_fitter.filter.apply(result_dev)\n",
+    "\n",
+    "    mag_sim.append(get_M(result)/4)\n",
+    "    mag_dev[0, i] = get_M(result_dev)/4\n",
+    "    mag_dev[1, i] = get_M(result_mitigated)/4\n",
+    "print(\"Done\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def exact(lam):\n",
+    "    if lam < 1:\n",
+    "        return lam/(2*np.sqrt(1+lam**2))\n",
+    "    if lam > 1:\n",
+    "        return 1/2+lam/(2*np.sqrt(1+lam**2))\n",
+    "    return None\n",
+    "\n",
+    "fig = plt.figure(figsize=(10, 6))\n",
+    "ax1 = fig.add_subplot(111)\n",
+    "\n",
+    "vexact = np.vectorize(exact)\n",
+    "l = np.arange(0.0, 2.0, 0.01)\n",
+    "ax1.scatter(lam, mag_sim, c=\"blue\",label=\"Simulation\")\n",
+    "ax1.scatter(lam, mag_dev[0, :],marker=\"x\", c=\"red\", label=\"Raw data\")\n",
+    "ax1.scatter(lam, mag_dev[1, :], c=\"red\", label=\"Mitigated\")\n",
+    "\n",
+    "ax1.plot(l, vexact(l), 'k', label='exact')\n",
+    "ax1.set_xlabel(r'$\\lambda$')\n",
+    "ax1.set_ylabel(r'$\\langle \\sigma_z \\rangle$')\n",
+    "ax1.set_title(\"IBMQ 16 Melbourne results\")\n",
+    "ax1.legend()\n",
+    "ax1.grid()\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The jump in the magnetization is due to the finite size effect. If we consider more and more spins, this jump decrease it until desappear in the limit $n\\rightarrow \\infty$."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Time evolution\n",
+    "\n",
     "Schrödinger equation describes the time evolution of a quantum system:\n",
     "\n",
     "$$ i\\hbar\\frac{d}{dt}|\\Psi(t)\\rangle=H(t)|\\Psi(t)\\rangle. $$\n",
@@ -637,191 +1195,341 @@
     "Once we have $|\\psi(t)\\rangle$, we can apply $U_{dis}$ and compute the expected value of the magnetization."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### QASM Simulator"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 109,
+   "metadata": {},
    "outputs": [],
    "source": [
-    "def Initial_time(qc,t,lam,q0,q1,q2,q3):\n",
-    "    qc.u3(np.arccos(lam/np.sqrt(1+lam**2)),pi/2.+4*t*np.sqrt(1+lam**2),0.,q0)\n",
-    "    qc.cx(q0,q1)\n",
-    "def Ising_time(qc,ini,udis,mes,lam,t,q0,q1,q2,q3,c0,c1,c2,c3):\n",
-    "    Initial_time(ini,t,lam,q0,q1,q2,q3)\n",
-    "    Udisg(udis,lam,q0,q1,q2,q3)\n",
-    "    mes.measure(q0,c0)\n",
-    "    mes.measure(q1,c1)\n",
-    "    mes.measure(q2,c2)\n",
-    "    mes.measure(q3,c3)\n",
-    "    qc.add_circuit(\"Ising_time\",ini+udis+mes)"
+    "def Initial_time(t,lam):\n",
+    "    qc=QuantumCircuit(4,4)\n",
+    "    qc.u3(np.arccos(lam/np.sqrt(1+lam**2)),np.pi/2.+4*t*np.sqrt(1+lam**2),0.,0)\n",
+    "    qc.cx(0,1)\n",
+    "    \n",
+    "    return qc\n",
+    "\n",
+    "def get_circ_time(time,lam):\n",
+    "    qc=Initial_time(time,lam)\n",
+    "    qc=qc.compose(get_circ(lam,barriers=False, with_initial=False))\n",
+    "    \n",
+    "    return qc"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 112,
+   "metadata": {},
    "outputs": [],
    "source": [
-    "#Simulation\n",
-    "shots = 1024\n",
-    "backend = 'ibmqx_qasm_simulator'\n",
-    "coupling_map = None\n",
-    "# We compute the time evolution for lambda=0.5,0.9 and 1.8\n",
-    "nlam=3\n",
-    "magt_sim=[[] for _ in range(nlam)]\n",
-    "lam0=[0.5,0.9,1.8]\n",
-    "for j in range(nlam):\n",
-    "    lam=lam0[j]\n",
-    "    for i in range(9):\n",
-    "        Isex_time = QuantumProgram()\n",
-    "        q = Isex_time.create_quantum_register(\"q\",4)\n",
-    "        c = Isex_time.create_classical_register(\"c\", 4)\n",
-    "        Udis = Isex_time.create_circuit(\"Udis\", [q], [c])\n",
-    "        ini = Isex_time.create_circuit(\"ini\",[q],[c])\n",
-    "        mes = Isex_time.create_circuit(\"mes\",[q],[c])\n",
-    "\n",
-    "        t=i*0.25\n",
-    "        Ising_time(Isex_time,ini,Udis,mes,lam,t,q[0],q[1],q[2],q[3],c[0],c[1],c[2],c[3])\n",
-    "\n",
-    "        Isex_time.set_api(Qconfig.APItoken, Qconfig.config[\"url\"]) \n",
-    "\n",
-    "        result = Isex_time.execute([\"Ising_time\"], backend=backend,\n",
-    "                    coupling_map=coupling_map, shots=shots,timeout=240000)\n",
-    "        res=result.get_counts(\"Ising_time\")\n",
-    "        r1=list(res.keys())\n",
-    "        r2=list(res.values())\n",
-    "        M=0\n",
-    "        for k in range(0,len(r1)):\n",
-    "            M=M+(4-2*digit_sum(r1[k]))*r2[k]/shots\n",
-    "        magt_sim[j].append(M/4)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
+    "#gather the circuits\n",
+    "circuits=[] #It will have shape (3,15)\n",
+    "mag_time_sim=[]\n",
+    "lam_values=[0.5,0.9,1.8]\n",
+    "time=np.linspace(0,2,15)\n",
+    "\n",
+    "for lam in lam_values:\n",
+    "    circuits1=[]\n",
+    "    for t in time:\n",
+    "        circuits1.append(get_circ_time(t,lam))\n",
+    "    circuits.append(circuits1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 114,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Done\n"
+     ]
+    }
+   ],
    "source": [
+    "#Set your jobs for the backend\n",
     "shots = 1024\n",
-    "backend = 'ibmqx5'\n",
-    "max_credits = 5\n",
-    "# We compute the time evolution for lambda=0.5,0.9 and 1.8\n",
-    "nlam=3\n",
-    "magt=[[] for _ in range(nlam)]\n",
-    "lam0=[0.5,0.9,1.8]\n",
-    "for j in range(nlam):\n",
-    "    lam=lam0[j]\n",
-    "    for i in range(9):\n",
-    "        Isex_time = QuantumProgram()\n",
-    "        q = Isex_time.create_quantum_register(\"q\",12)\n",
-    "        c = Isex_time.create_classical_register(\"c\", 4)\n",
-    "        Udis = Isex_time.create_circuit(\"Udis\", [q], [c])\n",
-    "        ini = Isex_time.create_circuit(\"ini\",[q],[c])\n",
-    "        mes = Isex_time.create_circuit(\"mes\",[q],[c])\n",
-    "\n",
-    "        t=i*0.25\n",
-    "        Ising_time(Isex_time,ini,Udis,mes,lam,t,q[6],q[7],q[11],q[10],c[0],c[1],c[2],c[3])\n",
-    "\n",
-    "        Isex_time.set_api(Qconfig.APItoken, Qconfig.config[\"url\"]) \n",
-    "\n",
-    "        result = Isex_time.execute([\"Ising_time\"], backend=backend,\n",
-    "                    max_credits=max_credits, wait=10, shots=shots,timeout=240000)\n",
-    "        res=result.get_counts(\"Ising_time\")\n",
-    "        r1=list(res.keys())\n",
-    "        r2=list(res.values())\n",
-    "        M=0\n",
-    "        for k in range(0,len(r1)):\n",
-    "            M=M+(4-2*digit_sum(r1[k]))*r2[k]/shots\n",
-    "        magt[j].append(M/4)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 76,
+    "qasm = Aer.get_backend(\"qasm_simulator\")\n",
+    "\n",
+    "jobs_total=[]\n",
+    "for i in range(len(circuits)):\n",
+    "    jobs_total.append(execute(circuits[i],backend=qasm, shots=shots))\n",
+    "print(\"Done\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 116,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Done\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Data analysis\n",
