qiskit-documentation/docs/tutorials/wire-cutting-to-improve-performance.ipynb

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   "source": [
    "# Improving estimation of expectation values using wire cutting\n",
    "*Usage estimate: 1 minute on IBM Brisbane (NOTE: This is an estimate only. Your runtime may vary.)*"
   ]
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   "source": [
    "## Background\n",
    "\n",
    "Circuit-knitting is an umbrella term which encapsulates various methods of partitioning a circuit in multiple smaller subcircuits involving fewer gates and/or qubits. Each of the subcircuits can be executed independently and the final result is obtained via some classical postprocessing over the outcome of each subcircuit. This technique is accessible in the [circuit cutting Qiskit addon](https://qiskit.github.io/qiskit-addon-cutting/index.html), a detailed explaination of the technique is given in the [docs](https://qiskit.github.io/qiskit-addon-cutting/explanation/index.html) along with other [introductory material](https://qiskit.github.io/qiskit-addon-cutting/tutorials/index.html).\n",
    "\n",
    "This notebook deals with a method called <b>wire cutting</b> where the circuit is partitioned along the wire [\\[1\\], \\[2\\]](#references). Note that, partitioning is simple in classical circuits since the outcome at the point of partition can be determined deterministically, and is either 0 or 1. However, the state of the qubit at the point of the cut is, in general, a mixed state. Therefore, each subcircuit needs to be measured multiple times in different basis (usually a tomographically complete set of basis such as the Pauli basis [\\[3\\], \\[4\\]](#references) and correspondingly prepared in its eigenstate. The Figure below (<i>courtesy: PhD Thesis, Ritajit Majumdar</i>) shows an example of wire cutting for a 4-qubit GHZ state into three subcircuits. Here $M_j$ denote a set of basis (usually Pauli X, Y and Z) and $P_i$ denote a set of eigenstates (usually $|0\\rangle$, $|1\\rangle$, $|+\\rangle$ and $|+i\\rangle$).\n",
    "\n",
    "![wc-1.png](/images/tutorials/wire-cutting-to-improve-performance/0ce8857b-7f5f-400e-8536-6a496c724d50.avif)\n",
    "![wc-2.png](/images/tutorials/wire-cutting-to-improve-performance/cbce4455-4794-4c81-8630-3e3993e1b29f.avif)\n",
    "\n",
    "Since each subcircuit has fewer qubits and/or gates, they are expected to be less amenable to noise. This notebook shows an example where this method can be used to effectively suppress the noise in the system."
   ]
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   "id": "8f692382",
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   "source": [
    "## Requirements\n",
    "Before starting this tutorial, be sure you have the following installed:\n",
    "\n",
    "- Qiskit SDK v1.0 or later, with visualization support ( `pip install 'qiskit[visualization]'` )\n",
    "- Qiskit Runtime 0.22 or later ( `pip install qiskit-ibm-runtime` )\n",
    "- Circuit cutting Qiskit addon 0.9.0 or later (`pip install qiskit-addon-cutting`)\n",
    "\n",
    "We shall consider a Many Body Localization (MBL) circuit for this notebook. The MBL circuit is a hardware-efficient circuit and is parameterized by two parameters $\\theta$ and $\\vec{\\phi}$. When $\\theta$ is set to $0$ and the initial state is prepared in $|0\\rangle$ for all the qubits, the ideal expectation value of $\\langle Z_i \\rangle$ is $+1$ for every qubit site $i$ irrespective of the values of $\\vec{\\phi}$. You can check more details on MBL circuits in <a href=\"https://arxiv.org/abs/2307.07552\">this paper</a>."
   ]
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   "cell_type": "markdown",
   "id": "fd0848a8",
   "metadata": {},
   "source": [
    "## Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "36fb62a7",
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   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "from qiskit.circuit import Parameter, ParameterVector, QuantumCircuit\n",
    "from qiskit.quantum_info import PauliList, SparsePauliOp\n",
    "from qiskit.transpiler import generate_preset_pass_manager\n",
    "from qiskit.result import sampled_expectation_value\n",
    "\n",
    "from qiskit_addon_cutting.instructions import CutWire\n",
    "from qiskit_addon_cutting import (\n",
    "    cut_wires,\n",
    "    expand_observables,\n",
    "    partition_problem,\n",
    "    generate_cutting_experiments,\n",
    "    reconstruct_expectation_values,\n",
    ")\n",
    "\n",
    "from qiskit_ibm_runtime import QiskitRuntimeService\n",
    "from qiskit_ibm_runtime import SamplerV2, Batch\n",
    "\n",
    "\n",
    "class MBLChainCircuit(QuantumCircuit):\n",
    "    def __init__(\n",
    "        self, num_qubits: int, depth: int, use_cut: bool = False\n",
    "    ) -> None:\n",
    "        super().__init__(\n",
    "            num_qubits, name=f\"MBLChainCircuit<{num_qubits}, {depth}>\"\n",
    "        )\n",
    "        evolution = MBLChainEvolution(num_qubits, depth, use_cut)\n",
    "        self.compose(evolution, inplace=True)\n",
    "\n",
    "\n",
    "class MBLChainEvolution(QuantumCircuit):\n",
    "    def __init__(self, num_qubits: int, depth: int, use_cut) -> None:\n",
    "        super().__init__(\n",
    "            num_qubits, name=f\"MBLChainEvolution<{num_qubits}, {depth}>\"\n",
    "        )\n",
    "\n",
    "        theta = Parameter(\"θ\")\n",
    "        phis = ParameterVector(\"φ\", num_qubits)\n",
    "\n",
    "        for layer in range(depth):\n",
    "            layer_parity = layer % 2\n",
    "            # print(\"layer parity\", layer_parity)\n",
    "            for qubit in range(layer_parity, num_qubits - 1, 2):\n",
    "                # print(qubit)\n",
    "                self.cz(qubit, qubit + 1)\n",
    "                self.u(theta, 0, np.pi, qubit)\n",
    "                self.u(theta, 0, np.pi, qubit + 1)\n",
    "                if (\n",
    "                    use_cut\n",
    "                    and layer_parity == 0\n",
    "                    and (\n",
    "                        qubit == num_qubits // 2 - 1\n",
    "                        or qubit == num_qubits // 2\n",
    "                    )\n",
    "                ):\n",
    "                    self.append(CutWire(), [num_qubits // 2])\n",
    "                if use_cut and layer < depth - 1 and layer_parity == 1:\n",
    "                    if qubit == num_qubits // 2:\n",
    "                        self.append(CutWire(), [qubit])\n",
    "            for qubit in range(num_qubits):\n",
    "                self.p(phis[qubit], qubit)"
   ]
  },
  {
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   "id": "2c0e1ce3",
   "metadata": {},
   "source": [
    "## Part I. Small scale example"
   ]
  },
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   "cell_type": "markdown",
   "id": "a9c3e7a2-49ef-4510-abd1-e1707070ca44",
   "metadata": {},
   "source": [
    "### Step 1: Map classical inputs to a quantum problem"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "215f9315-431d-4e60-b5b2-f7b36b830efa",
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   "source": [