+    "mag_time_sim=[]\n",
+    "for i in range(len(jobs_total)):\n",
+    "    results=jobs_total[i].result()\n",
+    "    mag_t=[]\n",
+    "    for t in range(len(time)):\n",
+    "        mag_t.append(get_M(results.get_counts(t))/4)\n",
+    "    mag_time_sim.append(mag_t)\n",
+    "print(\"Done\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 125,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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9iPv03OVJpzFOdBztyCF7hjAyLtKkbSqWz6KSM1NdLDE5e+7E9mNs1bk4oQML/mDH32aM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\nAABJRU5ErkJggg==\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fe584f69438>"
+       "<Figure size 720x360 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
    "source": [
+    "#Plotting\n",
     "def exact_time(lam,tt):\n",
     "    Mt=(1 + 2*lam**2 + np.cos(4*tt*np.sqrt(1 + lam**2)))/(2 + 2*lam**2)\n",
     "    return Mt\n",
     "vexact_t = np.vectorize(exact_time)\n",
-    "t=np.arange(0.0,2.0,0.01)\n",
-    "tt=np.arange(0.0,2.25,0.25)\n",
-    "plt.figure(figsize=(10,5))\n",
-    "plt.plot(t,vexact_t(0.5,t),'b',label='$\\lambda=0.5$')\n",
-    "plt.plot(t,vexact_t(0.9,t),'r',label='$\\lambda=0.9$')\n",
-    "plt.plot(t,vexact_t(1.8,t),'g',label='$\\lambda=1.8$')\n",
-    "#plt.plot(tt, magt_sim[0], 'bo',label='simulation')\n",
-    "#plt.plot(tt, magt_sim[1], 'ro')\n",
-    "#plt.plot(tt, magt_sim[2], 'go')\n",
-    "plt.plot(tt, magt[0], 'b*',label='ibmqx5')\n",
-    "plt.plot(tt, magt[1], 'r*')\n",
-    "plt.plot(tt, magt[2], 'g*')\n",
-    "plt.plot(tt, magt[0], 'b--')\n",
-    "plt.plot(tt, magt[1], 'r--')\n",
-    "plt.plot(tt, magt[2], 'g--')\n",
-    "plt.xlabel('time')\n",
-    "plt.ylabel('$<\\sigma_{z}>$')\n",
-    "plt.legend()\n",
-    "plt.title('Time evolution |↑↑↑↑> state')\n",
+    "\n",
+    "time_exact=np.linspace(0,2,200)\n",
+    "\n",
+    "fig=plt.figure(figsize=(10,5))\n",
+    "ax=fig.add_subplot(111)\n",
+    "\n",
+    "ax.scatter(time,mag_time_sim[0], marker=\"^\", s=100, c=\"green\")\n",
+    "# ax.plot(time,mag_time_sim[0], linestyle=\"dashed\", color=\"green\")\n",
+    "ax.plot(time_exact,vexact_t(lam_values[0],time_exact),color=\"green\",label=r'$\\lambda={}$'.format(lam_values[0]))\n",
+    "\n",
+    "ax.scatter(time,mag_time_sim[1], marker=\"^\", s=100, c=\"red\")\n",
+    "ax.plot(time_exact,vexact_t(lam_values[1],time_exact),color=\"red\",label=r'$\\lambda={}$'.format(lam_values[1]))\n",
+    "\n",
+    "ax.scatter(time,mag_time_sim[2], marker=\"^\", s=100, c=\"blue\")\n",
+    "ax.plot(time_exact,vexact_t(lam_values[2],time_exact),color=\"blue\",label=r'$\\lambda={}$'.format(lam_values[2]))\n",
+    "\n",
+    "ax.set_xlabel('Time')\n",
+    "ax.set_ylabel('$<\\sigma_{z}>$')\n",
+    "ax.set_title('Time evolution |↑↑↑↑> state')\n",
+    "ax.legend()\n",
+    "ax.grid()\n",
     "plt.show()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Real device: IBMQ Santiago"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 126,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Done\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Get the error mitigation matrix\n",
+    "meas_calibs, state_labels = complete_meas_cal(qr=QuantumRegister(4)) #first we get the measurement calibration circuits: one for each basis state->2^n circuits\n",
+    "cal_results = execute(meas_calibs, \n",
+    "                      backend=device, \n",
+    "                      shots=4096).result()\n",
+    "\n",
+    "meas_fitter = CompleteMeasFitter(cal_results, state_labels)\n",
+    "print(\"Done\")"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 81,
+   "execution_count": 129,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Done\n"
+     ]
+    }
+   ],
+   "source": [
+    "#gather the circuits\n",
+    "circuits=[] #It will have shape (3,15)\n",
+    "mag_time_sim=[]\n",
+    "lam_values=[0.5,0.9,1.8]\n",
+    "time=np.linspace(0,2,15)\n",
+    "\n",
+    "for lam in lam_values:\n",
+    "    circuits1=[]\n",
+    "    for t in time:\n",
+    "        circuit_transpiled=transpile(get_circ_time(t,lam),backend=device,optimization_level=3)\n",
+    "        circuits1.append(circuit_transpiled)\n",
+    "    circuits.append(circuits1)\n",
+    "print(\"Done\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 130,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Done\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Set your jobs for the backend\n",
+    "shots = 1024\n",
+    "\n",
+    "jobs_total=[]\n",
+    "for i in range(len(circuits)):\n",
+    "    jobs_total.append(execute(circuits[i],backend=device, shots=shots))\n",
+    "print(\"Done\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 150,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Done\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Data analysis\n",
+    "mag_time=[]\n",
+    "mag_time_mitigated=[]\n",
+    "for i in range(len(jobs_total)):\n",
+    "    results=jobs_total[i].result()\n",
+    "    mag_t=[]\n",
+    "    mag_t_mitigated=[]\n",
+    "    for t in range(len(time)):\n",
+    "        mag_t.append(get_M(results.get_counts(t))/4)\n",
+    "        mag_t_mitigated.append(get_M(meas_fitter.filter.apply(results.get_counts(t)))/4)\n",
+    "    mag_time.append(mag_t)\n",
+    "    mag_time_mitigated.append(mag_t_mitigated)\n",
+    "print(\"Done\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 162,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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GoAYZNozrgs6frzsSkRzBwcCKFYCPDycFdVSVK3M9+y++AJ4757EBYU316/Nq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\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fe57fc5e358>"
+       "<Figure size 1152x504 with 4 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
    "source": [
-    "plt.figure(figsize=(13,3))\n",
-    "plt.subplot(1,3,1)\n",
-    "plt.plot(t,vexact_t(0.5,t),'b',label='$\\lambda=0.5$')\n",
-    "plt.plot(tt, magt[0], 'b*',label='ibmqx5')\n",
-    "plt.plot(tt, magt[0], 'b--',label='ibmqx5')\n",
-    "plt.xlabel('time')\n",
-    "plt.ylabel('$<\\sigma_{z}>$')\n",
-    "plt.title('$\\lambda=0.5$')\n",
-    "plt.subplot(132)\n",
-    "plt.plot(t,vexact_t(0.9,t),'r',label='$\\lambda=0.9$')\n",
-    "plt.plot(tt, magt[1], 'r*',label='ibmqx5')\n",
-    "plt.plot(tt, magt[1], 'r--',label='ibmqx5')\n",
-    "plt.xlabel('time')\n",
-    "plt.ylabel('$<\\sigma_{z}>$')\n",
-    "plt.title('$\\lambda=0.9$')\n",
-    "plt.subplot(133)\n",
-    "plt.plot(t,vexact_t(1.8,t),'g',label='$\\lambda=1.8$')\n",
-    "plt.plot(tt, magt[2], 'g*',label='ibmqx5')\n",
-    "plt.plot(tt, magt[2], 'g--',label='ibmqx5')\n",
-    "plt.xlabel('time')\n",
-    "plt.ylabel('$<\\sigma_{z}>$')\n",
-    "plt.title('$\\lambda=1.8$')\n",
-    "plt.tight_layout()\n",
+    "#Plotting\n",
+    "from matplotlib.gridspec import GridSpec\n",
+    "\n",
+    "def exact_time(lam,tt):\n",
+    "    Mt=(1 + 2*lam**2 + np.cos(4*tt*np.sqrt(1 + lam**2)))/(2 + 2*lam**2)\n",
+    "    return Mt\n",
+    "vexact_t = np.vectorize(exact_time)\n",
+    "\n",
+    "time_exact=np.linspace(0,2,200)\n",
+    "\n",
+    "\n",
+    "fig = plt.figure(figsize=(16,7))\n",
+    "gs = GridSpec(3, 2, width_ratios=[0.7,0.3], hspace=0.1, wspace=0.1)\n",
+    "ax1 = fig.add_subplot(gs[:,0])\n",
+    "ax2 = fig.add_subplot(gs[0,1])\n",
+    "ax3 = fig.add_subplot(gs[1,1])\n",
+    "ax4 = fig.add_subplot(gs[2,1])\n",
+    "\n",
+    "ax2.set_xticklabels([])\n",
+    "ax3.set_xticklabels([])\n",
+    "\n",
+    "ax1.scatter(time,mag_time_mitigated[0], marker=\"+\", s=120, c=\"green\")\n",
+    "ax1.plot(time,mag_time_mitigated[0], linestyle=\"dashed\", color=\"green\")\n",
+    "ax1.plot(time_exact,vexact_t(lam_values[0],time_exact),color=\"green\",label=r'$\\lambda={}$'.format(lam_values[0]))\n",
+    "\n",
+    "ax1.scatter(time,mag_time_mitigated[1], marker=\"+\", s=120, c=\"red\")\n",
+    "ax1.plot(time,mag_time_mitigated[1], linestyle=\"dashed\", color=\"red\")\n",
+    "ax1.plot(time_exact,vexact_t(lam_values[1],time_exact),color=\"red\",label=r'$\\lambda={}$'.format(lam_values[1]))\n",
+    "\n",
+    "ax1.scatter(time,mag_time_mitigated[2], marker=\"+\", s=120, c=\"blue\")\n",
+    "ax1.plot(time,mag_time_mitigated[2], linestyle=\"dashed\", color=\"blue\")\n",
+    "ax1.plot(time_exact,vexact_t(lam_values[2],time_exact),color=\"blue\",label=r'$\\lambda={}$'.format(lam_values[2]))\n",
+    "\n",
+    "ax1.set_xlabel('Time')\n",
+    "ax1.set_ylabel('$<\\sigma_{z}>$')\n",
+    "ax1.set_title('Time evolution |↑↑↑↑> state')\n",
+    "ax1.legend()\n",
+    "ax1.grid()\n",
+    "\n",
+    "#---------Individual plots---------#\n",
+    "ax2.scatter(time,mag_time_mitigated[0], marker=\"+\", s=120, c=\"green\")\n",
+    "ax2.plot(time,mag_time_mitigated[0], linestyle=\"dashed\", color=\"green\")\n",
+    "ax2.plot(time_exact,vexact_t(lam_values[0],time_exact),color=\"green\",label=r'$\\lambda={}$'.format(lam_values[0]))\n",
+    "ax2.legend()\n",
+    "ax2.grid()\n",
+    "\n",
+    "ax3.scatter(time,mag_time_mitigated[1], marker=\"+\", s=120, c=\"red\")\n",
+    "ax3.plot(time,mag_time_mitigated[1], linestyle=\"dashed\", color=\"red\")\n",
+    "ax3.plot(time_exact,vexact_t(lam_values[1],time_exact),color=\"red\",label=r'$\\lambda={}$'.format(lam_values[1]))\n",
+    "ax3.legend()\n",
+    "ax3.grid()\n",
+    "\n",
+    "ax4.scatter(time,mag_time_mitigated[2], marker=\"+\", s=120, c=\"blue\")\n",
+    "ax4.plot(time,mag_time_mitigated[2], linestyle=\"dashed\", color=\"blue\")\n",
+    "ax4.plot(time_exact,vexact_t(lam_values[2],time_exact),color=\"blue\",label=r'$\\lambda={}$'.format(lam_values[2]))\n",
+    "ax4.legend()\n",
+    "ax4.grid()\n",
+    "\n",
+    "ax4.set_xlabel('Time')\n",
+    "\n",
+    "fig.suptitle(\"IBMQ Santiago | Mitigated data\")\n",
     "plt.show()"
    ]
   },
@@ -844,9 +1552,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "QISKitenv",
+   "display_name": "Python 3",
    "language": "python",
-   "name": "qiskitenv"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -858,7 +1566,1758 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.2"
+   "version": "3.7.6"
+  },
+  "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": true
+  },
+  "widgets": {
+   "application/vnd.jupyter.widget-state+json": {
+    "state": {
+     "020990ea72bb4a7b8bcde3587376b034": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "1.5.0",
+      "model_name": "DescriptionStyleModel",
+      "state": {
+       "description_width": ""
+      }
+     },
+     "064365c71afa4badaba8743afebab5f2": {
+      "model_module": "@jupyter-widgets/output",
+      "model_module_version": "1.0.0",
+      "model_name": "OutputModel",
+      "state": {
+       "layout": "IPY_MODEL_2024a292a186446285d7ad991bd328ad",
+       "outputs": [
+        {
+         "data": {
+          "image/png": 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\n",
+          "text/plain": "<Figure size 792x648 with 5 Axes>"
+         },
+         "metadata": {},
+         "output_type": "display_data"
+        }
+       ]
+      }
+     },
+     "09c2053b68af4162b72b94bbd39e2d10": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "1.5.0",
+      "model_name": "TabModel",
+      "state": {
+       "layout": "IPY_MODEL_da2291afc4a64e4fb2b3a8015f58b053"
+      }
+     },
+     "0d13b3b361964663bd51b891cc017b41": {
+      "model_module": "@jupyter-widgets/base",
+      "model_module_version": "1.2.0",
+      "model_name": "LayoutModel",
+      "state": {
+       "max_height": "620px"
+      }
+     },
+     "0e0db5ca1e514014b67ccf315091b088": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "1.5.0",
+      "model_name": "DescriptionStyleModel",
+      "state": {
+       "description_width": ""
+      }
+     },
+     "1082eb1344ab4421b284afc2cfb6e8d4": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "1.5.0",
+      "model_name": "DescriptionStyleModel",
+      "state": {
+       "description_width": ""
+      }
+     },
+     "116b7f53b497438e8c92e057f30684c1": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "1.5.0",
+      "model_name": "HTMLModel",
+      "state": {
+       "layout": "IPY_MODEL_edcf16402cd24d38abc57be2a8492e68",
+       "style": "IPY_MODEL_962876487f784d6bb04c6f062e1afd30",
+       "value": "<table><style>\ntable {\n    border-collapse: collapse;\n    width: auto;\n}\n\nth, td {\n    text-align: left;\n    padding: 8px;\n}\n\ntr:nth-child(even) {background-color: #f6f6f6;};\n</style><tr><th></th><th>Type</th><th>Gate error</th></tr><tr><td><font style='font-weight:bold'>cx2_1</font></td><td>cx</td><td>0.00697</td></tr><tr><td><font style='font-weight:bold'>cx2_3</font></td><td>cx</td><td>0.00797</td></tr><tr><td><font style='font-weight:bold'>cx3_2</font></td><td>cx</td><td>0.00797</td></tr></table>"
+      }
+     },
+     "11fb34c9c2c6485abafed998806e5881": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "1.5.0",
+      "model_name": "HTMLMathModel",
+      "state": {
+       "layout": "IPY_MODEL_fe04470767a840509e5fbff3ab936fc2",
+       "style": "IPY_MODEL_ad3dbc4974774b2fa7114c602987ed4b",