    "Initially we build a template circuit without any specific parameter values. We also provide placeholders, called `CutWire`, to annotate the position of cuts. For the small scale example we consider a 10-qubit MBL circuit."
   ]
  },
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   "execution_count": 68,
   "id": "9c7939a1-6b70-4dad-b873-5f34d67551c4",
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",
      "text/plain": [
       "<Figure size 621.941x869.556 with 1 Axes>"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_qubits = 10\n",
    "depth = 2\n",
    "mbl = MBLChainCircuit(num_qubits, depth)\n",
    "mbl.draw(\"mpl\", fold=-1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed373c57-5037-41ad-a992-4f9b6c0663a1",
   "metadata": {},
   "source": [
    "Recall that we aim to find the expectation value of the observable $\\frac{1}{n}\\sum_{i=1} ^n Z_i$ when $\\theta=0$. We shall put some random values for the parameter $\\vec{\\phi}$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "eab4ce0a-89a1-4c3d-b949-eadac29bb035",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0,\n",
       " 0.2376615174332788,\n",
       " 0.28244289857682414,\n",
       " 0.019248960591717768,\n",
       " 0.46140600996102477,\n",
       " 0.31408025180068433,\n",
       " 0.718184005135733,\n",
       " 0.991153920182475,\n",
       " 0.09289485768301442,\n",
       " 0.8857848280067783,\n",
       " 0.6177529765767047]"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "phis = list(np.random.rand(mbl.num_parameters - 1))\n",
    "theta = [0]\n",
    "params = theta + phis\n",
    "params"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad6708aa-e6e8-42c2-a4b9-a8a805651222",
   "metadata": {},
   "source": [
    "Now we annotate the circuit for cutting by inserting proper **CutWire** to create two roughly equal cuts. We set `use_cut=True` in the function, and allow it to annotate after $\\frac{n}{2}$ qubits, $n$ being the number of qubits in the original circuit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "31844134-514b-46ea-85f9-133e432f053f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 789.163x869.556 with 1 Axes>"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mbl_cut = MBLChainCircuit(num_qubits, depth, use_cut=True)\n",
    "mbl_cut.assign_parameters(params, inplace=True)\n",
    "mbl_cut.draw(\"mpl\", fold=-1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "043b3cb2-25db-486c-afba-8968a7d9b4ba",
   "metadata": {},
   "source": [
    "### Step 2: Optimize problem for quantum hardware execution"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93fbe6b3-e1ef-43d2-bf80-4ff56a861ae5",
   "metadata": {},
   "source": [
    "Next we cut the circuit into two smaller subcircuits. For this example, we stick to only 2 subcircuits. For this, we use the <a href=\"https://qiskit.github.io/qiskit-addon-cutting/\">Qiskit Addon: Circuit Cutting</a>."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc73dbb9-6eeb-4117-80b0-690e30d8a543",
   "metadata": {},
   "source": [
    "#### Cut the circuit into smaller subcircuits"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "34b09a7d-c82c-41e3-8538-42f562e1b1eb",
   "metadata": {},
   "source": [
    "Cutting the wire at a point increases the qubit count by one. Apart from the original qubit, there is now an extra qubit as a placeholder to the circuit after cutting. The following image gives a representation:\n",
    "\n",
    "![wc-4.png](/images/tutorials/wire-cutting-to-improve-performance/dfc5f923-e507-4873-888e-d90e1618be3a.avif)\n",
    "\n",
    "This Addon uses the function `cut_wires` to account for the extra qubits arising due to cutting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e5c6638d-8c37-40ef-902c-ee9661f95399",
   "metadata": {},
   "outputs": [],
   "source": [
    "mbl_move = cut_wires(mbl_cut)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "784e19b1-6df5-4c91-99ee-7580da82d09d",
   "metadata": {},
   "source": [
    "#### Create and expand the observables\n",
    "\n",
    "Now we construct the observable $M_z = \\frac{1}{n}\\sum_{i=1}^n \\langle Z_i \\rangle$. Since the ideal outcome of $\\langle Z_i \\rangle$ for each $i$ is $+1$, the ideal outcome of $M_z$ is also $+1$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "74eadae9-5443-465d-8c87-9f34a77bf6ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PauliList(['ZIIIIIIIII', 'IZIIIIIIII', 'IIZIIIIIII', 'IIIZIIIIII',\n",
       "           'IIIIZIIIII', 'IIIIIZIIII', 'IIIIIIZIII', 'IIIIIIIZII',\n",
       "           'IIIIIIIIZI', 'IIIIIIIIIZ'])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "observable = PauliList(\n",
    "    [\"I\" * i + \"Z\" + \"I\" * (num_qubits - i - 1) for i in range(num_qubits)]\n",
    ")\n",
    "observable"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28c2d715-adb9-4218-aed5-96ebb1520756",
   "metadata": {},
   "source": [
    "However, note that the number of qubits in the circuit has increased after inserting the virtual 2-qubit `Move` operations after cutting. Therefore, we need to expand the observables as well by inserting identities to assert to the current circuit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0e2eabac-b224-4933-9567-7d218c53bc02",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PauliList(['ZIIIIIIIIII', 'IZIIIIIIIII', 'IIZIIIIIIII', 'IIIZIIIIIII',\n",
       "           'IIIIZIIIIII', 'IIIIIIZIIII', 'IIIIIIIZIII', 'IIIIIIIIZII',\n",
       "           'IIIIIIIIIZI', 'IIIIIIIIIIZ'])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_obs = expand_observables(observable, mbl, mbl_move)\n",
    "new_obs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0f6d41d-2cd5-43b3-8cd4-afdefac65879",
   "metadata": {},
   "source": [
    "Note that each observable has now expanded to accommodate 7 qubits, as in the circuit with `Move` operation, instead of the original 6 qubits. Now we shall partition the circuit into two subcircuits."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "07651125-238c-4e7d-82c5-3ca8d9ba6fc8",
   "metadata": {},
   "outputs": [],
   "source": [
    "partitioned_problem = partition_problem(circuit=mbl_move, observables=new_obs)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6ac68da-71ba-4344-9b87-fdb776f720b7",
   "metadata": {},
   "source": [
    "Let us visualize the subcircuits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "265a60eb-50e8-47be-99f0-931fa876644d",
   "metadata": {},
   "outputs": [],
   "source": [
    "subcircuits = partitioned_problem.subcircuits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c10af39c-88fe-4605-975c-5bf0e21ee4c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 723.783x535.111 with 1 Axes>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subcircuits[0].draw(\"mpl\", fold=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "35920640-76e8-4af6-a252-ee6a22e9c26a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 723.984x451.5 with 1 Axes>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subcircuits[1].draw(\"mpl\", fold=-1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0204a26d-5775-4604-8809-3a3b194c710e",
   "metadata": {},
   "source": [
    "The observables have been partitioned as well to fit the subcircuits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "3e892c89-3f46-44d2-a8f6-154daac0415b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: PauliList(['IIIIII', 'IIIIII', 'IIIIII', 'IIIIII', 'IIIIII', 'IZIIII',\n",
       "            'IIZIII', 'IIIZII', 'IIIIZI', 'IIIIIZ']),\n",
       " 1: PauliList(['ZIIII', 'IZIII', 'IIZII', 'IIIZI', 'IIIIZ', 'IIIII', 'IIIII',\n",
       "            'IIIII', 'IIIII', 'IIIII'])}"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subobservables = partitioned_problem.subobservables\n",
    "subobservables"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7204362-5168-4e9e-a2b7-20313b89949a",
   "metadata": {},
   "source": [
    "Note that each subcircuit leads to a number of samples. The reconstruction takes into account the outcome of each of these samples. Each of these samples is termed a `subexperiment`."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76a69768-e612-43f0-b21e-748bcbf94d43",
   "metadata": {},
   "source": [
    "Extending the observable using the `Move` operation requires a `PauliList` data structure. We can also create the $M_z$ observable in the more generic `SparsePauliOp` data structure which will be useful later during reconstruction of the subexperiments."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "320484d1-7be2-48fa-809f-66a54394b4f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SparsePauliOp(['ZIIIIIIIII', 'IZIIIIIIII', 'IIZIIIIIII', 'IIIZIIIIII', 'IIIIZIIIII', 'IIIIIZIIII', 'IIIIIIZIII', 'IIIIIIIZII', 'IIIIIIIIZI', 'IIIIIIIIIZ'],\n",
       "              coeffs=[0.1+0.j, 0.1+0.j, 0.1+0.j, 0.1+0.j, 0.1+0.j, 0.1+0.j, 0.1+0.j, 0.1+0.j,\n",
       " 0.1+0.j, 0.1+0.j])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "M_z = SparsePauliOp(\n",
    "    [\"I\" * i + \"Z\" + \"I\" * (num_qubits - i - 1) for i in range(num_qubits)],\n",
    "    coeffs=[1 / num_qubits] * num_qubits,\n",
    ")\n",
    "M_z"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e3c96631-4b8c-4026-95ba-c95026c13956",
   "metadata": {},
   "outputs": [],
   "source": [
    "subexperiments, coefficients = generate_cutting_experiments(\n",
    "    circuits=subcircuits,\n",
    "    observables=subobservables,\n",
    "    num_samples=np.inf,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1bd19292-79a0-4402-b09d-baada463b616",
   "metadata": {},
   "source": [
    "Let us see two examples where the cut qubits are measured in two different basis. First, it is measured in normal Z basis, and next it is measured in X basis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "749e8f27-9c83-48d8-bcf6-635c967bf10b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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9RxnzJ6vyRJ6sQWFKuv1lBXboUWuZsp8ylLFwior3fS3/dh2VMn+7Y17Bt2uU9a8/y15SKFksChswVvG/fVQWHx+V/ZSufXN+JcNeJaOqUgEJ3ZU47R+ytYlw8V42zOz7j+apCUcAAN6DMUYAmNaOHTsk1X8ZTXOFhoZqzZo1at++va677jr9/ve/19ChQ7V8+XL5+HDI9QQhHWM15v2/68r1C3XZykcV3jWhRdfzZAeeuUXRl9ysns/uVuxVdytjwZQ6y1iDQhU/8W9KvvPfdebZ2kQo+a431OPp79V93lYV/vCl8j/7lyTJNzJO3R5Zr5T529Vj0Xfyi4xTzpKHWniPmsbs+w8AAKrxLh2AabVEMCJJnTp10vLly1VYWKhjx47ptddeU0xMjFO3geYbOvcW7X7tE707bIZ2PPWehi2Y3qLreaqKY4dUtHeLokZMkiSFD52g8ryDKs3ZW2s5W0ik2qQMk49/cJ02gpL7yj82WZLk4xegoI59VH4oo/qxr798/AMlSUZVlapKizxqhF6z7z8AAPgZwQgA02qpYASeKyAqVFG9Oylt2ReSpP0fblRwXJRCkmJbZD2/0CBdvXWxrtv5ksZ/8piu+GK+rs9YoqGPT3XODp2B8ryD8o1oL4u1+qpai8Uiv5gOKj98oFntVRzN1dEv31bYgMsc0+wV5fr+jj765vpoleXsUdyv/+qU2p3B7PsPAAB+RjACwLTWrFkjwzA0duxYd5cCFwmOj1bJT0dlVP18J4rCrDwFx0e3yHrlBcXa9+46ff/Ch3r/opna9MBLOrxtt76867kz2xEPU1VcoL1/G6fYq2YpuMsAx3QfXz+lzN+u1Fd+UkDC2Tr88WI3VtlyzL7/AAC0dgQjAAC0oMieHXVkR7okKSq1k458l+7miqr5RZ+liqM5MqoqJUmGYaj88AH5xXRoUjtVxSe056FLFX7O5Wp3+Z/qXcbH109RF96g/LWvnnHdzmL2/QcAAD8jGAEAmEZRVp4C20XIYv35z1+b+GgVZeW1yHqSFNkjyRGGRKUmK3+HZwQjvuFtFdSpn/LXviZJOvblMvlFJSigfedGt1FVUqg9f71Uof0uVftr7qs1r+zQftnLiiVJht2uoxveUlBiqvN24AyZff8BAMDPuF0vAMA0SvMLdGRHujpNGK69b65V4tjBKso5ohMZuZKkYQtv04GV/9WBlZucsl5QbKRkGCrOPSJJiuyeqG8XLHPBnjZO4q2LlbFwinLfni1rYKiSZrwkScpY9HuFDxqv8HPGy15WrO9u7SqjokxVxcf17Y0JihpxveJ/+4gOfbBARXs2yV5WpGMb35EkRQy9Wu2vuVclGd8q7bV7JUmGYVdQcj+dddNCt+1rfcy+/wAAoJrFMAzD3UUAAFqHQe9LdlWfbrhpvLurObWK4lK93mlSnemhneI0bP40+UeEqKKwROvveFrHfqgecPOKz5/UuhlPKf+bNKeslzCqv7rfeKk++c3fJUmXrXxUR3Zm1DvGyMS01+QbFHDG+12jpFI6b4XTmnOJdWOkQCd9ZWP2/YdUWVmpZcs8J4hsjAkTJshm40UAAK7GkRcAYCoFadlaMe7eOtP9o0JVnHOk3lCkuetlrt6qzNVbHY+Xj/7zGVQOAACAlsAYIwAASCrLL9Cq6x522XoAAADwDAQjAAAAAADAtAhGAAAAAACAaRGMAAAAAAAA0+KuNACARmtNd6UxDEOVJWXuLqPRbIH+slgsTmvPMKTSKqc15xIBVslZT4HZ9x/Vx4CqKue9CB5bvFQniooUEhysmbdcW+exM1itVqceBwAAjcNdaQAAXslisTj19retjcVi7lu/mn3/UX0McOatbw1JdqP6X5vNVucxAKD14lIaAAAAAABgWgQjAAAAAADAtAhGAAAAAACAaRGMAAAAAAAA0yIYAQAAAAAApkUwAgAAAAAATItgBAAAAAAAmBbBCAAAAAAAMC2CEQAAAAAAYFoEIwAAAAAAwLQIRgAAAAAAgGkRjAAAAAAAANMiGAEAAAAAAKZFMAIAAAAAAEyLYAQAAAAAAJgWwQgAAAAAADAtghEAAAAAAGBaNncXAABASzAMQ5UlZe4uo9Fsgf6yWCxOa88wpNIqpzXnEgFWyYlPgem1tj4gOb8fmBnHgOo+UFXVup4Eq9VKHwDcgGAEAOCVKkvK9HqnSe4uo9Empr0m36AAp7VXWiWdt8JpzbnEujFSIO9MnKa19QHJ+f3AzDgGSFVVVVq2bJnzGnSBCRMmyGbjQAi4GpfSAAAAAAAA0yIYAQAAAAAApkUwAgAAAAAATItgBAAAAAAAmBbBCAAAAAAAMC2CEQAAAAAAYFoEIwAAAAAAwLQIRgAAAAAAgGkRjAAAcAqdrxmhKTlvq/M1I+qd3yYhRlNy3taw+dNcW5gLndixVlsvt9T6+fraNtr1p/766YMFMqqq3F0iWhB9ABLHAQDezebuAgAAQOsQMfzXCus/RjIMVRzJVv6al5X5wh0qPbBTidP+4e7yALgAxwEA3ohgBAAANEpQcj9FjZjkeBwz+lbtnNZdeZ+8oLiJD8s3vJ0bqwPgChwHzkxRUZGCg4PdXQaAXyAYAYAGZGRk6NNPP9XmzZu1efNm7dixQxUVFZo8ebJefvlld5cHuJ01KFTB3Ybo2FfLVJa7jw9EgAmZ4Thw/Phx7du3T+np6crNzVV5ebl8fHwUHBysxMREJScnq0OHDrLZTv3RauPGjXr++ec1a9YsdevWzUXVA2gMghEAaMD8+fO1YMECd5cBeCzDMFSWu1eSZAuNdnM1ANzBW48DlZWV2rRpkz755BPt2rXrtMsHBgZq+PDhuuiii5SQkFBn/saNG7Vw4ULZ7XY98sgjmj17tuLi4lqidADNQDACAA2Ijo7WmDFjNHDgQA0YMEArV67UM8884+6y3OZYuWT873fjlEvCW9nLilVZkCfDMFRxNEeHly9SSfo3Cu42WAFxXdxdXosrrJCyi6WyKincT0oIliwWd1cFuJYZjgMbN27UK6+8oqNHjzZ6nZKSEn388cf6+OOP1b9/f/3ud79TZGSko72aUESShgwZotjY2BapHUDzEIwAQAPuu+++Wo83btzopkrcxzCkrfnS2xnSZzm1g5HfrZeuTpIujJN8uceZKeQseVA5Sx78eYKPj8IGjff6ARd/OC69nS6tzJTK7D9P7xwi/SpJGnOWFMQ7KpiENx8HCgoK9OKLL9b5ex8XF6e+ffuqY8eOSkxMVEBAgOx2u44cOaL09HTt3btXW7duVVlZmSRp69at2rVrl37729/K399fixYtcoQiI0eO1E033SQfH/5wAp6EP+MATCMvL09z587VO++8o8zMTMXExOiqq67S7NmzNWPGDL344otatGiRpk+f7u5SPUKVIT2+Q3oro/753xyp/nk7Q3pikBTm58rqPI9heP95NNGX3KyIoVdLFot8AoIVENdVtpBId5fVol7dKy34vv55e09Ij+6QlqRLC8+R4k0+nqIZ+gC89zjw008/6e9//7sOHTrkmNa3b19ddtllSklJkaWe08PatWun7t27S6oeVPXzzz/X+++/r2PHjqm4uFjPPfecLBaLo28QigCei2AEgCls375do0ePVm5uroKDg5WSkqLs7GwtXLhQaWlpOnLkiCSpT58+7i3UQxiG9MR3DYciJ9t+RLrjv9KzQ6UAa4uX5nKVpeWSJGugf73zbUHV06v+t5w382/fRaF9Rrm7DJdZsq/hUORk+wulW7+SXhomRQW0fF2uRh/AybzxOJCXl6f/+7//U35+viSpTZs2uuGGGzR06NB6A5H6BAcHa8yYMRo+fLheeeUVrVu3TpIIRYBWgp4JwOvl5eVp3Lhxys3N1Z133qmcnBxt27ZNubm5mjNnjj788ENt3rxZFotFqamp7i7XI3xzRHozvfHL7zgqvbGv5epxp8ID1d8ehneJr3d+WJfqQfZOHDhU73y0Tj+VSPN3Nn757GLp6R9arh53og/Am1VUVGjOnDmOUCQhIUFz5szRueee2+hQ5GRt2rRR//7966ybmJhIKAJ4MHonAK83Y8YMZWZmavr06Xr88ccVEhLimDdr1iz17t1blZWVSkpKUmhoqBsr9RxvZzR9nWUZ1ZffeJv8HftUmHVYHa84V4HtImrN8/G1qfuNo2XY7Tq4aoubKkRLeG9/01/PH2dKBV540gR9AN7s7bff1sGDByVJ7du31/3336+oqKhmt1cz0OovLy1bsmSJcnNzz6hWAC2HYASAV9u1a5eWLl2q6OhoPfLII/Uu079/f0lS7969z3h7NQHMoEGD5O/v36xvm9ytsEJand309XJKpM2HnV+PuxlVdm28+3n5hgTp8jVPqP+9k9R10iil/vFXGrdqrmKH9tC3i95VQVoznjR4rP8caPo6ZXbp4yzn1+Ju9AF4q3379un999+XJFmtVv3xj39UWFhYs9v75d1nRo4cqYsuukiSVFZWpsWLFzMWD+ChGGMEgFdbsmSJ7Ha7Jk6cqDZt2tS7TGBgoCTnBCN79+7VsmXLNHDgQPn5+WnDhg1n3OaAAQNc+i2TNbaLYh74rFnrXj/jbpWsf93JFTWPr+GjBzXIKW1lfrpNK8bfp17TrlDna86Xf0SIKovLlP9dutbe/IQyPvjqjLfRtUtXVVjsp1+wkSx+gWo3f4/T2nOFrl27yCgvcXcZktVXsYuacC3ZSR6Y95z++O7fnFxQ87S2PiA5vx