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'acquire3': {'operates': {'qubits': [3]}, 'purpose': 'acquire', 'type': 'acquire'}, 'acquire4': {'operates': {'qubits': [4]}, 'purpose': 'acquire', 'type': 'acquire'}, 'd0': {'operates': {'qubits': [0]}, 'purpose': 'drive', 'type': 'drive'}, 'd1': {'operates': {'qubits': [1]}, 'purpose': 'drive', 'type': 'drive'}, 'd2': {'operates': {'qubits': [2]}, 'purpose': 'drive', 'type': 'drive'}, 'd3': {'operates': {'qubits': [3]}, 'purpose': 'drive', 'type': 'drive'}, 'd4': {'operates': {'qubits': [4]}, 'purpose': 'drive', 'type': 'drive'}, 'm0': {'operates': {'qubits': [0]}, 'purpose': 'measure', 'type': 'measure'}, 'm1': {'operates': {'qubits': [1]}, 'purpose': 'measure', 'type': 'measure'}, 'm2': {'operates': {'qubits': [2]}, 'purpose': 'measure', 'type': 'measure'}, 'm3': {'operates': {'qubits': [3]}, 'purpose': 'measure', 'type': 'measure'}, 'm4': {'operates': {'qubits': [4]}, 'purpose': 'measure', 'type': 'measure'}, 'u0': {'operates': {'qubits': [0, 1]}, 'purpose': 'cross-resonance', 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7775814414.0]]</td></tr><tr><td>simulator</td><td>False</td></tr><tr><td>conditional</td><td>False</td></tr><tr><td>online_date</td><td>2020-06-03 04:00:00+00:00</td></tr><tr><td>parametric_pulses</td><td>[]</td></tr><tr><td>description</td><td>5 qubit device</td></tr><tr><td>meas_map</td><td>[[0, 1, 2, 3, 4]]</td></tr><tr><td>backend_name</td><td>ibmq_santiago</td></tr><tr><td>allow_object_storage</td><td>True</td></tr><tr><td>hamiltonian</td><td>$$\\begin{align} \\mathcal{H}/\\hbar = & \\sum_{i=0}^{4}\\left(\\frac{\\omega_{q,i}}{2}(\\mathbb{I}-\\sigma_i^{z})+\\frac{\\Delta_{i}}{2}(O_i^2-O_i)+\\Omega_{d,i}D_i(t)\\sigma_i^{X}\\right) \\\\ & + J_{0,1}(\\sigma_{0}^{+}\\sigma_{1}^{-}+\\sigma_{0}^{-}\\sigma_{1}^{+}) + J_{3,4}(\\sigma_{3}^{+}\\sigma_{4}^{-}+\\sigma_{3}^{-}\\sigma_{4}^{+}) + J_{2,3}(\\sigma_{2}^{+}\\sigma_{3}^{-}+\\sigma_{2}^{-}\\sigma_{3}^{+}) + J_{1,2}(\\sigma_{1}^{+}\\sigma_{2}^{-}+\\sigma_{1}^{-}\\sigma_{2}^{+}) \\\\ & + \\Omega_{d,0}(U_{0}^{(0,1)}(t))\\sigma_{0}^{X} + 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J_{1,2}(\\sigma_{1}^{+}\\sigma_{2}^{-}+\\sigma_{1}^{-}\\sigma_{2}^{+}) \\\\ & + \\Omega_{d,0}(U_{0}^{(0,1)}(t))\\sigma_{0}^{X} + \\Omega_{d,1}(U_{1}^{(1,0)}(t)+U_{2}^{(1,2)}(t))\\sigma_{1}^{X} \\\\ & + \\Omega_{d,2}(U_{3}^{(2,1)}(t)+U_{4}^{(2,3)}(t))\\sigma_{2}^{X} + \\Omega_{d,3}(U_{6}^{(3,4)}(t)+U_{5}^{(3,2)}(t))\\sigma_{3}^{X} \\\\ & + \\Omega_{d,4}(U_{7}^{(4,3)}(t))\\sigma_{4}^{X} \\\\ \\end{align}$$</td></tr><tr><td>meas_map</td><td>[[0, 1, 2, 3, 4]]</td></tr><tr><td>parametric_pulses</td><td>[]</td></tr><tr><td>coupling_map</td><td>[[0, 1], [1, 0], [1, 2], [2, 1], [2, 3], [3, 2], [3, 4], [4, 3]]</td></tr><tr><td>url</td><td>None</td></tr><tr><td>open_pulse</td><td>False</td></tr><tr><td>u_channel_lo</td><td>[[{'q': 1, 'scale': (1+0j)}], [{'q': 0, 'scale': (1+0j)}], [{'q': 2, 'scale': (1+0j)}], [{'q': 1, 'scale': (1+0j)}], [{'q': 3, 'scale': (1+0j)}], [{'q': 2, 'scale': (1+0j)}], [{'q': 4, 'scale': (1+0j)}], [{'q': 3, 'scale': (1+0j)}]]</td></tr><tr><td>dynamic_reprate_enabled</td><td>False</td></tr><tr><td>uchannels_enabled</td><td>True</td></tr><tr><td>allow_object_storage</td><td>True</td></tr><tr><td>meas_lo_range</td><td>[[6952624018.0, 7952624018.0], [6701014434.0, 7701014434.0], [6837452605.0, 7837452605.0], [6901770712.0, 7901770712.0], [6775814414.0, 7775814414.0]]</td></tr><tr><td>allow_q_object</td><td>True</td></tr><tr><td>credits_required</td><td>True</td></tr><tr><td>discriminators</td><td>['quadratic_discriminator', 'linear_discriminator']</td></tr><tr><td>sample_name</td><td>Snake</td></tr><tr><td>qubit_lo_range</td><td>[[4333430597.562168, 5333430597.562168], [4123852322.963768, 5123852322.963768], [4320531275.264617, 5320531275.264617], [4242331971.9375987, 5242331971.937599], [4316318056.924669, 5316318056.924669]]</td></tr><tr><td>qubit_channel_mapping</td><td>[['d0', 'm0', 'u0', 'u1'], ['m1', 'u1', 'u3', 'u2', 'u0', 'd1'], ['m2', 'u3', 'u2', 'u4', 'u5', 'd2'], ['m3', 'd3', 'u4', 'u5', 'u7', 'u6'], ['u7', 'd4', 'm4', 'u6']]</td></tr><tr><td>description</td><td>5 qubit device</td></tr><tr><td>conditional_latency</td><td>[]</td></tr><tr><td>backend_name</td><td>ibmq_santiago</td></tr><tr><td>dt</td><td>2.2222222222222221e-10</td></tr><tr><td>rep_times</td><td>[0.001]</td></tr><tr><td>online_date</td><td>2020-06-03 04:00:00+00:00</td></tr><tr><td>conditional</td><td>False</td></tr><tr><td>n_uchannels</td><td>8</td></tr><tr><td>n_registers</td><td>1</td></tr><tr><td>acquisition_latency</td><td>[]</td></tr><tr><td>channels</td><td>{'acquire0': {'operates': {'qubits': [0]}, 'purpose': 'acquire', 'type': 'acquire'}, 'acquire1': {'operates': {'qubits': [1]}, 'purpose': 'acquire', 'type': 'acquire'}, 'acquire2': {'operates': {'qubits': [2]}, 'purpose': 'acquire', 'type': 'acquire'}, 'acquire3': {'operates': {'qubits': [3]}, 'purpose': 'acquire', 'type': 'acquire'}, 'acquire4': {'operates': {'qubits': [4]}, 'purpose': 'acquire', 'type': 'acquire'}, 'd0': {'operates': 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\n",
+          "text/plain": "<Figure size 360x360 with 1 Axes>"
+         },
+         "metadata": {},
+         "output_type": "display_data"
+        }
+       ]
+      }
+     },
+     "64caade196d54d109a568450711460ed": {
+      "model_module": "@jupyter-widgets/output",
+      "model_module_version": "1.0.0",
+      "model_name": "OutputModel",
+      "state": {
+       "layout": "IPY_MODEL_be167568476440688a87221c8e63add6",
+       "outputs": [
+        {
+         "data": {
+          "image/png": 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\n",
+          "text/plain": "<Figure size 360x360 with 1 Axes>"
+         },
+         "metadata": {},
+         "output_type": "display_data"
+        }
+       ]
+      }
+     },
+     "65049b766bbf4b03aeb15a960df21c98": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "1.5.0",
+      "model_name": "DescriptionStyleModel",
+      "state": {
+       "description_width": ""
+      }
+     },
+     "653b8e47e27d4233bd0d50f70b3428dc": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "1.5.0",
+      "model_name": "HTMLModel",
+      "state": {
+       "layout": "IPY_MODEL_fc37f2e31db4496398069eb6dd7fda7c",
+       "style": "IPY_MODEL_5a781324e51e4e8ab281f83ebf88a00b",
+       "value": "<h5>Backend</h5>"
+      }
+     },
+     "6cdc4c3bbab049199bec2a883bec1541": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "1.5.0",
+      "model_name": "DescriptionStyleModel",
+      "state": {
+       "description_width": ""
+      }
+     },
+     "6d17a02efb004ff286f2eb62fee9c92d": {
+      "model_module": "@jupyter-widgets/base",
+      "model_module_version": "1.2.0",
+      "model_name": "LayoutModel",
+      "state": {
+       "grid_template_areas": "\n                                       \". . . . right \"\n                                        ",
+       "grid_template_columns": "20% 20% 20% 20% 20%",
+       "width": "100%"
+      }
+     },
+     "6d4768dbf82e4b54ba7abdc12a3a2192": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "1.5.0",
+      "model_name": "TabModel",
+      "state": {
+       "layout": "IPY_MODEL_0d13b3b361964663bd51b891cc017b41"
+      }
+     },
+     "6fd818bc635e492aa1cb81eacc10ef0b": {
+      "model_module": "@jupyter-widgets/base",
+      "model_module_version": "1.2.0",
+      "model_name": "LayoutModel",
+      "state": {
+       "grid_gap": "0px 0px",
+       "grid_template_areas": "\n                                                   \"top top top\"\n                                                   \"left middle right\"\n                                                   ",