8405U33KHgNqHKyc1RQkJCnceehmOA5Ofn1+CXIpL07rvvOoKKCRMmqEOHDs3eVn2hyE033aTy8nJt375dhw8f1q5du7Rr1y6lpKQ02E7Xrl1VXu6Fp54BLhAbG6stW5p39iLBCACvtmbNGknVb1AakpmZKck5wcjw4cOVk5MjSXrooYecEozk5uYqK8t1X0MH2CIU08x1jxec0GEX1noqfhar1M557eV/k6a1Nz/hvAZ/ITsnW+VGldPa8/EPctruh/Qaof7/aflvObOzs2UvK27x7ZyOxS9Asc1ct7CkxKX99VRaWx+QnN8PnMleVeX4Nysrq85jT+PMY4DkmuOAs48B/v71DxgsSfn5+Y4PUBERERo/fnyzt9NQKOLj46OAgABdd911WrRokSRp1apVpwxGsrOzHbf9BeA6BCMAvNr+/fslVQ96Vp/KykpHeOGMYKQlBlaLjW3uR7TmsQQ0//KfEEu5/OLrH6DR1XwNH8kzv3iuV1z7OKefMdLaxMXFecYZI5LsxcflE9T0U+oDK4sUTx9oNmf3A2fysVod/8bHx9d57Gk4BlSfMdKQNWvWOM4WueCCC2SzNe9j0alCkRqDBg1SWFiYjh8/rs2bN+vYsWMKDw+vt724uDjOGAGa6UzeMxOMAPBqRUVFkqSSkvrfaC1dulR5eXkKCQlRx44dXVlaozX3lMAzcdtX0ldNHC8kzFda//4LHnPL3oriUr3eaZK7y2i03Xt2yzfIefd6LamUzlvhtOZcYvfuPQr0kHcmc75t3O2qT+Yj6Ytn71Ns4H0tUVKTtbY+IDm/HzjT7KdfV0FhkdrHtldmZmadx56GY0D1lx/Lli2rd9733/98L+5TnVV6Ko0JRSTJ19dXw4YN04cffqiqqirt3r1bgwbVf5nb7t27mx3SAGg+Bl8F4NVqkuNt27bVmZeTk6OZM2dKklJTU1vlQKkt5VfNyIjGd5DHhCLAmfpVUtPXGR4rxba+L+kB07Hb7crIyJAkRUZGKjo6usltNDYUqdGtWzfH7/v2een97YFWjGAEgFcbNWqUJGnOnDnavXu3Y/rmzZs1cuRI5eXlSZL69OnjjvI81rB20tC2jV++faA0qVPL1QO4WqdQ6eqkxi8fbJNuPbvFygHgRLm5uY4zSTt1avofr6aGIpKUnJzs+D09vXmDOwNoOQQjALzarFmzFBUVpYMHD6pHjx7q1auXunTpokGDBik5OVkXXHCBpPrHF9mwYYOio6MdP/PmzZMkvfHGG7WmO2OAVU9jtUiPDJAGNeJLtLgg6akhUpRnnv0ONNudPaXLzjr9cqG+0oJzqsMUAJ7vxIkTjt+berZIc0IRSYqKinKcmXry9gF4Bi5gA+DVEhIStG7dOs2cOVOff/65MjIylJKSosWLF+umm25yfFNUXzBSUVGh/Pz8OtPLyspqjRhfUVHRcjvgRsE2acFg6Z390tvpUnph7fkRftIVidKvk6XIhgf+B1otm4/0YB/pnBhpabr03dHa84Os0tizpImdpIRgt5QIoBnat2+vGTNmqLy8vEkD55aXl+vVV19tcigiSRaLRTfffLOsVqvCwpo+sDOAlkUwAsDrde/eXcuXL68zvbCwUBkZGfLx8VHPnj3rzB8xYoRjxHqz8vWRru0oXZMkfXNEyiyWKu1SdED12SR+jCkCL2exSKMTqn9+PC5N+lwyJFkkrbykOkAE0LqEhoZq6NChTV7Pz89P9957rx5++GH16dOn0aFIjeYO8gqg5fHnHIBp7dy5U4ZhqGvXrgoKCnJau2+//bakn0e8r3mclJSkAQMGOG07rmSxSH2iqn/gPUqz9yhj/mRVnsiTNShMSbe/rMAOPeosV5KxQweev02Vx36SJMVN+rsihlylEzvWas//jVZA/M+DCp495yv5+AfKsNuV+dJdKtj2kSxWm6whUUqc/rwC2nd21e45Xbew6kCkJhghFEFrxzGg6eLi4vT3v/9d4eHhTQpFAHg2/qQDMK0dO3ZIqv8ymjNx9dVX1/t48uTJevnll526LTRdSMdYnbfgNvlHhqjiRLHW3/6Uju0+/a02m7ueJzvwzC2KvuRmRV84RUc3vK2MBVPU/YnNtZaxlxVr7+zL1fGOf6lNyjAZVVWqLDzimB8Q300p87fXafv4pvdVuGuDUhZ8I4vNVzlv/k3Zr/5FybPebOndwmnQB1CDY0DzREZGursEAE5GzAnAtFoqGDEMo94fQhHPMHTuLdr92id6d9gM7XjqPQ1bML1F1/NUFccOqWjvFkWNmCRJCh86QeV5B1Was7fWckc+/7eCuw1Wm5RhkiSL1SrfsJjTb8BikVFZJnt5qQzDUFVxgXyjEpy+H2g6+gAkjgEAcDLOGAFgWi0VjMBzBUSFKqp3J6267mFJ0v4PN2rw7N8pJClWJzJynb6eX2iQLv/sSVkD/FScnScff1+FdGintLc/15d3PefcnWui8ryD8o1oL4u1+q2AxWKRX0wHlR8+UOtU95KD38vH5q+9D1+m8vxMBSamKuHGJxwfjMpy0vT9H/vJ4mNV1IU3qO2YP0iSwgaO04kdn+nbKbHyCQyRX1S8uv79c9fvKGqhD6AGxwAA+BnBCADTWrNmjbtLgIsFx0er5KejMqrsjmmFWXkKjo8+5Ye75q5XXlCsfe+uU0VRqb598m3Fjeit1BlXtaoPhEZVpQq+Wa2zH9so38g4Zb/6Fx149lZ1+vPbCurUT6kvZsoaHKbyvEzt/b8xsoVGK3LYNSreu0Ul+79TrxezZA0KVda//qwDz05Vxz+95u5dMjX6AJqKYwAAM+BSGgAAWlBkz446siNdkhSV2klHvkt3c0XV/KLPUsXRHBlVlZKqLwErP3xAfjEdai8X00EhvUbKLypeFotFkSMmqWj3RkmSNShU1uCw/7WXoIjhv1bh9+skSfmf/UshqRfI1iZcFh8fRV0wWSd2fObCPYSn8NQ+YHYcAwDgZwQjAADTKMrKU2C7CFmsP//5axMfraKsvBZZT5IieyQ5PghGpSYrf4dnfCj0DW+roE79lL+2+tvbY18uk19UQp07RlR/87tZVcUFkqTjW1YoMKn68rOKIzky7NVnEFQVn9DxzcsVlNxXkuQfm6wT366RvaK8er3NyxXYoe5tseFa9AHU4BgAAD/jUhoAgGmU5hfoyI50dZowXHvfXKvEsYNVlHPEcSnAsIW36cDK/+rAyk1OWS8oNlIyDBXnVt/BIbJ7or5dsMwFe9o4ibcuVsbCKcp9e7asgaFKmvGSJClj0e8VPmi8ws8ZL7+YDor91V/0w91DZbH4yDcqXol/+Ick6ehXy3R45bOyWG0yqioVce7VirrwBklSzJhpKj24S7vu6C2L1Ve2iFgl3srlE+5GH8DJOAYAQDWCEQCAqXw5a7GGzZ+mXjOuUkVhidbf8bRjXnTvZO365wqnrRfZs2OtywbKC4p09uRLPGZ8hYCEbjp77ld1pifd9kKtx1Ejr1fUyOvrLNd27HS1HVv/nUl8fP2VOP155xQKp6IPoAbHAACoRjACADCVgrRsrRh3b53p/lGhKs45ovxv0py2XubqrcpcvdXxePnoP59B5YBz0AcAAKiNMUYAAJBUll/guBWpK9YDPA19AABgVgQjAAAAAADAtAhGAAAAAACAaRGMAAAAAAAA02LwVQCAV7IF+mti2mvuLqPRbIH+Tm0vwCqtG+PUJltcgNXdFXiX1tYHJOf3AzPjGCBZrVZNmDDBae09tnipThQVKSQ4WDNvubbOY2ewWjkQAu5AMAIA8EoWi0W+QQHuLsNtLBYpkL/ypmb2PmB2HAOq+4DN5rwnwZBkN6r/tdlsdR4DaL24lAYAAAAAAJgWwQgAAAAAADAtghEAAAAAAGBaBCMAAAAAAMC0CEYAAAAAAIBpEYwAAAAAAADTIhgBAAAAAACmRTACAAAAAABMi2AEAAAAAACYFsEIAAAAAAAwLYIRAAAAAABgWgQjAAAAAADAtAhGAAAAAACAaRGMAAAAAAAA0yIYAQAAAAAApkUwAgAAAAAATItgBAAAAAAAmBbBCAAAAAAAMC2buwsAAKAlGIahypIyd5fRaLZAf1ksFqe1ZxhSaZXTmnOJAKvkxKfA9FpbH5Cc3w/MjGMADMNQVVXrehFYrVaOAXALghEAgFeqLCnT650mubuMRpuY9pp8gwKc1l5plXTeCqc15xLrxkiBvDNxmtbWByTn9wMz4xiAqqoqLVu2zN1lNMmECRNks/EigOtxKQ0AAAAAADAtghEAAAAAAGBaBCMAAAAAAMC0CEYAAAAAAIBpEYwAAAAAAADTIhgBAAAAAACmRTACAAAAAABMi2AEAIBT6HzNCE3JeVudrxlR7/w2CTGakvO2hs2f5trCXOjEjrXaerml1s/X17bRrj/1108fLJBRVeXuEtGC6AOQOA4A8G42dxcAAABah4jhv1ZY/zGSYajiSLby17yszBfuUOmBnUqc9g93lwfABTgOAPBGBCMAAKBRgpL7KWrEJMfjmNG3aue07sr75AXFTXxYvuHt3FgdAFfgOADAG3EpDQA0ICMjQ//85z81depU9e/fX35+frJYLJoyZYq7SwM8gjUoVMHdhkiGobLcfe4uB4AbcBwwj9LSUu3Zs0fffvuttm/fru+//15Hjx5t9PqGYWjp0qX68ccfW7BKoHk4YwQAGjB//nwtWLDA3WUAHsswDJXl7pUk2UKj3VwNAHfgOOC9DMPQ3r179dlnn2n37t3KysqSYRh1louIiFBycrKGDBmic845R76+vvW29dprr+nDDz/UypUrdc8996hbt26u2A2gUQhGAKAB0dHRGjNmjAYOHKgBAwZo5cqVeuaZZ9xdFuA29rJiVRbkyTAMVRzN0eHli1SS/o2Cuw1WQFwXd5cHwAU4Dng/wzD01Vdf6YMPPlB6evpplz969Ki2bt2qrVu36l//+pcuvPBCXX755QoICHC0VxOKSFJZWZlycnIIRuBRCEYAoAH33XdfrccbN250UyXuZTekjYeld/dLWUVSpSFF+0uXJEiXxEkB/CUxjZwlDypnyYM/T/DxUdig8V4/4KJhSDuOSm9nSPb/TbNLej1NGneWFOrnxuIAFzPrccAsjh49qhdeeEFbt26tNd1qteqss85Sx44dFRERIYvFouLiYh04cEDp6ekqLi6WJBUUFOjdd9/Vhg0bdMsttyglJaVWKGKxWHTzzTdrxIgRrt414JR4OwvANPLy8jR37ly98847yszMVExMjK666irNnj1bM2bM0IsvvqhFixZp+vTp7i7VY2zPlx76Wsosrj193wlpU540f6d0W3fpqiS3lOdR6ju92NtEX3KzIoZeLVks8gkIVkBcV9lCIt1dVovaXyjdu1X64XjdeU/ulJ7ZJV3fWbq5m+RjcX19nsQMfQDmPA6YxXfffacnn3xSRUVFjmmdOnXSRRddpCFDhsjf37/e9ex2u3788Ud98skn+u9//6uqqiodOnRIDz/8sDp16qS0tDRJP4ciI0eOdMn+AE1BMALAFLZv367Ro0crNzdXwcHBSklJUXZ2thYuXKi0tDQdOXJEktSnTx/3FupBNh2W7vivVG5veJkTFdLsb6WCCmmKl55BXVlaLkmyBtb/htAWVD296n/LeTP/9l0U2meUu8twmX0npN+vr359N6TMLr2wWzpcKt3XW7J4YThCH8DJzHYcMIvt27friSeeUEVF9QEvLCxMv//97zVw4MDTruvj46Pu3bure/fuys3N1eLFi7Vr1y5JIhRBq8FdaQB4vby8PI0bN065ubm68847lZOTo23btik3N1dz5szRhx9+qM2bN8tisSg1NdXd5XqEvFJp5uZThyIne2qX9NWhlq3JXQoPVO9YeJf4eueHdUmQJJ044KVPgElV2KuDwVOFIif7zwHprYwWLclt6AOAd9u7d6/mzZvnCEX69u2rxx9/vFGhyC/FxsbqvvvuU/fu3WtN79evH6EIPBrBCACvN2PGDGVmZmr69Ol6/PHHFRIS4pg3a9Ys9e7dW5WVlUpKSlJoaKgbK/Uc7x2Qiiqbts7raS1Ti7vl79inwqzD6njFuQpsF1Frno+vTd1vHC3DbtfBVVvcVCFawpocKbv49Mud7PW06jF5vA19APBeZWVleuqpp1ReXn3G1znnnKM777yz1nulpjAMQ//+978dZ4zU2LZtG7fphUcjGAHg1Xbt2qWlS5cqOjpajzzySL3L9O/fX5LUu3fvM97e22+/rQkTJigxMVFBQUE6++yzde+996qwsPCM23aVSrv0bkbT19t4uHo8Bm9jVNm18e7n5RsSpMvXPKH+905S10mjlPrHX2ncqrmKHdpD3y56VwVp2e4uFU701ulvxFBHVrF3njlFHwC81xtvvKHc3FxJUufOnXXbbbfJZmveaAu/vPuMxWLRoEGDHPOeffZZlZWVOadwwMkYYwSAV1uyZInsdrsmTpyoNm3a1LtMYGCgJOcEI48//rg6dOig2bNnKyEhQdu3b9df//pXff755/riiy/k49P0PHrAgAGONy2uYI1JUsxf1zdr3VFT/qSSjW86uaLm8TV89KAGOaWtzE+3acX4+9Rr2hXqfM358o8IUWVxmfK/S9fam59QxgdfnfE2unbpqgpLI69dagSLX6Dazd/jtPZcoWvXLjLKS9xdhmSxqN2i/bI0o7/+7q8LVfj+3BYoqulaWx+QnN8PnOnKG+5QcJtQ5eTmKCEhoc5jT8MxwPla22vAz8+vwS+FJCk7O1sfffSRJMnX11e33nqrU0ORm2++Weeff74eeOAB7d27V7m5ufroo490+eWXN9hO165dHWevAE0VGxurLVuad/YiwQgAr7ZmzRpJOuV1rZmZmZKcE4x88MEHiomJcTw+//zzFRMTo4kTJ2r9+vUaPnx4k9vMzc1VVlbWGdfWWIF+0Yo5/WL1Ol5aocMurPVU/CxWqZ3z2sv/Jk1rb37CeQ3+QnZOtsqNKqe15+Mf5LTdD+k1Qv3/0/LXiGRnZ8te1sTrV1qAT0CwYpsRikhScaVc2l9PpbX1Acn5/cCZ7FVVjn+zsrLqPPY0zjwGSK45DnjKMaAhre010NBdZGqsXr3acTepK664QvHx9Y8jdDoNhSI1772mTp2qmTNnyjAMffLJJxo3blyDXxRlZ2dzVgncgmAEgFfbv3+/JCkxMbHe+ZWVldqwYYMk5wQjJ4ciNQYMGCCp+R+WYmNjz6imprKGBDV73bAAm/ya+cbK2XwNH8kzv3iuV1z7OKefMdLaxMXFeca3xRaLDHuVLD7WJq8aZDWa/eHC2VpbH5Cc3w+cycdqdfwbHx9f57Gn4