+       "grid_template_columns": "33% 33% 33%",
+       "grid_template_rows": "auto auto"
+      }
+     },
+     "71fea12ba96e44168cd4fae223e18325": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "1.5.0",
+      "model_name": "HTMLMathModel",
+      "state": {
+       "layout": "IPY_MODEL_3c46028c3e90439b870ba4b46d20f7b3",
+       "style": "IPY_MODEL_35b754fef960488e81170f88445c7139",
+       "value": "<table><style>\ntable {\n    border-collapse: collapse;\n    width: auto;\n}\n\nth, td {\n    text-align: left;\n    padding: 8px;\n}\n\ntr:nth-child(even) {background-color: #f6f6f6;}\n</style><tr><th>Property</th><th>Value</th></tr><tr><td><font style='font-weight:bold'>n_qubits</font></td><td>5</td></tr><tr><td><font style='font-weight:bold'>quantum_volume</font></td><td>32</td></tr><tr><td><font style='font-weight:bold'>operational</font></td><td>True</td></tr><tr><td><font style='font-weight:bold'>status_msg</font></td><td>active</td></tr><tr><td><font style='font-weight:bold'>pending_jobs</font></td><td>0</td></tr><tr><td><font style='font-weight:bold'>backend_version</font></td><td>1.0.0</td></tr><tr><td><font style='font-weight:bold'>basis_gates</font></td><td>['id', 'u1', 'u2', 'u3', 'cx']</td></tr><tr><td><font style='font-weight:bold'>max_shots</font></td><td>8192</td></tr><tr><td><font style='font-weight:bold'>max_experiments</font></td><td>75</td></tr></table>"
+      }
+     },
+     "72973bca5f6d4116adc11ae473be0ddc": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "1.5.0",
+      "model_name": "HTMLModel",
+      "state": {
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JrWMPoaS2ZxiUpNYyEEpqa4ZBSWo9A6GktmUYlKT+4TGEBdl9993LLkEaVAyDktR/IqWWb0vdWNcnyi5AahftFAaP2OsD6yxj5s3f9vMqaVBxl7GkttJOYVCSqsJAWJBjjz2WY489tuwypAHNMChJ5fAYwoI8+eSTZZcgDWiGQUkqjz2EkkpnGJSkchkIJZXKMChJ5TMQSiqNYVCS2oPHEBZkypQpZZcgDSiGQUlqHwbCgnzkIx8puwRpwDAMSlJ7cZexpH5lGJSk9mMgLMhRRx3FUUcdVXYZUlszDEpSe3KXcUGWL19edglSWzMMSlL7sodQUssZBiWpvbW8h3D+9de1ehFtYdkTTwD1r+92BxzYynKktmEYlKT2Zw+hpJYxDErSwOAxhAV5xaRdyi5BaiuGQUkaOAyEBXn7615XdglS2zAMStLA4i5jSYUyDErSwGMgLMjHzzyLj595VtllSKUyDErSwGQglFQIw6AkDVwGQkl9ZhiUpIHNk0ok9UkeBv8A7N1DE8OgJLU5A6GkpuVh8M/Abj00MQxK0gBgICzI1Ml7ll2C1K8Mg5I0eBgIC3LYfvuVXYLUb/Iw+Dt6DoNnYRiUpAHDk0oKsnzFSpavWFl2GVLL1ZxAsk8PTWYDnzIMStLAYSAsyPHnnMPx55xTdhlSS9VxNvFsYH/DoCQNLAZCSXUxDErS4GUglNSrOsLglzEMStKAZSCUtF51XnT6OMOgJA1cBkJJPfIXSCSpGrzsTEEOmtLTCZfSwGQYlKTqMBAW5KApU8ouQSqMYVCSqsVdxgV56pnFPPXM4rLLkPrMMChJ1WMgLMgp06dzyvTpZZch9YlhUJKqqaFAGBEfjoi5EbEiIi5qUU2SSmAYlKTqavQYwgeBLwAHAaOKL0dSGQyDklRtDQXClNKPACJiMrBlSyqS1K8Mg5IkjyGUKswwKEmCFgXCiPhgfqzh3JnXXNOKRbSdN059NW+c+uqyy5DqZhiUJHVqyXUIU0rfAr4FMP/66yrxz2T/vfYquwSpboZBSVItdxkX5NFFi3h00aKyy5B6ZRiUJHXV6GVnhkXESGAoMDQiRkaEv3YCfGnGd/jSjO+UXYa0XoZBSVJ3Gu0h/BywDPgs8K78/ueKLkpS8QyDkqSeNHrZmZOBk1tSiaSWMQxKktbHYwilQc4wKEnqjYFQGsQMg5KkenhCSEHeduABZZcgrcUwKEmql4GwIPvsumvZJUjPMgxKkhrhLuOCLHz4YRY+/HDZZUiGQUlSwwyEBTn7kks5+5JLyy5DFWcYlCQ1w0AoDRKGQUlSswyE0iBgGJQk9YWBUBrgDIOSpL4yEEoDmGFQklQELztTkCMPPbTsElQxhkFJUlEMhAXZc6cdyy5BFWIYlCQVyV3GBbl74ULuXriw7DJUAYZBSVLRDIQFOe+yyznvssvLLkODnGFQktQKBkJpgDAMSpJaxUAoDQCGQUlSKxkIpTZnGJQktZqBUGpjhkFJUn/wsjMFef9b3lx2CRpkDIOSpP7S8kC43QEHtnoRbaEq66n+YRiUJPUndxkX5JZbbuGWW24puwwNAoZBSVJ/i5Ra/j+lEv+0jjjiCABmzpzZ7CyisGJUutMPO2lbYArwCmAPYBSwGngAuBGYC/zhuKtOWVk7nWGwPVx2zNlbkb1+L3/8mSXThg0dQkqJFavX8PjiJbxsiwmvBeYcPn3a8pJLVTcWzZs7AXglsDfwcmAc2f+ix4CbgJuBOeMnTV5cWpFSmzEQFsRAqNMPO2kYcBjwGeBlZAFwLOu+tivyWwLOA8477qpT7jcMluuyY84eAhxC9vrtBawke/3W2pOypqODoUOGPJ0P/zZwzuHTp83v53LVxaJ5cwN4DfApYD+yz9g6rx+wClgKjAAuAc4eP2nynf1XqdSeDIQFMRBW2+mHnfQy4IfAlmT/hOq1AliTUjrxstt+OzGRPtJDO8NgC112zNnbA5cBL6ax128lsAY4E/jfw6dPW9WC8tSLRfPmbkEW7iYDY6h/e7qaLCBeAHx2/KTJy1pTodT+DIQFMRBW0+mHnRTAJ8gC20iafB3XdHSsemr5kuGz5/+V5atXdh1tGGyhy445+/3AN4ANgKFNzmYpcD9w8OHTp/2rqNrUu0Xz5r4VuIjs9Rve5GyWAv8GDh0/afLfCipNGlA8qURqUh4GzwROITtGsOlQP3TIkOEbjRzDwS/Zk1HDN6gdZRhsocuOOfszZGFwNM2HQfLpXwTMveyYs19cRG3q3aJ5c48GLibr1W02DEL2+m0J/G7RvLl7FFGbNNDYQ1iQO+64A4Cddtqp2VnYQzjAnH7YSZ8BPk+2i6oQHR0dLFu9kll/v5nVHWsMgy102TFnHwV8kywMFKWDrKdpl8OnT3ukwPmqi0Xz5r4JmEmxrx/AU8Du4ydNtqdXlWIgbB8GwgHk9MNO2hX4PVnPYKFWd6zhiaWLb7v+7lt3Nwy2xmXHnL0t8DeKDxOQHVd4A3Do4dOn+fq1wKJ5c18I/BPYsAWzXwPcCrx8/KTJHS2Yv9SW3GVckJtuuombbrqp7DLUD04/7KThwOVkxwwWbtiQoWwyZsMdjthtv9e2Yv5Vd9kxZwfwA7JjzlphBLAv8PYWzV8wgxZ9/sgOHdgR+FCL5i+1JQNhQc4991zOPffcsstQ//gPYHNa2KsbEaOBr7dq/hX3GmAn+nbMYG/GAF/NL2WjAi2aN3c3YH+y4N0qY4AvLJo3t1VfGqS248ZKatxnaOzSJM3a5vTDTtqzH5ZTNZ+iwOM+12NDwF7e4k2jtWGwU5B9+ZMqoeW/ZSwNJqcfdtIOwEvrbT90+DBe877XseOrdmbEqA14ZP5D/GrGL3jwHw/UM/kGZLutjm6yXHVx2TFnv4DsosV19e5OftcBbPKizRk9fhwdq9fw7389zG1X3sjTD/27nsnHAB8Drmu2Xq1t0by5o8h2xdf1v2vMltswfNyGxNBhpI41rF62lGUP3c+a5XVdbnAc8HGyE1ekQa/lgXD+9QN7W7jdAQeWXYLayyvJDjqvywEfOJg9Dt6LR+99hAXz/sWOr9qZd5zyHs7/4NdZ9szS3iYfSrZrTMV5ObCcOo8ffNG+u/Dv+Q9x3813semOW7P5Ltuy8ZabcM3nvkPH6l7fBgHs08d6tbZdyU7aqev4wSEjNmDV4mdIHWsYPnZDRozbiKEbjOSpv/+13uVNWjRv7tDxkybX/ZmXBip3GUuN2Yc6dxeP3mgMu752dzrWdDDzxO9y1VlXcPvsv7LB6JHs+fq9613elqcfdlLhZzJX2F40sLv4+i//gOu//APmXnI9v/7qFQCMft44Ntrs+fXOYvRlx5y9aeNlqgd70sD1Bp+ZfxdLFv6LpQ/cx+L7sl8XHDJ8BA0c/ruSBvYISAOZu4wLctppp5VdgvrH7vU23GTrFzB0+DCefOQJlj61BICH7n6Ql+2/K5tuO6He2SwDtgfq7tLQeu1BA9u9f89/6Nn7Q4Zm3587OjpYlr+evRk2YugGux6608OL5s1trEoVZoPnv4ChG4xi+LjsCjXLH3uEBq6G1kF2xvHtralOah8GwoJst912ZZeg/lH3devGbJx1JK5c9txP0a1akd0f87y6z0npaGSZ6lVTJwMN22A4L3/fQQDcdd2fWf50fYEQYM0qL2VXphEbjWf42HEArFm5ktVLFzcy+RD8/KkiDIQF+dWvfgXAa1/rSYWD3Op6Gy55MvvHM2LUcydEjhiZ3V/yREP/lFY10ljrVffr12nEmJG8+iNv4fnbTuCeG//KvB/d2ND0Q4Z4zfkyPTP/Lohg+LiNGDvxRYyduB1P/f1vdKxa5zfDu5No4j0jDUQGwoJ8+9vfBgyEFfAw2YHtvXr8vsdYs2o1G26yEaM3GsPSp5aw2Q5bAPDovXX/qtkI4LGmKlV37m+k8ejx45j6P//BhhPGc8fP/8Rff/K7hhaWEowY1Zef2FXTIrIXACAlVj3zFKljDUOGDmPIiA3qDYQdwKMtrFJqGwZCqTG/Jbuwca//5Zc+tYR5N/yF3Q+azDv/9708dt+j7PjKnVmxbAVzr/ljvctbTYMhRus1B3gbdZ5Y8tpPv4PRzxvLkn8/zbARw9j97VMBWPCnv7OojlDfsaaDMePd41iGYaPHMnbrbVm1ZDFpzWqGjRnHkKHD6Fi9ijXLej3Dv9Mo4M8tLFNqGwZCqTE3k53oUVe3z/Xf/jkdazrY8ZU787zNxvPAP+7nhhm/ZNnTdf9D+utxV53i7+EWZy5Zr09dRufHeo55/oa8+LV7PDv8iYWP1RUIU0e6a5Pd9/Ys1YIsmjd3a+Dv1PEb4h2rVrJmxQqGj92QGDKEtGY1K55cxPJHHiR11H0VmSfHT5q8qC81SwOFgVBqzE00cLmm1StXc+30a7h2+jXNLGsx8L1mJlSP5pFdh3BcPY0vO+bsvixrGXBpX2agdSwk6zHfobeGHStXZMcPNm8V8MO+zEAaSLwOodSA4646ZRlwIdn1yVptCAaKQh0+fdoa4GyysNZqAUzvh+VUxvhJkxNwBtmXpVZbjb8nrgqxh7AgZ511VtklqP98A/hgi5exHPjOcVed0h//+Krm28DnWryMlcDVh0+f5gkJxZsJnNniZawG/jR+0uR/tng5Utuwh7Agm2++OZtvvnnZZagfHHfVKfPJeinqvxhd454Bjmvh/Cvr8OnTHgM+SWtfv+Vkv0Otgo2fNHkJ8IE1azpa2Uu/EjiqhfOX2o6BsCBXX301V199ddllqP+cRnYsUyt+43Qp8M7jrjrlmRbMW5npZL/+0opQsRT4T3sHW+fMT1256b13PTJi9aqW/MTwEuAz4ydN/lcrZi61KwNhQS699FIuvdTDvariuKtOWQW8DlhEA2et1mEpcPJxV51yfYHzVBeHT5/WAbyJ7LqSRV74ewlw7uHTp11e4DxV4/iDjvsQ8M1rvv8nnlq0hNWrCw2FS8lOJDm3yJlKA4GBUGrScVedch/wCrIL164oYJadYfArBcxLvch3Hb+C7MzV5QXMcilwHvCZAualbnSGQYCVK1bz/XN/wxOPPsOqlYX8mMgS4DLgA/nJK1KlGAilPsiPJ9wZuIbmj0lbDjwOvMUw2L8Onz7tIWA3shMV6r44ZBcrgKeAdx8+fdqnD58+zTDRArVhsNOKZau45Jxfc88dD92VUmr2zPFVZGctfwh4//hJk1uyH1pqdwZCqY+Ou+qURcdddcpbgSOB+WT/XOrZjbyE7PInFwEvOu6qU65tWZHq0eHTpz1z+PRpR5PtQr6DLBjWEwqWkoX5HwLbHz592o9aV2W1dRcGO61Z3THz6kv/9LKIeA3Zr4oso77fH15G9vpdDbx0/KTJ37VnUFUWKbX2/T//+usG9AdsuwMOrKvdEUccAcDMmTObXVQ0O6Hax+mHnRTAPsBHgFcDL2DtnqfOXzi5nSwIXuzJI+3lsmPO3pPs9dsf2IwsOKT8NhwYSvZrGZcCMw6fPs1fsmih9YVBsp7d93zxl6c/GwAXzZu7M/BR4EBgK7LPX+f/oaFkr+E/gCuA6eMnTa77h8WlwcxA2It6A+GiRdn/hPHjxze7KAPhIHT6YSeNA14MjCbbNfUo8C9/jm5guOyYs8cALyH77ePVZLv278lPSlGLNRoGu1o0b+4ostdvLFmv/SLgn+4WltZlIOxFvYGwAAZCScr1NQxKaozHEBbkiiuu4Iorrii7DEka8AyDUv8zEBbkyiuv5Morryy7DEka0AyDUjnqDoQRsUFEXBgRCyLimYi4NSIOaWVxkqTqMAxK5Wmkh3AY2QVcpwIbAZ8HLo+IbVpQlySpQgyDUrmG1dswpbQEOLlm0NUR8S9gT+DeYsuSJFWFYVAqX9PHEEbEpmSX07i9m3EfjIi5ETF35jXX9KU+SdIgZhiU2kNTl52JiOHAz4F7UkrHrK9tVS47s2xZ9qtJo0aNanZRXnZGUqUYBqX2Ufcu404RMQS4GFgJfLjwigaoPgRBSaocw6DUXhoKhBERwIXApsChKaVVLalqALr44osBePe7311yJZLU3gyDUvtp9BjC84EdgTemlJa1oJ4Ba9asWcyaNavsMiSprRkGpfbUyHUIJwLHALsBD0fE4vx2ZKuKkyQNHoZBqX01ctmZBXjigySpCYZBqb3503WSpJYyDErtz0AoSWoZw6A0MDR82Rl1b+bMmWWXIEltxTAoDRz2EEqSCmcYlAYWA2FBLrjgAi644IKyy5Ck0hkGpYHHQFiQG264gRtuuKHsMiSpVIZBaWAyEEqSCmEYlAYuA6Ekqc8Mg9LAZiCUJPWJYVAa+LzsTEFGjhxZdgmS1O8Mg9LgYCAsyHe+852yS5CkfmUYlAYPdxlLkhpmGJQGFwNhQc455xzOOeecssuQpJYzDEqDj4GwIHPmzGHOnDlllyFJLWUYlAYnA6EkqS6GQWnwMhBKknplGJQGNwOhJGm9DIPS4OdlZwqy8cYbl12CJBXOMChVg4GwIOeff37ZJUhSoQyDUnVESqnVy2j5AgaJKLsASepkGJSqxWMIC3LGGWdwxhlnlF2GJPWZYVCqHncZF+TWW28tuwRJ6jPDoFRN9hBKkgDDoFRlBkJJkmFQqjh3GUtSxR1/0HGXAu/sYbRhUKoAA2FBJkyYUHYJktSw4w867jLg7T2MNgxKFeFlZ9qHl52R1K8Mg5I6eQyhJFXQ8Qcd9z16DoM/xTAoVYo9hAU59dRTATjxxBObnYU9hJL6RS8nkCwAXvLFX56+oh9LklQyjyEsyJ133ll2CZLUK8OgpO64y1iSKsIwKKknLe8hnH/9da1eRFtY9sQTQP3ru90BB7ayHElaSy9h8MfAEYZBqbrsIZSkQa6Oi06/3TAoVZvHEBZky01fWHYJkrQOf4FEUj0MhAX5+LvfXXYJkrQWw6CkernLWJIGIcOgpEYYCAvy1Ysv5qsXX1x2GZJkGJTUMHcZF+T+Rx4tuwRJMgxKaoo9hJI0SBgGJTXLQChJg4BhUFJfGAglaYAzDErqK48hLMj2W21ZdgmSKsgwKKkIBsKC/Pfhh5ddgqSKMQxKKoq7jCVpADIMSiqSgbAgp194IadfeGHZZUiqAMOgpKK5y7ggjz3xZNklSKoAw6CkVrCHUJIGCMOgpFYxEErSAGAYlNRKBkJJanOGQUmt5jGEBdlpu+3KLkHSIGQYlNQfDIQF+cB/vKXsEiQNMoZBSf3FXcaS1IYMg5L6k4GwICef/3+cfP7/lV2GpEHAMCipvzUUCCPikoh4KCKejoh/RMQHWlXYQPP0kiU8vWRJ2WVIGuAMg5LK0GgP4enANimlDYE3AV+IiD2LL0uSqscwKKksDQXClNLtKaUVnQ/z24sKr0qSKsYwKKlMDR9DGBHnRcRS4O/AQ8CswquSpAoxDEoqW8OBMKX038A4YF/gR8CKrm0i4oMRMTci5s685pq+VzkA7PHSl7LHS19adhmSBhjDoKR2ECml5ieO+D/gjpTSN3pqM//665pfwCC23QEHdh0UZdQhqTyGQUntoq+XnRmGxxBKUsMMg5LaSd2BMCJeGBHviIixETE0Ig4CjgBuaF15A8dxX/8Gx329x45SSXqWYVBSu2nkp+sScCzwf2RBcgHwsZTSVa0obKBZsWpV2SVIGgAMg5LaUd2BMKX0GDC1hbVI0qBmGJTUrvzpOknqB4ZBSe3MQChJLWYYlNTuGjmGUOvxikm7lF2CpDZkGJQ0EBgIC/L2172u7BIktRnDoKSBwl3GktQChkFJA4mBsCAfP/MsPn7mWWWXIakNGAYlDTTuMpakLuacNmMjYDKwJ7AX2e+3rwEeAP4AzAVun3LC0Wu6TmsYlDQQGQglCZhz2owA9gY+CbwBWAGMAkZ0aXpE/nfZnNNmnA1cMOWEox8Hw6CkgctAKKny5pw2YwLwXeCVZCFwCDCyh+Zja/5+HvjcnNNmfPaaG/+ZgHN6mMYwKKmteQyhpEqbc9qMtwD/APYHxtDYdnEUMLqjI535yl23OmeD4UO7a2MYlNT27CEsyNTJe5ZdgqQGzTltxtFku3hH9WU+Q4bEiA3HbsC+e2zNTX9ZyPIVz2Y/w6CkASFSSi1dwPzrr2vtAgao7Q44sOugKKMOqarmnDbjP4BL6GMYrNXR0cGKlWuYfct9rF7TYRiUNGC4y7ggy1esZPmKlWWXIakOc06bsRlwEQWGQYAhQ4YwYvhQJu+82QIMg5IGEANhQY4/5xyOP6en48kltYv8bOKL6fmkkT4ZOnQIz99o1Ave8OodDmrF/CWpFQyEkqpmH+AVwPBWLSAiRgPn5uFTktqegVBS1XyKgncV9+D5wKv6YTmS1GeeZSypMuacNuN5wCE0+GV4k52248Vv2Q+AB/90O/de98d6JhsDfAy4sZFlSVIZ7CGUVCUvB5Y3MsGIcaPZ7uB96FjT0eiyAnh1oxNJUhkMhAU5aMo+HDRln7LLkLR+k4HRjUyw/RtfzcrFS1n093ubWd6Gc06b8fxmJpSk/tTyXcbdXG9vUDq2IuspDXB708DJJJvtvTMbbrUp8y76GZvvtXMzy1sGvAyY3czEktRf7CEsyKJFi1i0aFHZZUhav3H1Nhz9go2ZuP+eLPztn1n6SJ8+22N7byJJ5fKkkoJ86EMfAmDmzJklVyJpPdbU23D8S7chhg5lw60nMG6rTRmz6fhs+A5b07FqNff95pZ6Z+XFqSW1PQOhpCp5qN6GQRARPG/7rdYaPvJ54xi35Qvrnw08Xn95klQOA6GkKpkD/Ad1nFiy8MZbWXjjrc8+3v4N+/LCXXdo5LIzkF3v8G/NFCpJ/cljCCVVyVz6dxfufVNOOHpFPy5PkppiD6GkKvkL0PAFBQHuvvpG7r66oWtMLwcub2ZZktTf7CEsyJFHHsmRRx5ZdhmS1mPKCUevAr5Jgxen7oPz+mk5ktQnkVJq9TJavoBBIsouQKqCOafN2By4m9b+nvFq4NopJxz9+hYuQ5IKYw9hQR588EEefPDBssuQ1IspJxz9IPC/wJIWLmYF8F8tnL8kFcpAWJBPfOITfOITnyi7DEn1OQP4Fw1cl7ABS4CPTTnh6IUtmLcktYSBUFLlTDnh6DXA64FFNHmSSQ+Wkp1IcmGB85SkljMQSqqkKSccfR+wD/AYsLKAWS4BLgP+c8oJR3vstKQBpeWXnZl//XWtXkRbWPbEE8C667vdAQeWUY6kOkw54eh75pw2YxfgO8D+1HHB6m6sJjtm8BPAtwyDkgYir0MoqdKmnHD0Y8Ab5pw2423AN4AxwFh6P/N/Jdnu5t+S9Qre19JCJamFWn7ZmfnXX1eJb8u/v+02APbZdde1hjfQQ+hlZ6SSzTltxhDgAGAaMAUYQdb7V2sM8CDZsYLfnHLC0Qv6tUhJagEDYYsZCKWBac5pMwLYHNiZLASuBh4H5k054ehWXrJGkvqdu4wLsvDhhwHYasKEkiuRVIT8WMAH8pskDWqeZVyQsy+5lLMvubTsMiRJkhpmIJQkSao4A6EkSVLFGQglSZIqzkAoSZJUcZ5lXJAjDz207BIkSZKaYiAsyJ477Vh2CZIkSU1xl3FB7l64kLsXLiy7DEmSpIYZCAty3mWXc95ll5ddhiRJUsMMhJIkSRVnIJQkSao4A6EkSVLFGQglSZIqzsvOFOT9b3lz2SVIkiQ1xUBYkJ1f9KKyS5AkSWqKu4wLcvs993D7PfeUXYYkSVLDDIQFufDHP+HCH/+k7DIkSZIa1lQgjIgdImJ5RFxSdEGSJEnqX832EJ4L3FxkIZIkSSpHw4EwIt4BPAn8qvBqJEmS1O8aCoQRsSFwKvCJXtp9MCLmRsTcmddc05f6JEmS1GKNXnbmf4ELU0oLI6LHRimlbwHfAph//XWp+fIGjv8+/O1llyBJktSUugNhROwGHADs3rJqBrDtt9qq7BIkSZKa0kgP4X7ANsB9ee/gWGBoROyUUtqj+NIGllvuuBOAPXfaseRKJEmSGtNIIPwW8IOax58kC4jHFlnQQHXprFmAgVCSJA08dQfClNJSYGnn44hYDCxPKT3WisIkSZLUP5r+LeOU0skF1iFJkqSS+NN1kiRJFWcglCRJqrimdxlrbdPedWTZJUiSJDXFQFiQrSZMKLsESZKkprjLuCC/v+02fn/bbWWXIUmS1DB7CAvyw+uuB2CfXXctuRJJkqTG2EMoSZJUcQZCSZKkijMQSpIkVZyBUJIkqeI8qaQgnz36qLJLkCRJaoqBsCAvHD++7BIkSZKa4i7jgvz65pv59c03l12GJElSw+whLMjPZv8WgP332qvkSiRJkhpjD6EkSVLFGQglSZIqzkAoSZJUcQZCSZKkivOkkoKcdMwxZZcgSZLUlEgptXoZLV/AIBFlFyBJkqrJXcYFueKKK7jiiivKLkOSJKlhBsKCXHnllVx55ZVllyFJktQwA6EkSVLFGQglSZIqzkAoSZJUcQZCSZKkivOyMwVZtmwZAKNGjWp2Fl52RpIklcJA2D4MhJIkqRTuMi7IxRdfzMUXX1x2GZIkSQ0zEBZk1qxZzJo1q+wyJEmSGmYglCRJqjgDoSRJUsUZCCVJkirOQChJklRxLb/sTER8MKX0rZYupE00sq5Vel4kSVJ7648ewg/2wzLaRSPrWqXnRZIktTF3GUuSJFWcgVCSJKni+iMQVuk4uUbWtUrPiyRJamP98VvGkiRJamPuMpYkSao4A6EkSVLFNR0II+LgiLgrIu6OiM92Mz4i4hv5+HkRsUe907abiJgREY9GxN96GP+piPhLfvt7RKR8/e6KiH9GxJ0RcXtE/E/efn3Pzb0R8dd8XnP7ax0lSVJ1NXUMYUQMBf4BHAjcD9wMHJFSuqOmzaHAR4BDgZcDX08pvbyeadtNRLwaWAx8L6X0sl7avgf4cEpp74jYHvgVcAiwELgFeDOwDd08N/n09wKTU0qPt2RlJEmSumi2h3Bv4O6U0vyU0krgB8BhXdocRhagUkrpD8DGEbFZndO2lZTSb4FFdTY/GLggn+5u4C/AFimlZ4A7gS3o+bmRJEnqd80Gwi3Ierw63Z8Pq6dNPdMOSBExmiwQXpk/3gbYHfhj7X3W/xwk4NqIuCUi/DUTSZLUcsOanC66GdZ133NPbeqZdqB6I/C7lNKiiBhLFgw/BnR03k8pPR0R63sOXplSejAiXghcFxF/z3soJUmSWqLZHsL7ga1qHm8JPFhnm3qmHajeAcyMiOFkAfBS4Ged91NKP8rb9fgcpJQ6/z4K/JhsF7skSVLLNBsIbwZ2iIhtI2IEWRD6aZc2PwXek59R+wrgqZTSQ3VOO+BExEbAVOAq4EKy4wXP7ryfUvpqTfNun5uIGBMR4/L5jQFeB3R7ZrMkSVJRmtplnFJaHREfBn4JDAVmpJRuj4j/ysf/HzCL7Czau4GlwFHrm7bPa9JCETET2A/YJCLuB04ChsOz6wrwFuBasuME3w38lexs6u2BeyPirXm7Y+jhuQE2BX6c71EeBnw/pfSLVq6bJEmSP10nSZJUcf5SiSRJUsUZCCVJkirOQChJklRxBkJJkqSKMxBKkiRVnIFQkiSp4gyEkiRJFWcglCRJqjgDoSRJUsUZCCVJkirOQChJklRxBkJJkqSKMxBKkiRVnIFQkiSp4gyEkiRJFWcglCRJqjgDoSRJUsUZCNWjiNgpIuY2Oe1HI+JL6xm/TUSkiFgcER9svkpFxIvz53FNRHyg7HoktQ+34wNDO2zHKxcII+LeiDigy7D3RcRNZdXUChFxckRc0sfZ/C9wZs08vxYRT0TE7yNii5rhR0bE17tM+y3gXRHxwl6WsXFK6Vtdat82Ijoi4rw+1t9yETEtIh6OiKciYkZEbLCetrtFxC0RsTT/u1vNuPfmw56OiPsj4oyIGFYz/jcRsTzfYCyOiLs6x6WU/pFSGgvc2Kr1lBoREe+MiLn5e/WhiPh5RLwqH3dyHiLeVtN+WD5sm5phUyLihoh4Jv98/SwidsrHHVnzWViWby86Hy/u9xVuEbfj/aPe7XhE7Fv7PstvKSLemo/vbTv+4fxzsSIiLqqddztsxysXCFstIoZ2eTysp7Y9TN9r+0bn2YyI2AzYH/hJ/nhvYE9gAnATcFw+fCPgk8CJtdOnlJYDPwfe08Ti3wM8AbxjfQGrbBFxEPBZ4LXANsB2wCk9tB0BXAVcAjwP+C5wVT4cYDTwMWAT4OX5PD/ZZTYfTimNzW8vKXRlpIJExMeBrwFfBDYFtgbOAw6rabYIOLXr9rJmHvsA15J9ZjYHtgVuA34XEdullC7t/CwAhwAP1nw2xhawDm7HcTveVUrpxi7vszcAi4Ff5E16244/CHwBmFH4ihTAQFiHiHhpRFwXEYsi4q6IeHvNuIsi4vyImBURS4D9I+uF/ExEzAOW5N9+3xQRt0fEk3lvz44181infTc1pIj4UET8E/hnPuzrEbEw/zZyS0Tsmw8/GDgeODz/BnNbPnyjiLgw/8b+QER8oacNMnAg8Od8gwDZBvmmlNIK4FdkHxqA04CvpJSe6mYevwFeX9eTvLb3AJ8DVgFvzGt/R3TZ7ZF/q/tpfv/5eQ/C0xFxc75ure71fS9wYUrp9pTSE2TfxN/XQ9v9gGHA11JKK1JK3wACeA1ASun8fGOzMqX0AHAp8MoW1y8VKg8WpwIfSin9KKW0JKW0KqX0s5TSp2qa/gJYCbyrh1mdAXwvpfT1lNIzKaVFKaXPAX8ATm6yNrfjbse708h2vLtpr0gpLYHet+P5Z+InwL+LXIGiGAh7ERFjgOuA7wMvBI4AzouInWuavZPsAzWO7FsXebvXAxuTfehmkn1zeAEwC/hZTe/QWu1TSqt7KOfNZN86dsof3wzsBozP6/thRIxMKf2C7Nv5Zfk3mV3z9t8FVgPbA7sDrwN6OlZhF+Cumse3A/tGxCiybz23R8Rk4CUppe/3MI87gV17GNetfGO4JfAD4HKe+2b6U+AlEbFDTfN3kq03wLnAErJvvu/Nb/Uu81X5Br6n26t6mHRnsl6LTrcBm0bE83toOy+llGqGzcuHd+fVZM95rdMj4vGI+F1E7Nfrikn9bx9gJPDjXtol4PPASRExvHZERIwGpgA/7Ga6y8lCTkPcjj/L7fi6GtmO1y5vNPD/yF6PnnS3HW9bVQ2EP6l9o5DtzujJG4B7U0rfSSmtTin9GbiS7I3Q6aqU0u9SSh0138S+kVJamFJaBhwOXJNSui6ltIrseI5RZBs9umnfk9Pzb8rLAFJKl6SU/p3XdRawAdDtrsSI2JRs18rH8m/tjwJnA+/oYVkbA890Pkgp/S1f7z+Q7QL6MvB14KORHXj824i4NCI2rpnHM8BG61mf7rwX+Hn+Te37wCER8cKU0lKy3UdH5OuzA/BS4Kf5t+O3AiellJamlO5g/R/StaSUbkopbbyeW0/fUMcCtd+oO++Pq6NtZ/t12kbEUcBkao77AT5D9g9pC7Ljen4WES/qdeWk/vV84PH1hKFnpZR+CjzGumFmPNn/poe6mewhst1xjXI7jtvxHiZtZDte663A48Ds7kb2sB1va1UNhG+ufaMA/72ethOBl3cJkEeSfYPptLCb6WqHbQ4s6HyQUurIx2/RQ/uerNUmIj4REXdGdiDsk2Qf2p42lhOB4cBDNesxnezbcneeoMsHIqV0dkpp15TS4WQbxxvJ3kMfJPu2eSfZsRidxrFuCOpR/q31bWTd7KSUfg/cR/YNErINyxH5/XcCP8k3MC8g2x1b+/zU83z21WJgw5rHnfefqaNtZ/u12kbEm4EvAYeklB7vHJ5S+mO+62xFSum7wO+AQ/tWvlS4fwObdLe7tAefA04g61Xs9ATQAWzWTfvNyP4JN8rt+HN1ux1fWyPb8VrvJTusIXUd0dN2vN1VNRA2YiEwu8s3jbEppWNr2qzzhugy7EGyDzIAERHAVsADvcyjx3nmXfKfAd4OPC8Ptk+RHZfW3fwWAiuATWrWY8OUUk+7LOcBL+5uRP4t9RiyY4VeRrYrdBXZro9JNU13ZO2u+N68hezDeF5kZ3w9TLax7dzdcC3ZP5vdyDYonbsZHiPbhbJlzby2qneh0f2ZY7W3fXuY9HbW3pWyK/BISqm740NuByblr32nSdTsTsiPGboAeGNK6a+9lJ147rWW2sXvgeVku0V7lVK6Dribmi/lKTse6/dkoaKrt5Md+9Yot+NduB1/ViPb8c5lbUV2XPj3uhnXyHa8rRgIe3c18OKIeHdEDM9ve0XNwcR1uBx4fUS8Nj9e5hNkH+o5fahrHNmH5zFgWEScyNrfch4BtomIIQAppYfIPohnRcSGETEkIl4UEVN7mP91wB4RMbKbcV8l79YH/gXsFRFjyT4g82vaTSU7Q61e7yU7+2oXsmNqdiM7IHe3iNgl3w11BfAVst1K1+Xrtgb4EXByRIyOiJfSwFlxqcuZY93ceroMwPeA90d2na/nkfV2XNRD298Aa8h2zWwQER/Oh98AEBGvIftG/daU0p9qJ4yIjSPioIgYGdmB7UeSHZvyy3rXUeoPKTsp4UTg3Ih4c/55HB4Rh0TEGT1MdgLw6S7DPgu8N9+NOS4inhcRXyA7RrHbM0B74XZ8XW7HM41sxzu9G5iTUrqnduD6tuP5+GH5azEUGNq5Ta93HVsupVSpG3AvcECXYe8jO/Oqp2leAlxD9qH9N9k/8d3ycRcBX6hjGW8B7iD79jcb2Hl97bupIQHb1zweClwIPE12XM2na+dDdizPTWS7DP6cD9sIOB+4P6/jVuAd61nmD4HDuwzbn+w4mtphX8uX8wdgy3zYyHw5m/Yw723ydRqWP96CbMO4SzdtZwFn5vf3zac7t0ubF+Sv0dNk33C/DPyqZvztwJEteD99nGyj/TTwHWCDmnE/B46vebw7cAuwDPgzsHvNuF/n67+45vbzmnW7mWwXxpP583xgN7X8BvhA2Z8xb97IdsfOJTtB4OH8szklH3cycEmX9rPyz/U2NcNelb+nF+efr2uAl3WzrP2A++uoye34c8Pcjq+93Lq34/mwvwPv72Y+PW7Ha977qcvt5C7z+A0lbccjL0BaR2QXgf0usHdq8I0SER8Btkopdf3m3zl+ItnZb8uBT6WULuhrvV3m/2VgQkqp7rPUBqrIDsy+GRgB/HdK6aJyK5LULtyODwztsB03EGpQyHcvjAD+CuxF9m30Aym75pMkqc25HS9X++y7lvpmHNk1wjYHHgXOIru8gSRpYHA7XiJ7CCVJkirOs4wlSZIqzkAoSZJUcS0/hnDcqPFpTccqaq+h++y96GZYzaNYZ2TU2W7dlutMEd0vt+tU0d3Muz6KnpaWt+txPbuvqbt20X3B684votvh0UOB0cNC153vuq9gD09Nl+dm7SHrjut5ed1UuNa4nqfpZqqeHvZQUKwzrmuz6HFd1hrc04y6q6euJ7T7z0/3bWvWZ903VJ3L6unJrqeG7M5td9z5y5TSwT3MQf2sjG1yq7fH6wxxm9xzTW6Tux/pNrn1gXBNxyp22GxPIoYwpPNNG8GQGEI8+0HJHj83rnP4kC6Pe2q39vwAhkQ8+8Str21tuyER3bTtaR6x9jzWGsda0zzbjq7TrF3r+sY9Vwd1zuO5dlkdz9UUwVrrstY8uiyn63S17aLHcWsPr51nd+vReb+7+a1Tb9fphvQ+D7qu15D1L6vzPkPWrbHzPhHrLLt2HNQsp9t6n9uaR2d9Q7o8zl6gZ6ehy/PJkC7tampivfMI6KGOtYdHD8vqZjk161zzJALw/F33aua3Z9UiZWyTW7k9rh3nNrmncWsPd5vsNpluuMtYkiSp4gyEkiRJFWcglCRJqjgDoSRJUsUZCCVJkirOQChJklRxBkJJkqSKMxBKkiRVnIFQkiSp4gyEkiRJFWcglCRJqjgDoSRJUsUZCCVJkirOQChJklRxBkJJkqSKMxBKkiRVXKSUWruAiF8Am7R0IeXaBHi87CL6ies6eLVyfR9PKR3conmrQYNomzwYP6ODcZ1gcK7XQF6nbrfJLQ+Eg11EzE0pTS67jv7gug5eVVtfDXyD8T07GNcJBud6DcZ1cpexJElSxRkIJUmSKs5A2HffKruAfuS6Dl5VW18NfIPxPTsY1wkG53oNunXyGEJJkqSKs4dQkiSp4gyENSLi4Ii4KyLujojPdjM+IuIb+fh5EbFHPnyriPh1RNwZEbdHxP/UTDM+Iq6LiH/mf5/Xn+vUk1asa820n4yIFBFtc2mLFr22u0XEHyLiLxExNyL27s916kkf1nVkRPwpIm7L1/WUmmm+EhF/z9v/OCI27sdV0iBXx3v2U/nn7C8R8beIWBMR49c3bTtse5tdr162OydHxAM10x06ENYpH3dvRPy1c5tZM02pr1UfXqeX1Az/S0Q8HREfy6cp9XVqSkrJW7bbfChwD7AdMAK4DdipS5tDgZ8DAbwC+GM+fDNgj/z+OOAfndMCZwCfze9/FvjyYF3XfNhWwC+BBcAmZa9ri1/ba4FDaqb/zQBf1wDG5veHA38EXpE/fh0wLL//5XZ4H3sbHLd63rNd2r8RuKG3acve9vZxvda33TkZ+ORAe63yx/d293+hzNeqr+vUZT4PAxPLfp2avdlD+Jy9gbtTSvNTSiuBHwCHdWlzGPC9lPkDsHFEbJZSeiil9GeAlNIzwJ3AFjXTfDe//13gzS1ej3q0al0BzgY+DbTTwamtWt8EbJjf3wh4sNUrUoe+rGtKKS3O2wzPbwkgpXRtSml1Pu4PwJYtXxNVRT3v2VpHADPrmLbsbW/T61XHdrYsfXmt1qfM16qodXotcE9KaUELauwXBsLnbAEsrHl8P+t+AHttExHbALuT9a4AbJpSegiyDznwwuJKblpL1jUi3gQ8kFK6reB6+6pVr+3HgK9ExELgTOC4wipuXp/WNSKGRsRfgEeB61JKf2RdR5P1MEpFqOc9C0BEjAYOBq6sY9qyt719Wa/acduw9nYH4MP54Rsz+nn3al/XKQHXRsQtEfHBmuFlvlaFvE7AO1g3KJb1OjXFQPic6GZY116u9baJiLFkb5SPpZSeLrC2ohW+rvkH5QTgxMKqLE6rXttjgWkppa2AacCFBdTaV31a15TSmpTSbmQ9gHtHxMvWmjDiBGA1cGnfS5WA+t6znd4I/C6ltKiJaftbX9Yrm0H3253zgRcBuwEPAWcVUm19+rpOr0wp7QEcAnwoIl5ddIFNKOJ1GgG8CfhhzeAyX6emGAifcz/Z8W+dtmTdXYA9tomI4WQf3EtTSj+qafNIRGyWt9mMrOelbK1Y1xcB2wK3RcS9efs/R8SEwqtvXKte2/cCnY9/SLbroWx9WtdOKaUngd+QfRsGICLeC7wBODLlB8lIBajnPdupay/M+qYte9vbl/XqcbuTUnok/+LWAVxA/253+rROKaUH87+PAj/mudrLfK36tE65Q4A/p5Qe6RxQ8uvUnLIPYmyXGzAMmE8WajoPLN25S5vXs/bB+H/KhwfwPeBr3cz3K6x9sOwZg3Vdu0x/L+1zUkmrXts7gf3y+68Fbhng6/oCYOP8/ijgRuAN+eODgTuAF5S9jt4G162e92zebiNgETCmnmnL3vb2cb3Wt93ZrOb+NOAHA2SdxgDjau7PAQ4u+7XqyzrVjPsBcFS7vE5NPxdlF9BON7KzL/9BdsbRCfmw/wL+K78fwLn5+L8Ck/PhryLrYp4H/CW/HZqPez7wK+Cf+d/xZa9nq9a1y/zvpU0CYQtf21cBt+QbkD8Ce5a9nn1c10nArfm6/g04sWaed5MdZ9P5HPxf2evpbfDcenvP5o/f190/1e6mzYeXvu1tdr162e5cnH9u5wE/pSZ4tPk6bZdvK28Dbm+n16qP77/RwL+BjboML/V1aubmL5VIkiRVnMcQSpIkVZyBUJIkqeIMhJIkSRVnIJQkSao4A6EkSVLFGQglSZIqzkAoSZJUcQZCSZKkivv/OBgeo/tbUWUAAAAASUVORK5CYII=\n",
+          "text/plain": "<Figure size 792x648 with 5 Axes>"
+         },
+         "metadata": {},
+         "output_type": "display_data"
+        }
+       ]
+      }
+     },
+     "d81092f433ee4dba907fd5391c09e91c": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "1.5.0",
+      "model_name": "GridBoxModel",
+      "state": {
+       "children": [
+        "IPY_MODEL_210adf4d3c9044e0bc35e00d1866633e"
+       ],
+       "layout": "IPY_MODEL_2bfbcf142f9b49fba35f254a3ca7e9d7"
+      }
+     },
+     "da2291afc4a64e4fb2b3a8015f58b053": {
+      "model_module": "@jupyter-widgets/base",
+      "model_module_version": "1.2.0",
+      "model_name": "LayoutModel",
+      "state": {
+       "max_height": "620px"
+      }
+     },
+     "da5e5e0e851d4d569cb624222429191c": {
+      "model_module": "@jupyter-widgets/output",
+      "model_module_version": "1.0.0",
+      "model_name": "OutputModel",
+      "state": {
+       "layout": "IPY_MODEL_8e6f6815731f449284f9c00b40ae9ab5",
+       "outputs": [
+        {
+         "data": {
+          "image/png": 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\n",
+          "text/plain": "<Figure size 360x360 with 1 Axes>"