RjgfK3tNeDn59fgvLKyMn3++eeSqs8Wufjii5u1jdOFIpKUkJCg3r17a/v27crLy9PXX3/tuIz5l+Li4jhjBM12Ju+ZCUYAeLWioiJJUklJ/W+0li5dqry8PIWEhKhjx44tUsNnn30mSXVGaG+s5p4S2FxVhnTVp9XjJTSFRdLn/3pScUFPtkhdTVVRXKrXO01ydxmNtnvPbvkGBTitvZJK6bwVTmvOJXbv3qNAD3ln8ocvpU15TV/vtYdv14Bnb3d+Qc3Q2vqA5Px+4Eyzn35dBYVFah/bXpmZmXUeexqOAc7X2l4DlZWVWrZsWb3zvv/+e8d7pCFDhjRrsNXGhCI1LrroIm3fvl2StGnTpgaDkd27dzf7ch7gTDD4KgCvVpMcb9u2rc68nJwczZw5U5KUmpoqi8Xi9O1nZWXp/vvv16WXXqo+ffo4vf2WYLVIV9V/gs0pDWsnxTX/ZBPAo/wqqenrJLWR+kc5vRQAcLp9+/Y5fm/O+5OmhCKS1KtXL8flM+npzRjdGmhhBCMAvNqoUaMkSXPmzNHu3bsd0zdv3qyRI0cqL6/6K+GWCC0KCwt1+eWXy8/PTy+++KLT229Jl3eQwhs+A7cOi6RJnVqsHMDlhsdWBx1N8dvOUgvkqwDgdCcHI009Y7apoYhUfVlPzQC1mZmZjCMCj0MwAsCrzZo1S1FRUTp48KB69OihXr16qUuXLho0aJCSk5N1wQUXSKp/fJENGzYoOjra8TNv3jxJ1be2O3l6zRglJyspKdG4ceOUnp6uVatWqX379i27o04W7i/NGyQFNXKIhVm9pP7RLVsT4Eo2H2n+OVJMI6/qmJgsjTurZWsCAGf56aefJFWPL9KuXeOH6W1OKFKjQ4cOkiS73e74YgrwFAQjALxaQkKC1q1bp7FjxyogIEAZGRmKjIzU4sWL9eGHHzrOIqkvGKmoqFB+fr7jp2ackrKyslrTKyoq6qz3q1/9Slu2bNHKlSuVkpLS8jvaAlIjpeeHSd3CGl6mbYD09/7S1S0zPAvgVgnB0kvnSYNOEfqF+kp39Kj+4WwRAK1F586dlZKSopSUlAbvEFOfd955p1mhiCSdddZZOvvss5Wamso4IvA4vCIBeL3u3btr+fLldaYXFhYqIyNDPj4+6tmzZ535I0aMcNzGrrHsdrsmTpyoTz/9VCtWrNCgQYOaXbcn6BYmvTZc+u6o9O4B6f0DP897bKB0Xrvqb9YBbxUbKD0zVNp3QnonQ0o7IZVVSWF+0gXtpYvipYCm37wGANxq6tSpzVpvyJAhWr16tY4dO9akUESSLr/8cl1++eXN2i7Q0ghGAJjWzp07ZRiGunbtqqAg54waOm3aNL311lv685//rKCgIG3cuNExr1OnTvXeztfTWSxSr8jqn+UHqu/+6SNpZOu6Ogg4I8kh0l293F0FALhXXFyc7r//fqWlpem8885zdzmA0xCMADCtHTt2SKr/MprmWrlypSTp0Ucf1aOPPlpr3ksvvaQpU6Y4bVtonpCOsTpvwW3yjwxRxYlirb/9KR3bffrbLDZ3PU9Wmr1HGfMnq/JEnqxBYUq6/WUFduhRa5kTO9Zqz/+NVkB8N8e0s+d8JR//QBl2u7JemaXj2z6SUVWpNt3PVYepz8rH10/Ht32srH/d7Vin8tgh2SJilfJk3TtEwbXoA6jBMQDNERcXp7i4OHeXATgVwQgA02qJYCQjI8NpbaFlDJ17i3a/9on2vrlWiWMHa9iC6Vo++s8ttp4nO/DMLYq+5GZFXzhFRze8rYwFU9T9ic11lguI76aU+dvrTM9b/U8Vp21T93nbZLH56sDTN+vQBwsUe9VMhfW7RGH9LnEsu/fhyxTSq/GnXKPl0AdQg2MAAFTjynAAptUSwQg8W0BUqKJ6d1Lasi8kSfs/3KjguCiFJMW2yHp+oUG6eutiXbfzJY3/5DFd8cV8XZ+xREMfb9613c5UceyQivZuUdSISZKk8KETVJ53UKU5exvdRkn6NwrpPUo+vn6yWCwK7T9aR9a+Wme58vxsFXz7qSJHXO+0+tE89AHU4BgAAD8jGAFgWmvWrJFhGBo7dqy7S4GLBMdHq+SnozKq7I5phVl5Co4/9b2Gm7teeUGx9r27Tt+/8KHev2imNj3wkg5v260v73ruzHbECcrzDso3or0s1uqTRy0Wi/xiOqj88IE6y5blpOn7P/bTrjsH6tCKZxzTgzr11/FN76uquEBGZYWOrn9TZYcy6qyfv+ZlhfUfI9/wti22P2gc+gBqcAwAgJ9xKQ0AAC0osmdH7XphhSQpKrWTjnyX7uaKmiaoUz+lvpgpa3CYyvMytff/xsgWGq3IYdco6sIpKj+8Xz/+5Xz5+AUqtPcoFWxfVWt9wzCUv/pFnXXTQjftAdyttfcBs+MYAMAMOGMEAGAaRVl5CmwXIYv15z9/beKjVZSV1yLrSVJkjyTHB8Go1GTl7/CMD4V+0Wep4miOjKpKSdUfXsoPH5BfTIday1mDQmUNDvvfOgmKGP5rFX6/TlL1N8xxv35IKfO/1tlzv1TAWSl1Bm4s/O5z2StKFdr3EsH96AOowTEAAH5GMAIAMI3S/AId2ZGuThOGS5ISxw5WUc4RncjIlSQNW3ibOowe5LT1gmIjJcNQce4RSVJk90Qd/aHuaeru4BveVkGd+il/7WuSpGNfLpNfVIIC2neutVzFkRwZ9urLJ6qKT+j45uUKSu4rSbKXl6qy8KgkqbIgT7nvPKp2V86qtX7e6n8q6oIpslitLb1LaAT6AGpwDACAn3EpDQDAVL6ctVjD5k9TrxlXqaKwROvveNoxL7p3snb9c4XT1ovs2bHWZQPlBUU6e/IlHjO+QuKti5WxcIpy354ta2Cokma8JEnKWPR7hQ8ar/BzxuvoV8t0eOWzslhtMqoqFXHu1Yq68AZJUlXxce2+d4Rk8ZEMu9pedrvCB41ztF9VdFzHvnpHKQt3uGP30AD6AGpwDACAagQjAABTKUjL1opx99aZ7h8VquKcI8r/Js1p62Wu3qrM1Vsdjz3t1qYBCd109tyv6kxPuu0Fx+9tx05X27HT613fN7ydejy9q8H2rcFh6vtm0ZkXCqeiD6AGxwAAqMalNAAASCrLL9Cq6x522XqAp6EPAADMimAEAAAAAACYFsEIAAAAAAAwLYIRAAAAAABgWgy+CgDwSrZAf01Me83dZTSaLdDfqe0FWKV1Y5zaZIsL4G6eTtXa+oDk/H5gZhwDYLVaNWHCBKe199jipTpRVKSQ4GDNvOXaOo+dwcptneEmBCMAAK9ksVjkGxTg7jLcxmKRAvkrb2pm7wNmxzEAFotFNpvzXgSGJLtR/a/NZqvzGGjNuJQGAAAAAACYFsEIAAAAAAAwLYIRAAAAAABgWgQjAAAAAADAtAhGAAAAAACAaRGMAAAAAAAA0yIYAQAAAAAApkUwAgAAAAAATItgBAAAAAAAmBbBCAAAAAAAMC2CEQAAAAAAYFoEIwAAAAAAwLQIRgAAAAAAgGkRjAAAAAAAANMiGAEAAAAAAKZFMAIAAAAAAEyLYAQAAAAAAJgWwQgAAAAAADAtm7sLAACgJRiGocqSMneX0Wi2QH9ZLBantWcYUmmV05pziQCr5MSnwPRaWx+QnN8PzIxjAFB9HKyqaj0dwWq1cgx0E4IRAIBXqiwp0+udJrm7jEabmPaafIMCnNZeaZV03gqnNecS68ZIgbwzcZrW1gck5/cDM+MYAEhVVVVatmyZu8totAkTJshmoxO4A5fSAAAAAAAA0yIYAQAAAAAApkUwAgAAAAAATItgBAAAAAAAmBbBCAAAAAAAMC2CEQAAAAAAYFoEIwAAAAAAwLQIRgAAOIXO14zQlJy31fmaEfXOb5MQoyk5b2vY/GmuLcyFTuxYq62XW2r9fH1tG+36U3/99MECGVVV7i4RLYg+AInjAADvZnN3AQAAoHWIGP5rhfUfIxmGKo5kK3/Ny8p84Q6VHtipxGn/cHd5AFyA4wAAb0QwAgAAGiUouZ+iRkxyPI4Zfat2TuuuvE9eUNzEh+Ub3s6N1QFwBY4DALwRl9IAAIBmsQaFKrjbEMkwVJa7z93lAHADjgNA45WWlio9Pd3dZaAenDECAA3IyMjQp59+qs2bN2vz5s3asWOHKioqNHnyZL388svuLg9wO8MwVJa7V5JkC412czUA3IHjALzdkSNHtGPHDqWnpystLU1Hjx5VRUWFbDabwsPD1bFjRyUnJ6tnz55q27Ztg+2UlpZqzpw5Sk9P1z333KNu3bq5cC9wOgQjANCA+fPna8GCBe4uA/AY9rJiVRbkyTAMVRzN0eHli1SS/o2Cuw1WQFwXd5cHwAU4DsAMDMPQd999p08++URbtmyR3W6vd7n8/HylpaVJkiwWi1JTU3XxxRerb9++8vH5+eKMmlBk165dkqSFCxdq/vz58vX1bfmdQaMQjABAA6KjozVmzBgNHDhQAwYM0MqVK/XMM8+4uyy3qLRLGw9Lxv8eG5IKK6Q2/D03lZwlDypnyYM/T/DxUdig8aYYcNEwpK35UtoJqaxKCveThrWTIv3dXRngWmY+DsAcDh8+rH/84x/asWNHvfPDwsLk6+uriooKHT9+3DHdMAx98803+uabb9SlSxdNnTpV8fHxdUKRoKAg/elPfyIU8TAEIwDQgPvuu6/W440bN7qpEvcprZReS5Pe2S8dKv15uiFpzCppdIJ0QxcpNshtJXoMwzBOv1ArF33JzYoYerVkscgnIFgBcV1lC4l0d1ktqsqQ3s6Q3kqXMgprz7NZpAvjqvtA51C3lOdRzNAHYM7jAMxj7dq1euWVV1RSUuKYFhERoREjRiglJUUdO3ZUmzZtHPOKi4uVkZGhH374QZ999pkOHz4sSdqzZ4/+/Oc/a8KECdq+fbt++OEHSdWhyL333qtOnTq5dsdwWgQjAEwjLy9Pc+fO1TvvvKPMzEzFxMToqquu0uzZszVjxgy9+OKLWrRokaZPn+7uUj3C8XLp9v9K3x2tf35xlbRsv7Q2V1o0WOoa5tr6XKWytFySZA2s/9QAW1D19Kr/LefN/Nt3UWifUe4uw2XKq6S/bK1+jden0pA+zpI+z5UeGygNafjS8laNPoCTme04APNYtmyZ3nrrLcfjyMhITZw4Ueecc45stvo/NgcFBSklJUUpKSm64oor9PXXX+vVV19Vbm6uKioq9MYbb9RallDEcxGMADCF7du3a/To0crNzVVwcLBSUlKUnZ2thQsXKi0tTUeOHJEk9enTx72FeohKuzRzc8OhyMnyy6TbNkqvDJdiA1u+NlcrPHBIkhTeJb7e+WFdEiRJJ/63HLzH379pOBQ5WWmVdNcm6YVhUvfwFi/L5egDALzd+++/XysUGTFihK6//noFBwc3ug0fHx/1799fPXv21Ouvv65Vq1Y55tlsNv3lL38hFPFg3K4XgNfLy8vTuHHjlJubqzvvvFM5OTnatm2bcnNzNWfOHH344YfavHmzY9AsVH8Y3Jbf+OXzy6TX9rZcPe6Uv2OfCrMOq+MV5yqwXUSteT6+NnW/cbQMu10HV21xU4VoCT8clz7MbPzyZXbp2R9arh53og8A8Gbfffed/v3vfzseT5o0SVOnTm1SKHIywzB08ODBWtMqKyt16BDhsSfjjBEAXm/GjBnKzMzU9OnT9fjjj9eaN2vWLP373//WN998o44dOyo0lIECpOoxFZrqg4PStO5SoJf9ZTGq7Np49/Ma+eJMXb7mCe359xqd2J+rgJhwdRw/VBFnd9A3C5apIC3b3aXCiZZlNH2drw5JmUVSQvPeS3ss+gAAb1VSUqLnnnvO8fiaa67RZZdd1uz2fjnQqp+fn8rLqy8zfOmll5SSkqLw8PAzqhktgzNGAHi1Xbt2aenSpYqOjtYjjzxS7zL9+/eXJPXu3fuMt7du3TqNGjVK7du3l7+/vxISEnTttdc6/kC2Bnml0pa8pq9XVClt8NIvQzI/3aYV4+9T7oad6nzN+Ro8+/fqecs4leYXaO3NT+jrR5e4u0Q4kWFIHzfhbBHHepI+8dJsgD4AwBu9+eabysurftNTM05Ic9V395kHH3xQgwcPliSdOHFCr7zyyhnXjJbhZd/rAUBtS5Yskd1u18SJE2uNIn6ywMDqgTGcEYwcPXpUvXr10i233KK2bdsqMzNTjzzyiIYMGaLvvvtOCQkJTW5zwIABys1txEAHTmKLO1vR961u1rp/uOteFX/hGX/0fQ0fPahBTmsv/5s0rb35Cae190tdu3RVhcXutPYsfoFqN3+PU9oK6TVC/f/T8ncc6dq1i4zyktMv2NJ8AxS7oHnXhs19+gXd//ZDzq2nmVpbH5Cc3w+c6cob7lBwm1Dl5OYoISGhzmNP48xjgOSa44DHHAMa0NpeA87WGvffz8+vwS/GioqKtGbNGkmSv7+/brnlFvn4NO+8gfpCkZqBVm+88Ubt3LlTJ06c0MaNG/Wb3/xGMTEx9bbTtWtXxxkmaLrY2Fht2dK8yzoJRgB4tZo/eCNHjmxwmczM6q+GnRGMjB8/XuPHj681beDAgerWrZuWLVum22+/vclt5ubmKisr64xra6wAS4iim7nu0fzDynNhrafiZ7FK7dxdReNl52Sr3KhyWns+/kGtafclSdnZ2bKXFbu7DMlqU2wzVz1x7IhL++uptLY+IDm/HziTvarK8W9WVladx56GY4DztbbXgLO1xv3396//blqS9MUXX6isrExS9WCr7do1r8ecKhSRpNDQUI0ePVpvvvmmDMPQp59+quuuu67etrKzsx01wbUIRgB4tf3790uSEhMT651fWVmpDRs2SHJOMFKfqKgoSWrwVm+nExvb3I9ozWPxs8soL5XFL6DR6xiGIYvFopCKY/KPr//OFa7ma/hInvnFc73i2sc5/YyR1iYuLs5jvi2uyj8oa9RZTV4vsCRP8fSBZnN2P3AmH6vV8W98fHydx56GY4DztbbXgLO1xv338/NrcN5nn33m+H3UqObdgvp0oUiNCy64QMuWLVNVVZXWrl2ra6+9VhaLpU57cXFxnDFyBs7kPTPBCACvVlRUJKl6cK36LF26VHl5eQoJCVHHjh2dtt2qqirZ7Xbt379f99xzj2JjY3XNNdc0q63mnhJ4Jv76dfVgqo1lsVjUIVja9Omb8qn7d94tKopL9XqnSe4uo9F279kt36DGh1GnU1IpnbfCac25xO7dezxm8N4Xd0vPNPEuM0FW6fPX5yrYNrdlimqi1tYHJOf3A2ea/fTrKigsUvvY9srMzKzz2NNwDHC+1vYacLbWuP+VlZVatmxZneklJSWOO8ckJSXprLOaHoQ3NhSRpPDwcPXs2VPffPONjh07psOHD6tt27Z1ltu9e3ezv0jDmWHwVQBerSY53rZtW515OTk5mjlzpiQpNTW13uS+uc4//3z5+fmpS5cu2r59u9asWdPg9aSe6OpmZEQTkuQxoQhwpi7vIPk28V3SmLOkYN7PAoDHy8jIkGFUj5lTX5BxOk0JRWqcPC89Pb3J20TLIhgB4NVqTo2cM2eOdu/e7Zi+efNmjRw50jESeZ8+fZy63X/+85/auHGjlixZotDQUF188cU6cOCAU7fRklLCpRu6NH75gdHSNc474QZwu6gA6Z7Uxi/fsY30h7Nbrh4AgPOc/J6sqWcMNycUkaTk5GTH7zWXesNzEIwA8GqzZs1SVFSUDh48qB49eqhXr17q0qWLBg0apOTkZF1wwQWS6h9fZMOGDYqOjnb8zJs3T5L0xhtv1JpeM0bJybp166ZzzjlH1113nT799FOdOHFCc+d6xun1jfWHs6XfdT39ckPbSk8Mavq364CnG99Bure3ZD3NmVDdwqRnh0qhDV/KDgDwICdfYh0eHt7o9ZobikhSWFhYvduHZ+CETwBeLSEhQevWrdPMmTP1+eefKyMjQykpKVq8eLFuuukmxx+y+oKRiooK5efn15leVlZWa8TwioqKU9YQHh6uzp07a+/e5t3+010sFunWs6UL2ktvZ0grM6XS/90swiJpWDvpV0nSkLZcQgPvdWVi9RlRyzKk9w9Ix0/q7v2iqi8hu6A9wSAAtCaXXnqphg0bpoqKiiYFI8ePH1dubq6kpoUiUvWNAObNmyc/Pz8FBQU1p2y0IIIRAF6ve/fuWr58eZ3phYWFysjIkI+Pj3r27Fln/ogRIxzXn56JQ4cO6ccff9Q555xzxm25Q7ew6m/N7+wh5ZdJVYYU4S+F+Lq7MsA1EoKl23tIf+guDV0uGaoOB/9xrrsrAwA0R0BAgAICmj7Qc7t27XT//fdr3rx5uvXWW5s0Pomfn5/i4uKavE24BsEIANPauXOnDMNQ165dnZbcT5o0SZ07d1afPn0UHh6uPXv26Mknn5TNZtMf//hHp2zDXQJsUjx/NbxWafYeZcyfrMoTebIGhSnp9pcV2KFHrWUMu11Zr8zS8W0fyaiqVJvu56rD1Gfl4+unkowdOrB4miqOH5LFalNwl0HqcMvT8vFvfbcMbYivT3UgUhOMAN6G4wBwenFxcZo7d658fDhV0JvwvwnAtHbs2CGp/stommvw4MFasWKFbrjhBo0ePVqPPfaYzjvvPG3fvl2dO3d22nbQfCEdYzXm/b/ryvULddnKRxXeNaFF12stDjxzi6IvuVk9n92t2KvuVsaCKXWWyVv9TxWnbVP3edvU4+ldslh8dOiDBZIki1+AzrrlKfV85gelzP9G9tIi5b4zx8V7gcagD6AhHAeAxiEU8T78jwIwrZYIRqZPn65Nmzbp6NGjKikp0e7du7V48WIlJiY6bRs4M0Pn3qLdr32id4fN0I6n3tOwBdNbdL3WoOLYIRXt3aKoEZMkSeFDJ6g876BKc2qPi1OS/o1Ceo+Sj6+fLBaLQvuP1pG1r0qSAuK6KCip+jYuFqtVQV0GqvxQhkv3A41DH0B9OA4AMDOCEQCm1RLBCDxbQFSoonp3UtqyLyRJ+z/cqOC4KIUkxbbIen6hQbp662Jdt/Mljf/kMV3xxXxdn7FEQx+f6pwdcpLyvIPyjWgvi7X6WimLxSK/mA4qP1z7FtNBnfrr+Kb3VVVcIKOyQkfXv6myej70VJUWKe+TFxQ26HJXlI8moA+gIRwHAJgZV4sDMK01a9a4uwS4WHB8tEp+Oiqjyu6YVpiVp+D4aJ3IyHX6euUFxdr37jpVFJXq2yffVtyI3kqdcZW+vOs55+yQi0VdOEXlh/frx7+cLx+/QIX2HqWC7atqLWOvKFf6Y9cqtM/FihhypZsqRUPoAzhTHAcAeCPOGAEAoAVF9uyoIzvSJUlRqZ105Lt0N1dUl1/0Wao4miOjqlKSZBiGyg8fkF9Mh1rLWSwWxf36IaXM/1pnz/1SAWel1BqY0aisUPpj18o3or3OummBS/cBnqs19AFwHABgbgQjAADTKMrKU2C7CFmsP//5axMfraKsvBZZT5IieyQ5PghGpSYrf4fnfSj0DW+roE79lL/2NUnSsS+XyS8qQQHtaw8YbC8vVWXhUUlSZUGect95VO2unCVJMqoqte/x62QNiVSHaf+QxcJ9WzwRfQAN4TgAwMwIRgAAplGaX6AjO9LVacJwSVLi2MEqyjniuBRg2MLb1GH0IKetFxQbKRmGinOPSJIiuyfq6A8H6iznCRJvXay8jxfru1u7KnfZo0qa8ZIkKWPR73Xsv+9LkqqKj+vHu4dq5/Qe+vGe8xRzyVSFDxonSTqybqmOffWOivdu0a4/9tX3d/TRgeemuW1/UD/6AE6F4wAAs2KMEQCAqXw5a7GGzZ+mXjOuUkVhidbf8bRjXnTvZO365wqnrRfZs2OtywbKC4p09uRLPHJ8hYCEbjp77ld1pifd9oLjd9/wdurx9K56148aMVFRIya2WH1wHvoAGsJxAIBZEYwAAEylIC1bK8bdW2e6f1SoinOOKP+bNKetl7l6qzJXb3U8Xj76z2dQOeAc9AEAAGrjUhoAACSV5Rdo1XUPu2w9wNPQBwAAZkUwAgAAAAAATItgBAAAAAAAmBbBCAAAAAAAMC0GXwUAeCVboL8mpr3m7jIazRbo79T2AqzSujFObbLFBVjdXYF3aW19QHJ+PzAzjgGAZLVaNWHCBKe09djipTpRVKSQ4GDNvOXaBqedCauVTuAuBCMAAK9ksVjkGxTg7jLcxmKRAvkrb2pm7wNmxzEAqD4O2mzO6QiGJLtR/W9Nm/VNQ+vEpTQAAAAAAMC0CEYAAAAAAIBpEYwAAAAAAADTIhgBAAAAAACmRTACAAAAAABMi2AEAAAAAACYFsEIAAAAAAAwLYIRAAAAAABgWgQjAAAAAADAtAhGAAAAAACAaRGMAAAAAAAA0yIYAQAAAAAApkUwAgAAAAAATItgBAAAAAAAmBbBCAAAAAAAMC2CEQAAAAAAYFoEIwAAAAAAwLRs7i4AAICWYBiGKkvK3F1Go9kC/WWxWJzWnmFIpVVOa84lAqySE58C02ttfUByfj8wM44BAAzDUFVV6zoQWK1Wt/wdIBgBAHilypIyvd5pkrvLaLSJaa/JNyjAae2VVknnrXBacy6xbowUyDsTp2ltfUByfj8wM44BAKqqqrRs2TJ3l9EkEyZMkM3m+gMBl9IAAAAAAADTIhgBAAAAAACmRTACAAAAAABMi2AEAAAAAACYFsEIAAAAAAAwLYIRAAAAAABgWgQjAAAAAADAtAhGAAAAAACAaRGMAABwCp2vGaEpOW+r8zUj6p3fJiFGU3Le1rD501xbmAud2LFWWy+31Pr5+to22vWn/vrpgwUyqqrcXSJaEH0AEscBAN7N5u4CAABA6xAx/NcK6z9GMgxVHMlW/pqXlfnCHSo9sFOJ0/7h7vIAuADHAQDeiGAEAAA0SlByP0WNmOR4HDP6Vu2c1l15n7yguIkPyze8nRurA+AKHAcAnAm73S4fH8+7cIVgBADqkZGRoU8//VSbN2/W5s2btWPHDlVUVGjy5Ml6+eWX3V0e4BGsQaEK7jZEx75aprLcfXwgAkyI4wDg/QzDUH5+vvbt26esrCyVl5dLkgICAtShQwclJycrLCzstO3k5OToscce080336yzzz67pctuEoIRAKjH/PnztWDBAneXAXg0wzBUlrtXkmQLjXZzNQDcgeMA4L1ycnL0ySefaMOGDTp+/Pgpl23btq3OP/98XXDBBYqIiKi3rYcfflhHjhzRo48+qnvvvVddunRpqdKbjGAEAOoRHR2tMWPGaODAgRowYIBWrlypZ555xt1luZVhSMVVUpVdCvaVrBZ3VwRXs5cVq7IgT4ZhqOJojg4vX6SS9G8U3G2wAuI8581NSzEMyaj53a2VAO5j9uMAYAa5ubl65ZVX9PXXXzd6nUOHDumtt97SO++8o6FDh+r6669XaGiopNqhiFQdorRr51lnlxGMAEA97rvvvlqPN27c6KZK3C+3WFq2X/rPfulI9ZmT8veRLo6XfpUk9aj7pQC8VM6SB5Wz5MGfJ/j4KGzQeK8fcPFYmfSfA9I7+2sHIzM3Sb/qKA2KliwEhTAJsx4HADOw2+1atWqVlixZorKyMsd0X19fde/eXcnJyUpKSlJwcLAMw1BBQYHS09OVlpamH3/8UYZhqKqqSuvWrdM333yjG2+8UYmJibVCkQ4dOui+++5zhCaegmAEgCnk5eVp7ty5euedd5SZmamYmBhdddVVmj17tmbMmKEXX3xRixYt0vTp091dqkdZsk968jvJ/ovpZXbpg4PVP6Pjpfv7SH5Wd1ToOQzD+88hiL7kZkUMvVqyWOQTEKyAuK6yhUS6u6wWtSZbun9b9Wv+lz7Lrf4ZEC09NlAK8XV9fZ7EDH0A5jwOAGZQUVGhRYsWadOmTY5pUVFRuvTSSzVixAiFhITUu96wYcMkVb/X/vTTT7Vq1SoVFRWpoKBA8+fPV0BAgEpLSyV5bigiEYwAMIHt27dr9OjRys3NVXBwsFJSUpSdna2FCxcqLS3NkWD36dPHvYV6mNf2SvO/P/1yK7Ok0irp0YHeeXlNZWn1aTLWQP9659uCqqdX/W85b+bfvotC+4xydxkusyZbunvL6S+b2ZInTftKWjxUCvTCd1b0AZzMbMcBwAwqKys1b968WpfOXHzxxfrNb36jgICARrURHR2ta6+9Vpdccon++c9/avPmzZLkCEXOOussjw1FJMnz7pMDAE6Ul5encePGKTc3V3feeadycnK0bds25ebmas6cOfrwww+1efNmWSwWpaamurtcj7G3oHGhSI3PcqV397dcPe5UeOCQJCm8S3y988O6JEiSTvxvOXiH4+XSA183fiyR749Jz/3QkhW5D30AALzbiy++6AhF/P39dffdd+vGG29sdChysvDwcP36179WUFBQremDBg3y2FBEIhgB4OVmzJihzMxMTZ8+XY8//nit0wBnzZql3r17q7KyUklJSR59sHa1tzOavs5b6dWDU3qb/B37VJh1WB2vOFeB7WoPqOLja1P3G0fLsNt1cNUWN1WIlvDBweozoZq8TmXL1ONO9AEA8F7btm3TmjVrJFWPJTJr1iz17du32e3l5OTob3/7m4qLi2tN/89//qODBw+eUa0tiWAEgNfatWuXli5dqujoaD3yyCP1LtO/f39JUu/evZ2+/dGjR8tiseihhx5yetstqaRSWtGMv1tpJ6Rvjji/HnczquzaePfz8g0J0uVrnlD/eyep66RRSv3jrzRu1VzFDu2hbxe9q4K0bHeXCid6J6Pp6xRUSKu98GVAHwAA71RUVKTnn3/e8fiGG25Qjx49mt3eL+8+06FDB1188cWSqi/XefbZZ1VV1cRvHVzEC6+EBYBqS5Yskd1u18SJE9WmTZt6lwkMDJTk/GDkzTff1Pbt253S1oABA5Sbm+uUthrD2q6TYh78vFnrXv2HWSrZ8G8nV9Q8voaPHtQgp7SV+ek2rRh/n3pNu0Kdrzlf/hEhqiwuU/536Vp78xPK+OCrM95G1y5dVWGpZ4TPZrL4Bard/D1Oa88VunbtIqO8xN1lSD42xT6V0axV75r7jKa+N9u59TRTa+sDkvP7gTNdecMdCm4TqpzcHCUkJNR57Gk4Bjhfa3sNOBv7X3d/Pf058PPza/DLQUlatWqVjh49Kqn6vfDIkSObva36QpH77rtPAQEB+u6775Sdna19+/Zp8+bNGjx4cIPtdO3aVeXlzRuzKjY2Vlu2NO/sRYIRAF6r5rTAUx3kMzMzJTk3GCkoKNAdd9yhxx9/XJMmTTrj9nJzc5WVleWEyhon0DdKMc1c93hhsQ67sNZT8bNYpXbOay//mzStvfkJ5zX4C9k52So3nPctio9/kNN2P6TXCPX/T8tfJ5WdnS17WfHpF2xhPv5Bim3mukVlFS7tr6fS2vqA5Px+4Ez2/33Laa+qUlZWVp3HnsaZxwDJNccBTzkGNKS1vQacjf2vu7+e/hz4+9c/aLYkVVVVafXq1ZIki8WiG264QZZm3nu+oVCk5jL1yZMnOwKaTz755JTBSHZ2dq1bBbsKwQgAr7V/f/VooImJifXOr6ys1IYNGyQ5Nxi599571bVrV02cONEpwUhsbHM/ojWPT1D1PUcNw2jyH8hQm11+8fUP0OhqvoZP3fsMe7C49nFOP2OktYmLi/OYb4uN8lJZ/Jo+6FyQUaZ4+kCzObsfOJOP1er4Nz4+vs5jT8MxwPla22vA2dj/uvvr6c+Bn59fg/O+/vpr5efnS5L69u3b7PebpwtFJKlXr15q3769cnJytHPnTmVlZTX4fMXFxZ3RGSPNRTACwGsVFRVJkkpK6n+TtXTpUuXl5SkkJEQdO3Z0yja3bNmi559/Xlu3bnVKezVtutoN66QdR5sWigRYpa/ffkYhvs+0UFVNU1Fcqtc7nXkw5Sq79+yWb1DTP4g3pKRSOm+F05pzid2793jM7W7v3yatzGz6eh89OUsdX5jl/IKaobX1Acn5/cCZZj/9ugoKi9Q+tr0yMzPrPPY0HAOcr7W9BpyN/a+7v57+HFRWVmrZsmX1zvv2228dv1944YXNar8xoYgk+fj46MILL9Rrr73m2HZDwcju3btls7n+QMDgqwC8Vk1qvG3btjrzcnJyNHPmTElSampqs08dPFlVVZVuueUWTZ8+/YwGrvIEVyc1fZ3RCVKIr9NLAdzimqSmrzMgWuoYcvrlAABwt3379jl+7969e5PXb2woUt820tPTm7y9lkYwAsBrjRo1SpI0Z84c7d692zF98+bNGjlypPLy8iRJffr0ccr2nnrqKf3000+t7i409RkVJ50d1vjlQ32l33ZquXoAV+sZIZ3fhDNybRbplm4tVw8AAM5SVVXluOQ8NjZWQUFBTVq/qaFIzTLW/116dHIo4ykIRgB4rVmzZikqKkoHDx5Ujx491KtXL3Xp0kWDBg1ScnKyLrjgAkn1jy+yYcMGRUdHO37mzZsnSXrjjTdqTa8ZoyQvL0/333+/HnjgAVVWVurYsWM6duyYJKm0tFTHjh2T3e6Z183Xx88qzT9HSm7Et99tbNK8QdJZ9d/4B2iVLBbpb/2k/lGnX9ZmkR7uJ/VtxLIAALhbUVGRKioqJEnt27dv0rrNCUUkydfXVzEx1cP719wJx5MQjADwWgkJCVq3bp3Gjh2rgIAAZWRkKDIyUosXL9aHH37oOIukvmCkoqJC