+         },
+         "metadata": {},
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+      "model_module_version": "1.2.0",
+      "model_name": "LayoutModel",
+      "state": {
+       "grid_area": "top"
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+     },
+     "ec9fbb55949648f291bf6a9e8a8e3e20": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "1.5.0",
+      "model_name": "HTMLModel",
+      "state": {
+       "layout": "IPY_MODEL_9d014e0f9eb54f79a568c48ca5cd43a3",
+       "style": "IPY_MODEL_a04881a371c246aeac58a0dc9e5a6ccf",
+       "value": "<table><style>\ntable {\n    border-collapse: collapse;\n    width: auto;\n}\n\nth, td {\n    text-align: left;\n    padding: 8px;\n}\n\ntr:nth-child(even) {background-color: #f6f6f6;}\n</style><tr><th></th><th>Frequency</th><th>T1</th><th>T2</th><th>U1 gate error</th><th>U2 gate error</th><th>U3 gate error</th><th>Readout error</th></tr><tr><td><font style='font-weight:bold'>Q0</font></td><td>4.83343 GHz</td><td>92.68711 µs</td><td>157.17411 µs</td><td>0</td><td>0.00022</td><td>0.00044</td><td>0.0125</td></tr><tr><td><font style='font-weight:bold'>Q1</font></td><td>4.62385 GHz</td><td>88.58389 µs</td><td>94.28362 µs</td><td>0</td><td>0.00016</td><td>0.00033</td><td>0.0205</td></tr><tr><td><font style='font-weight:bold'>Q2</font></td><td>4.82053 GHz</td><td>148.28351 µs</td><td>93.83139 µs</td><td>0</td><td>0.00025</td><td>0.00049</td><td>0.014</td></tr><tr><td><font style='font-weight:bold'>Q3</font></td><td>4.74233 GHz</td><td>183.80524 µs</td><td>133.6838 µs</td><td>0</td><td>0.00034</td><td>0.00067</td><td>0.0205</td></tr><tr><td><font style='font-weight:bold'>Q4</font></td><td>4.81632 GHz</td><td>139.69121 µs</td><td>161.77685 µs</td><td>0</td><td>0.00027</td><td>0.00055</td><td>0.0185</td></tr></table>"
+      }
+     },
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+      }
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+       "layout": "IPY_MODEL_bdd500a138c349dba647397770c3afac",
+       "style": "IPY_MODEL_922507bf5f7d4d8489ddf262bafd972d",
+       "value": "<table><style>\ntable {\n    border-collapse: collapse;\n    width: auto;\n}\n\nth, td {\n    text-align: left;\n    padding: 8px;\n}\n\ntr:nth-child(even) {background-color: #f6f6f6;};\n</style><tr><th></th><th>Type</th><th>Gate error</th></tr><tr><td><font style='font-weight:bold'>cx2_1</font></td><td>cx</td><td>0.00697</td></tr><tr><td><font style='font-weight:bold'>cx2_3</font></td><td>cx</td><td>0.00797</td></tr><tr><td><font style='font-weight:bold'>cx3_2</font></td><td>cx</td><td>0.00797</td></tr></table>"
+      }
+     },
+     "f6a25e029a604504b2e46ac8d9fa78b8": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "1.5.0",
+      "model_name": "DescriptionStyleModel",
+      "state": {
+       "description_width": ""
+      }
+     },
+     "f7f4cfc0d755411f89a0bdce5dcad88e": {
+      "model_module": "@jupyter-widgets/output",
+      "model_module_version": "1.0.0",
+      "model_name": "OutputModel",
+      "state": {
+       "layout": "IPY_MODEL_a51f23f542b14476a00f2ac31e78248c",
+       "outputs": [
+        {
+         "data": {
+          "image/png": 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\n",
+          "text/plain": "<Figure size 792x648 with 5 Axes>"
+         },
+         "metadata": {},
+         "output_type": "display_data"
+        }
+       ]
+      }
+     },
+     "f8088ed9dfef486b8c20c71bfa6ea033": {
+      "model_module": "@jupyter-widgets/base",
+      "model_module_version": "1.2.0",
+      "model_name": "LayoutModel",
+      "state": {
+       "margin": "0px 0px 10px 0px"
+      }
+     },
+     "f94dd6350f47437d9cb5295874638354": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "1.5.0",
+      "model_name": "DescriptionStyleModel",
+      "state": {
+       "description_width": ""
+      }
+     },
+     "fa8f3e84417e4c34b1a77b694537ce1f": {
+      "model_module": "@jupyter-widgets/base",
+      "model_module_version": "1.2.0",
+      "model_name": "LayoutModel",
+      "state": {
+       "grid_gap": "0px 0px",
+       "grid_template_areas": "\n                                                   \"top top top\"\n                                                   \"left middle right\"\n                                                   ",
+       "grid_template_columns": "33% 33% 33%",
+       "grid_template_rows": "auto auto"
+      }
+     },
+     "fc37f2e31db4496398069eb6dd7fda7c": {
+      "model_module": "@jupyter-widgets/base",
+      "model_module_version": "1.2.0",
+      "model_name": "LayoutModel",
+      "state": {
+       "width": "145px"
+      }
+     },
+     "fd31b67599074ad4adc45f5631b776de": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "1.5.0",
+      "model_name": "HTMLModel",
+      "state": {
+       "layout": "IPY_MODEL_84cc3506039e48be9c1fa64470557e8c",
+       "style": "IPY_MODEL_27546698b0964fafb3996bfbdaaedb46",
+       "value": "<table><style>\ntable {\n    border-collapse: collapse;\n    width: auto;\n}\n\nth, td {\n    text-align: left;\n    padding: 8px;\n}\n\ntr:nth-child(even) {background-color: #f6f6f6;};\n</style><tr><th></th><th>Type</th><th>Gate error</th></tr><tr><td><font style='font-weight:bold'>cx0_1</font></td><td>cx</td><td>0.00677</td></tr><tr><td><font style='font-weight:bold'>cx1_0</font></td><td>cx</td><td>0.00677</td></tr><tr><td><font style='font-weight:bold'>cx1_2</font></td><td>cx</td><td>0.00697</td></tr></table>"
+      }
+     },
+     "fe04470767a840509e5fbff3ab936fc2": {
+      "model_module": "@jupyter-widgets/base",
+      "model_module_version": "1.2.0",
+      "model_name": "LayoutModel",
+      "state": {
+       "grid_area": "bottom",
+       "width": "auto"
+      }
+     },
+     "fe0f981acfdf4c969f06cbe0717e18dd": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "1.5.0",
+      "model_name": "HTMLModel",
+      "state": {
+       "layout": "IPY_MODEL_496ae2f8626f4c83997f03fb9dce6aa6",
+       "style": "IPY_MODEL_54ee40ba16874d118c7ea3e02e32a6a2",
+       "value": "<div><font style='font-weight:bold'>last_update_date</font>: 2020-08-11 04:51:47+00:00</div>"
+      }
+     },
+     "ff2f6924a1ea410598250c37fcbc3050": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "1.5.0",
+      "model_name": "HTMLModel",
+      "state": {
+       "layout": "IPY_MODEL_475ac5f0954841c384bcdfc2dc438f7b",
+       "style": "IPY_MODEL_020990ea72bb4a7b8bcde3587376b034",
+       "value": "<p style='font-family: IBM Plex Sans, Arial, Helvetica, sans-serif; font-size: 20px; font-weight: medium;'>Circuit Properties</p>"
+      }
+     },
+     "ffa650e4fccf4d36b4aa8bbc72378b4e": {
+      "model_module": "@jupyter-widgets/base",
+      "model_module_version": "1.2.0",
+      "model_name": "LayoutModel",
+      "state": {
+       "grid_area": "left"
+      }
+     }
+    },
+    "version_major": 2,
+    "version_minor": 0
+   }
   }
  },
  "nbformat": 4,