+fn5jp+acUrKyspqTa/5o5KZmakTJ07olltuUUREhONHqj5jJSIiQgcOHHDRnjtHdID0z2HSr5Orw49fslqqzyx56TypDx8I4YUCbdKiwdLN3aSoBgb2HxwjPTdUusjzxtwDAKBefn5+uvbaa3XllVdqyJAhjV7PMAw99dRTTQ5FaowaNUpXXXWVrrjiiuaU3aI8eHgjADhz3bt31/Lly+tMLywsVEZGhnx8fNSzZ88680eMGCHDaPxtCTt37qzPPvuszvSRI0dq8uTJmjJlisvvLuMMIb7SnT2lP5wtrc6W/rq9erpF0vKLpBjPHCMRcBo/a3UwckMX6fNcKa1AKrdLYX7SiFipA2dKAQBamYCAAF155ZVNXs9isWjatGl6+OGHFRIS0qRQRJIuu+yyJm/TVQhGAJjSzp07ZRiGunbt2uTrKuvTpk0bjRgxot55SUlJDc5rLQJt0rgO0sPbq+/+aRGhCMzF16f6DKlRce6uBAAA94mLi9MDDzyg4ODgJoUino5gBIAp7dixQ1L9l9HAu4V0jNV5C26Tf2SIKk4Ua/3tT+nY7tPfYq+563my0uw9ypg/WZUn8mQNClPS7S8rsEPtOyoZdrsyX7pLBds+ksVqkzUkSonTn1dA+86SpGOblyvzpbske5UCE3sp6faXZQ0K1fFtHyvrX3c72qk8dki2iFilPFn3LlFwLfoAanAMANAcTR2XpDVgjBEApuSqYMQwDK+4S403GTr3Fu1+7RO9O2yGdjz1noYtmN6i63myA8/couhLblbPZ3cr9qq7lbFgSp1ljm96X4W7NihlwTdKWfitQntfqOxX/yJJqiop1P5Fv1Pnv7ynns/tkW9knHKWPixJCut3iVLmb3f8BHXqp6jzJ7py99AA+gBqcAwAgGoEIwBMiTNGzCkgKlRRvTspbdkXkqT9H25UcFyUQpJOPf5Lc9fzCw3S1VsX67qdL2n8J4/pii/m6/qMJRr6+FTn7NAZqDh2SEV7tyhqxCRJUvjQCSrPO6jSnL21F7RYZFSWyV5eKsMwVFVcIN+oBElSwbaVCkruq4CEsyVJMaP/oCPrltTZVnl+tgq+/VSRI65v2Z3CadEHUINjAAD8jEtpAJjSmjVr3F0C3CA4PlolPx2VUfXzrZMLs/IUHB+tExm5Tl+vvKBY+95dp4qiUn375NuKG9FbqTOu0pd3PeecHToD5XkH5RvRXhZr9VsBi8Uiv5gOKj98wHGKvCSFDRynEzs+07dTYuUTGCK/qHh1/fvn1W0cPiC/tomOZf3bJaniaI6MqkpHu5KUv+ZlhfUfI9/wti7aOzSEPoAaHAMA4GecMQIAQAuK7NlRR3akS5KiUjvpyHfpbq6oaYr3blHJ/u/U68Uspb6UrZDUC3Xg2cZ/228YhvJXv6joUb9rwSrhyVp7HzA7jgEAzIBgBABgGkVZeQpsFyGL9ec/f23io1WUldci60lSZI8kxwfBqNRk5e/wjA+FftFnOb7Zlao/vJQfPiC/mA61lsv/7F8KSb1Atjbhsvj4KOqCyTqxo/rW1H4xHVR+aL9j2bKfMmp9Ay1Jhd99LntFqUL7XuKCvcLp0AdQg2MAAPyMYAQAYBql+QU6siNdnSYMlyQljh2sopwjjksBhi28TR1GD3LaekGxkZJhqDj3iCQpsnuijv5woEX2ral8w9sqqFM/5a99TZJ07Mtl8otKqHUKvST5xybrxLdrZK8olyQd37xcgR16SpJC+16q4n3bVJr5gyTp8MpnFHnedbXWz1v9T0VdMEUWq7WldwmNQB9ADY4BAPAzxhgBAJjKl7MWa9j8aeo14ypVFJZo/R1PO+ZF907Wrn+ucNp6kT071rpsoLygSGdPvsRjxldIvHWxMhZOUe7bs2UNDFXSjJckSRmLfq/wQeMVfs54xYyZptKDu7Trjt6yWH1li4hV4q3V9VuDQpQ47QXtnX2FVFWpgMSe6nj7K472q4qO69hX7yhl4Q537B4aQB9ADY4BAFCNYAQAYCoFadlaMe7eOtP9o0JVnHNE+d+kOW29zNVblbl6q+Px8tF/PoPKnS8goZvOnvtVnelJt73g+N3H11+J059vsI3wc6o/PNXHGhymvm8WnXmhcCr6AGpwDACAalxKAwCApLL8Aq267mGXrQd4GvoAAMCsCEYAAAAAAIBpEYwAAAAAAADTIhgBAAAAAACmxeCrAACvZAv018S019xdRqPZAv2d2l6AVVo3xqlNtrgA7ubpVK2tD0jO7wdmxjEAgNVq1YQJE5zW3mOLl+pEUZFCgoM185Zr6zx2Bqubbu1NMAIA8EoWi0W+QQHuLsNtLBYpkL/ypmb2PmB2HAMAWCwW2WzOOxAYkuxG9b82m63O49aMS2kAAAAAAIBpEYwAAAAAAADTIhgBAAAAAACmRTACAAAAAABMi2AEAAAAAACYFsEIAAAAAAAwLYIRAAAAAABgWgQjAAAAAADAtAhGAAAAAACAaRGMAAAAAAAA0yIYAQAAAAAApkUwAgAAAAAATItgBAAAAAAAmBbBCAAAAAAAMC2CEQAAAAAAYFoEIwAAAAAAwLQIRgAAAAAAgGkRjAAAAAAAANOyubsAAABagmEYqiwpc3cZjWYL9JfFYnFae4ZhqKqqymntuYLVanXqcwAAANAYBCMAAK9UWVKm1ztNcncZjTYx7TX5BgU4rb2qqiotW7bMae25woQJE2Sz8dYEAAC4FpfSAAAAAAAA0yIYAQAAAAAApkUwAgAAAAAATItgBAAAAAAAmBbBCAAAAAAAMC2CEQAAAAAAYFoEIwAAAAAAwLQIRgAAOIXO14zQlJy31fmaEfXOb5MQoyk5b2vY/GmuLQwAAABOQTACAAAAAABMi2AEAAAAAACYFsEIADQgIyND//znPzV16lT1799ffn5+slgsmjJlirtLA1zObrfr6NGjys3N1aFDh3TixIkmrV9ZWan33ntP5eXlLVQhAABA89jcXQAAeKr58+drwYIF7i4DcJv09HR9+eWXSktLU3p6ukpKSmrNj4iIUMeOHdWlSxcNHz5cUVFR9bZTWVmpBQsWaPPmzdq1a5fuvPNO+fn5uWIXAAAATotgBAAaEB0drTFjxmjgwIEaMGCAVq5cqWeeecbdZQEtym6368svv9THH3+sPXv2nHLZo0eP6ujRo9q2bZveeust9e/fX5dddpm6devmWObkUESSvv/+e+3fv19dunRp0f0AAABoLIIRAGjAfffdV+vxxo0b3VSJ++0vlP5zQLL/77Fd0rZ8qW+kZLG4szI406FDh7R48WLt3LmzzrzIyEglJiYqKChIhmHo+PHjSk9PV3FxsaTqQGXz5s3avHmzLr74Yv3mN7+RzWarFYr4+vrqrrvuIhQBAAAehWAEgGnk5eVp7ty5euedd5SZmamYmBhdddVVmj17tmbMmKEXX3xRixYt0vTp091dqsc4WCg9ukP67+G6827eICWHSH/sIQ1p6/raPI1hGO4u4Yxs2LBB//jHP1RWVuaY1qFDB1188cUaMGCAwsPD66xjGIZycnK0YcMGrVmzRkePHpUkrVq1Stu2bVO7du0cIUtNKNK7d2+X7A8AAEBjEYwAMIXt27dr9OjRys3NVXBwsFJSUpSdna2FCxcqLS1NR44ckST16dPHvYV6kL0F0tQvpWOnGCtz3wnp9v9K/9dXujTBdbW5UmVp9RNgDfSvd74tqHp6VWnrHVR09erVeuGFFxyPo6KidNNNN6l3796ynOKUIIvFori4OF199dW68sortXr1ai1ZskRlZWXKy8tTXl6eJEIRAADg2bgrDQCvl5eXp3Hjxik3N1d33nmncnJytG3bNuXm5mrOnDn68MMPtXnzZlksFqWmprq7XI9QWCHdvvHUoUgNuyE99LW082jL1+UOhQcOSZLCu8TXOz+sS3UidOJ/y7U2GzZsqBWKDB8+XI899pj69OlzylDkl2w2my699FLNnj1bwcHBteZNnjyZUAQAAHgsghEAXm/GjBnKzMzU9OnT9fjjjyskJMQxb9asWerdu7cqKyuVlJSk0NBQN1bqOZYflH4qbfzylYb0r70tV4875e/Yp8Ksw+p4xbkKbBdRa56Pr03dbxwtw27XwVVb3FRh8x06dEj/+Mc/HI/HjRunW2+9VUFBQc1qr7KyUm+88YaKiopqTV+1apUqKyvPqFYAAICWQjACwKvt2rVLS5cuVXR0tB555JF6l+nfv78kOeUb7bVr18pisdT5aU2X6BiG9FZG09dbmysdbkKY0loYVXZtvPt5+YYE6fI1T6j/vZPUddIopf7xVxq3aq5ih/bQt4veVUFatrtLbRK73a7Fixc7xhQZPny4fvOb3zTpLJGT/fLuM76+voqJiZEkHThwQO+++65zCgcAAHAyxhgB4NWWLFkiu92uiRMnqk2bNvUuExgYKMk5wUiNp59+Wv369XM8/uWlBZ4sp6T6LjRNVWVUD9J62VnOr8ndMj/dphXj71OvaVeo8zXnyz8iRJXFZcr/Ll1rb35CGR985e4Sm+zLL790DIwaHR2tKVOmODUUueuuuxQWFqZ7771XVVVVeu+993TeeecpNjbWafsAAADgDAQjALzamjVrJEkjR45scJnMzExJzg1GUlJSNHjwYKe0NWDAAOXm5jqlrcawxXdX9L2fNGvdP91zv6aufcnJFTWPr+GjBzXIae3lf5OmtTc/4bT2fqlrl66qsNhPv2Aj+fn5NXiWlCR9/PHHjt9///vfn9HlM/WFIjX9afz48Xr33XdVVVWlTz/9VBMnTmywra5du6q8vPUOYgvvduUNdyi4TahycnOUkJBQ5zG8n9lfA+x/3f01+3PgafsfGxurLVuad2kzwQgAr7Z//35JUmJiYr3zKysrtWHDBknODUacKTc3V1lZWS7bnr89UNHNXPdIbpbyXVjrqfhZrFI7d1fReNk52So3qpzWnr9//XfRkaT09HTt2bNHUnXfaO5r/3ShiCSNHj1aH3zwgSorK/XZZ5/p6quvlp+fX73tZWdn17pdMOBJ7FVVjn+zsrLqPIb3M/trgP2vu79mfw68af8JRgB4tZpBIEtKSuqdv3TpUuXl5SkkJEQdO3Z02navvfZa5eXlKSoqSuPHj9ejjz6q6OjmxQ0uv/TAWqmqgsOyhsY0ehXDMGSxWNSm4IAC4uu/e4ur+Ro+kvNOwGhxce3jnH7GSENqwkBJuuiii5p1CU1jQhFJCg0N1eDBg7V+/XoVFhbq22+/1YABA+ptMy4ujjNG4LF8rFbHv/Hx8XUew/uZ/TXA/tfdX7M/B562/2fynplgBIBXi42N1dGjR7Vt2zYNGTKk1rycnBzNnDlTkpSamtrs8RVOFhYWppkzZ2r48OFq06aNvvrqKz3yyCPauHGjtmzZooCAgCa32dxTAs/E07ukl/Y0fnmLxaK+kdLzm1a3XFFNVFFcqtc7TXJ3GY22e89u+QY1/fXRkMrKSi1btqzeeWlpaY7fGwopTtd2Y0KRk7exfv16SdK+ffsa3Obu3btls/HWBJ5p9tOvq6CwSO1j2yszM7POY3g/s78G2P+6+2v258Cb9p93HwC82qhRo7Rr1y7NmTNHF110kbp27SpJ2rx5s66//nrl5eVJktPuGtO3b1/17dvX8XjEiBHq2bOnxo8fryVLluiGG25wynZa2oRE6fU0qbwJJzD8Ornl6oHz2O12ZWRkSJIiIyMVHh7epPWbGopIUnLyzy+O9PT0JtcMAADQkrhdLwCvNmvWLEVFRengwYPq0aOHevXqpS5dumjQoEFKTk7WBRdcIKn+8UU2bNig6Ohox8+8efMkSW+88Uat6SdfllCfyy67TMHBwW4586O5YoOkv/Vr/B+J6ztJF8S1aElwkmPHjjkuLWto7J2GNCcUkaSYmBjH4K6t/RpkAADgfQhGAHi1hIQErVu3TmPHjlVAQIAyMjIUGRmpxYsX68MPP9Tu3bsl1R+MVFRUKD8/3/FT82GyrKys1vSKiopG1eKMS3Vc6YI4ad45UnjDQ1XI10e69WxpRorr6sKZqaqqUrt27RQREaGoqKhGr2e327Vw4cImhyJS9Wu/bdu2ioyMVFhYWLNrBwAAaAlcSgPA63Xv3l3Lly+vM72wsFAZGRny8fFRz54968wfMWKEDMM44+2///77Kioq0qBBzrt1rKsMayd9eJH0abb03gEps0iqMqQof+nSBGn8WVJ4wzc/gQeKiYnRggULmryej4+PkpOTtWnTpiaFIjUeffTRJm8TAADAFQhGAJjWzp07ZRiGunbt6jjN/0xNmjRJycnJ6tevn2Pw1blz56pPnz667rrrnLINV/O3SmPOqv6BuV1xxRWyWq3q0KGDx97eGgAAoKkIRgCY1o4dOyTVfxlNc/Xo0UP//ve/NX/+fJWUlCghIUE33XSTHnzwwVPePhWuE9IxVuctuE3+kSGqOFGs9bc/pWO7Tz+SenPX8zbjxo1zdwkAAABORTACwLRaIhi55557dM899zitPTjf0Lm3aPdrn2jvm2uVOHawhi2YruWj/9xi6wEAAMCzMfgqANNqiWAEni0gKlRRvTspbdkXkqT9H25UcFyUQpJiW2Q9v9AgXb11sa7b+ZLGf/KYrvhivq7PWKKhj091zg4BAADgjHHGCADTWrNmjbtLgIsFx0er5KejMqrsjmmFWXkKjo/WiYxcp69XXlCsfe+uU0VRqb598m3Fjeit1BlX6cu7nnPODgEAAOCMccYIAAAtKLJnRx3ZkS5JikrtpCPfpbu5IgAAAJyMYAQAYBpFWXkKbBchi/XnP39t4qNVlJXXIutJUmSPJEcYEpWarPwdBCMAAACehGAEAGAapfkFOrIjXZ0mDJckJY4drKKcI47LYYYtvE0dRg9y2npBsZGSYag494gkKbJ7oo7+cKBF9g0AAADNQzACADCVL2ctVtfrL9KV6xeq121Xav0dTzvmRfdOVlF2vtPWi+zZsdalM+UFRTp78iVO3BsAAACcKQZfBQCYSkFatlaMu7fOdP+oUBXnHFH+N2lOWy9z9VZlrt7qeMztfQEAADwPZ4wAACCpLL9Aq6572GXrAQAAwDMQjAAAAAAAANMiGAEAAAAAAKZFMAIAAAAAAEyLYAQAAAAAAJgWd6UBAHglW6C/Jqa95u4yGs0W6O/U9qxWqyZMmOC09h5bvFQniooUEhysmbdcW+exM1itVqe0AwAA0BQEIwAAr2SxWOQbFODuMtzGYrHIZnPen3lDkt2o/tdms9V5DAAA0FpxKQ0AAAAAADAtghEAAAAAAGBaBCMAAAAAAMC0CEYAAAAAAIBpEYwAAAAAAADTIhgBAAAAAACmRTACAAAAAABMi2AEAAAAAACYFsEIAAAAAAAwLYIRAAAAAABgWgQjAAAAAADAtAhGAAAAAACAaRGMAAAAAAAA0yIYAQAAAAAApkUwAgAAAAAATItgBAAAAAAAmBbBCAAAAAAAMC2CEQAAAAAAYFoEIwAAAAAAwLQIRgAAAAAAgGkRjAAAAAAAANMiGAEAL/DQQw/JYrHU+dm7d6+7SwMANGDFihXq06eP/P39lZSUpHnz5rm7JLjQF198ocsvv1yJiYmyWCz629/+5u6SXOqxxx7TkCFDFBERofDwcA0bNkwfffSRu8tyqVdffVX9+/dXRESEAgMD1b17d82bN0+GYbi7NLdYs2aNrFarOnfu7PJt21y+RQBAi0hKStJXX31Va1pMTIybqgEAnMqWLVt0+eWX66677tKSJUv03//+V1OnTlVQUJCmTp3q7vLgAoWFhUpJSdFvfvMb3XHHHe4ux+XWrFmjG2+8UQMHDlRQUJBeeOEFXXbZZfr888917rnnurs8l2jbtq3uv/9+devWTf7+/lq3bp3+8Ic/yGq16vbbb3d3eS6Vm5uryZMn6+KLL9aePXtcvn2CEQDwElarVbGxse4uAwDQCPPmzdPAgQP1yCOPSJK6d++unTt36tFHHyUYMYkxY8ZozJgxkqS7777bzdW43sqVK2s9njt3rj766CO98847pglGLrnkklqPk5OT9d5772nt2rWmCkbsdrsmTZqkadOmqbS01C3BCJfSAICXyMzMVEJCghISEjR69Gh9+eWX7i4JANCADRs26NJLL6017dJLL9X+/fuVmZnppqoA97Hb7SooKFBwcLC7S3ELwzC0adMmbdiwQSNHjnR3OS718MMPy2KxuDUg5IwRAPACgwYN0ksvvaSUlBQVFBRo8eLFOu+88/TRRx/poosucnd5ANBqFBaV6INPv6xzjX9Jaanj33//Z3WdxycbM3KwwkPbnHI7OTk5dc7yq3mck5OjhISEM9oPNF/6wRx9tW1nnemNfQ0EBvhr/KhzZbW2zu+gKyor9Z9VG1ReUVFrelP6wPBBvZXQvmmX886ePVvHjh3TzTfffAbVO8em7bu0d39WrWn17W9Dz0FCbIyGn9O7Uds6fvy44uPjVV5eLrvdrgcffFAzZsxwxm40W97R41r1xeY60xv7GvDx8dH4i85VUID/abf12Wef6bnnntPXX38ti8XipD1oOoIRAPACNafi1jjvvPOUmZmpxx57jGAEAJqgTXCgQtsEa93mb+udX1FZpW9/2Nfg43P6dD9tKALP1iG+nVau3aQD2T/VO/90r4Hrxl3QakMRSfK12ZTQPkbvrVpf7/zT7X+XpHjFxUY3aZvPPPOMZs+erffff98jQsGzO3XQR59vUnFpWZ15v9zfX07ztVl10bABjd5WSEiItm/fruLiYn355Ze65557FBcXp9/97ndnthNnIDoiTD4+Ptr+ff2D+J/uNTBqWP9GhSJ5eXmaNGmSXnrpJbdfDt56eywA4JSGDBmijIwMd5cBAK3OxcMHqF10RJPXi4oI1diRgxu1bPv27ZWbm1tr2k8//eSYB/ex+vjomstGyM+36d8hp56drN7dO7VAVa51Tp/u6pZ8VpPXC/D306/GjJBPE775f/zxxzVz5ky9//77GjVqVJO32RJCQ4J1xSXnNWvdMSMHKyYqvNHL+/j4qHPnzkpNTdXUqVM1a9Ys3Xvvvc3atjONv+hchYU0/bKms9rHaOSQvo1a9rvvvlN2drYuu+wy2Ww22Ww2/d///Z/S0tJks9n073//u8nbby6CEQDwUtu2bdNZZzX9TQ0AmJ2vzaZrx10gq0/j3ypbLBZdO3ak/Px8G7X8ueeeq48//rjWtI8++kiJiYke8Y252UVHhGnsBUOatE5ImyBdcfEwt14O4CwWi0UTRp+voMDTf+t/sisvHtakD9MPPPCA/vrXv2rFihUeE4rUSD07WX17NO22sV2SEjS4b8oZbddut6v0f5eouFNQgL+uHjOiSev42qy65rKRjT52Dhw4UDt27ND27dsdP1OnTtVZZ52l7du3a+zYsc2ovHlMFYxkZGTIYrEoKSnJ3aW0Gg899JAsFoseeuihJq9rsVi84g8D0Br86U9/0po1a7Rv3z5t375d06ZN0yeffGLK2/8BgDPEtY3SRec1/nT4kYP7qEN8u0Yv/8c//lGbNm3Svffeqx9++EGvvPKKFi1apD//+c/NKRctYFDvs3V2pw6NXv7q0ecrKDCg0csXFhY6PgyWl5crNzdX27dv19699V++4GqhbYJ0ZRPOmujdvZN6pzQ+SLjjjjv02GOP6dVXX1W3bt2Um5ur3NxcHT9+vDnltojxoxp/1kRggL9+Neb8Jn3+efDBB7V69Wrt27dPP/74o55//nnNmTNHkydPbm7JTtU5KV7n9u/Z6OXHjBysmMjwRi8fHBysnj171vpp27at/Pz81LNnT4WFhTWj6uYxVTACAM01f/58PfTQQzp27Ji7S6lXTk6Ofvvb36p79+66+OKL9eOPP2r16tUaN26cu0sDgFZr+KBUJSWc/rr3+NhoXXhu/ya1PXDgQL333ntavny5evfurQceeEB///vfuVWvB7FYLJpw6fBGnTUxpF+Kujbx0pMtW7aob9++6tu3r3JycvT000+rb9+++v3vf9/ckp2uV7dk9e3R5bTLhbYJ1uUXNe0WuwsWLFBpaamuvPJKtW/f3vHjSbepDQzw19VjRzRq2SuaeLaMJBUUFGjq1Knq0aOHBg8erOeee06PPPKI5s2b14xqW8al5w9S20ZcGtS145mfLeNODL4KAI0wf/587d+/X1OmTFF4eLi7y6ljyZIl7i4BALyOj4+Prh47QgteWqby8op6l7HZrLp27MhmDbY5duxYl54qjqYLaROkqy4Zrtfe+6TBZaIjwzR6ROPGljnZiBEj6tz9yBNdftG5Sj+Yo2MFhQ0uc/WYpp0tI6lV7LskdU6M17kDemrDlu8aXKZPSudmjS3z5JNP6sknnzyT8lqcr69N11w2Us+8+p7s9vr/z4IC/PWr0U07W6YhDz30ULOuVjhTnDECACZSVlauNz5Yo8zcw+4uBQBahajwUI07xVgTo88/R22bMVArWo+e3TqqX8+u9c7zqRlbphkDtbYWAf5+pxxrYmj/HurS0bvHxbl0+CC1jaq/n4e2Cdb4Jp4t09okxMac8qy4Ky45T6HNGKjVkxCMAICJfPX199r+/V698cEa2VvJNzUA4G4DUrupe+fEOtM7J8ZrSP8ebqgIrjZ+1NB6b8M8cmhfnRXX1g0VuVanxDgNG9irzvSYyDBdev45bqjItXx9bbp23Ej5+NQ9I+Lqsec36ta0rd2IwX3UoZ7Xep+Uzko9O9kNFTlXqw9Gjh8/rvvvv189e/ZUUFCQQkJCNGDAAD3xxBMqK6t73+mTPf/88+rfv7+Cg4MVGRmpcePGaevWrfUuW1xcrNmzZ6tv375q06aN/P39FR8fryFDhuj+++9Xfn5+nXXsdrteeeUVXXDBBYqKipK/v7+SkpI0depUHThwoN7tnDxg6QcffKBRo0YpKipKFotFa9eu1dChQ2WxWPTSSy81uF/r1q2TxWJRXFycqqqqHNNXr16t2267TX379lV0dLT8/f2VmJioG264QT/++OMpnytJys/P17Rp09ShQwf5+/urY8eOmjlzpgoKCk67bn3+85//aOzYsY4BduLj4zVp0iTt3LmzWe2dbMqUKbJYLHr55ZeVmZmpKVOmqH379goKClK/fv301ltvOZY9cOCAfve73yk+Pl4BAQFKTU3V66+/fsr2v/jiC1199dWKi4uTn5+f2rZtqyuvvFJffvllvcvn5ubqqaee0ujRo5WcnKyAgACFh4fr3HPP1XPPPSe73V7vepmZmfrDH/6gzp07KyAgQMHBwUpKStKll16qhQsX1lr25ZdflsVi0ZQpUxqsu6EBcU/3uqtRVlamBQsWaOjQoQoPD1dAQIC6deummTNnKi8vr067vxzw+OWXX9aAAQMUHBys2NhY3XjjjTp06JBj+X/9618aOHCg2rRpo6ioKF1//fV1bmV4soKCAj388MPq16+fQkJCFBQUpNTUVD388MMqKiqqs/zatWtlsVgcp64+99xz6tu3r4KCghQZGakrrriizuuv5nndv3+/JKljx46O56vmNVZj165d+u1vf6vExET5+fkpNDRUycnJuuKKK/Taa681uB+uUlZWri82fSNJunBovybdSg8AzKxmrIngoJ8vFaj+Fv18jqUmEeDvp2vGjtDJ/9sJ7WN0wZB+bqvJ1S4ZPrDWbax9fCy69rILvPpsmZPFt4vWRcNqD8g8tH9PdUny7rNlalTfxnqkfE/6/w4LafrYMh7LaMXS09ON5ORkQ5JhtVqN3r17GykpKYbFYjEkGYMGDTKOHj1aa3lJRmJionH77bcbkoy4uDhjwIABRmhoqCHJ8PX1Nd5///1a26moqDCGDBliSDIkGZ06dTIGDhxodOjQwbDZbIYk46uvvqq1zokTJ4yLLrrIkGRYLBYjPj7e6NOnjxEcHGxIMiIiIoxNmzbV2aeabTz++OOGJCM6OtoYOHCgER8fb3z22WfGU089ZUgyRo0a1eDzcssttxiSjD/+8Y+1plutVkOSERMTY6SmphqpqamO/W7Tpo2xfv36Om09+OCDhiTjtttuM5KTkw0fHx+jZ8+eRs+ePR3Pc48ePYy8vLwG9+WXKisrjeuvv94xv23btkbfvn2NsLAwQ5IREBBgfPDBBw3uX2NMnjzZkGQ8+OCDRtu2bY3AwECjf//+RmxsrGO7r7/+uvHDDz8Y7dq1q3f+v/71r3rbvueeexzLREZGGn379jViYmIMSYaPj4+xePHiOus8/PDDjn1LTk42Bg4caHTs2NHxHF511VWG3W6vtU5GRoajXT8/P6N79+5Gv379jHbt2hkWi8Xw9/evtfxLL71kSDImT57c4PPS0P/J6V53hmEYubm5Rt++fR37mZiYaPTq1cvw9/c3JBkdOnQw0tLSarV7cp+bOXOmIcno2LGjkZqaavj5+RmSjF69ehmlpaXGHXfcYUgykpKSas1PSUkxSktL69T8448/GklJSYYkw2azGZ07dzZSUlIMX19fQ5KRmppq5Ofn11rns88+MyQZ5/9/e3ceHtPV+AH8O5NEFtkIkUTI0sgiQmSx1BJBLUGUxlb60tbWqreNttLSWlpULRGKopRYqrYSKtraEgQvTSQkZCGJJURUBSERSc7vj/zmNmNmsgmh8/08j6fPnHPPPefemVH3O/ec6+cnfQbt7e2Fp6enMDAwEACEmZmZSEtLk9pERkaKDh06SMfp4+MjOnToIP2JjIwUQghx+vRpYWRkJAAIIyMj0aJFC+Hp6SksLCwEAOHi4qLxfXleDp84I0LmrhTzV/0sioqLa3s49JKZvXSjCJm7UsxeulHtayJtkJiaIULmrhQhc1eKM0lpFTegf51fD54QIXNXii8WrBY5f92p7eE8d9eyb4kp834QIXNXiv3H/qzt4Tx3RcXFYtn6XSJk7kqxYNUWUVj4uLaH9NydPJMk/T2YlnGttodTY17qYEQRVvj4+IjLly9L5QkJCdIF04gRI6RyxUWarq6u0NXVFevWrZMuRvPz88WYMWOk0OLmzZtSux07dggAokmTJiIpKUlpDPfu3RM//vijSE9PVypXXHS1a9dOnD17ViovLCwUX3zxhXRB9uQFn+ICtU6dOmLp0qWi+P8vXkpKSkRBQYG4deuW0NXVFXK5XFy/fl3lnBQWFor69esLAOLPP5X/slqxYoW4evWqyvYrV64Uurq6wtnZWeXiXBGM6OnpCTc3N5GSkiLVXbhwQTRr1kwAEG+++abKWDRdhH/55ZfSheLRo0el8pKSErF06VKho6MjzMzMlN6DqlIEI3p6emLQoEEiNzdXqvvqq68EAGFrayt8fHxU6mfMmCGFZsVPXDyuWbNGABDW1tYiIiJCqW7Lli3C2NhY6OnpiXPnzinVHT16VBw8eFA8fqz8l2daWpro2LGjACA2blS+uJg4caIAIHr06KFygX/lyhWxYMECpbKaCEY0fe5KSkqEn5+fACD69OkjMjIypLb3798X77zzjgAg2rdvr7Tfst85U1NTsW/fPqkuMzNTCjYHDBggTExMlOozMjKk7/H333+vtN+HDx8KFxcXAUCMGjVK5OTkSHU3b94Uffv2FQDEsGHDlNopghE9PT1haWkpoqOjpbrbt2+LTp06afw829nZCQBKx15Wv379pPN///59pbrk5GSxfPlyte2el4KCR2Lm4nUiZO5KEZeYWqtjoZcTgxGiUtv2RolNu/ar/JuJtEPh48cidPVWcTw2sbaHUmsOHY8TS8N/0dofWW79nSumL1orrt7IqXjjf6GSkhLx49ZIsftATG0PpUbJhHg5J5kfPnwYXbt2hY6ODlJTU+Ho6Ki2Xi6X4+LFi3BwcEBmZiYcHBwAABMmTMDSpUuV2pSUlMDNzQ2pqamYOXMmpk2bBgCYO3cuPv/8cwQHB1fq0UlJSUlo0aIFrKyscO7cOTRo0EBlm9dffx0RERHYuHEjhg8fLpUrpjOoG59C3759sXfvXoSGhiI4OFipbs+ePQgMDISrqysuXLhQ4VgV3nrrLWzcuBEnTpxAu3b/rKo9Y8YMzJw5EwBw+vRp+Pgo3z4WExODjh07Qi6X49KlS9KUibLHUvYj9tdff6Fp06aQyWSIj49Hs2aqj//66KOPsHjxYsyaNQtTp06t9DGUNWrUKISHh8PKygoXL15E3br/LAZUXFwMOzs7ZGVlqa0vKiqCnZ0drl+/jri4OLRu3RoA8PjxYzg4OCArKwtHjx5Fx44dVfoNCwtDcHAwRo8ejR9++KFSY7106RKcnJzQs2dP/Pbbb1J5r1698PvvvyMiIgKBgYEV7mfdunV4++23MXLkSKXpHWWpe0/Klmv63EVGRqJPnz5o0aIFTp8+DQMD5VXHi4uL0aZNG8TFxeHYsWPo0KH0lrqy37mFCxdi0qRJSu1WrFiB9957T2P9999/j/fffx+BgYGIiIiQypcvX44JEyagR48e+O2331SmB+Xl5cHNzQ3Xr19HZmYmmjQpfXxeVFQU/P39AQA///wzhgwZotTuzJkz8PLygrm5Oe7cuaNUZ29vj8uXLyMjI0Ppc67g6uqKlJQUJCQkoGXLlir1T+O78F9wPy//qfbxqLAQjwofQy6TwbiuIQDe+k1Vc//BQwghIJPJYFLXSOU1kbZQ/D+0Jp6+QC+nkpISjdOTtYEo/XEdcvlLvypDtZWUlGj58QvIZC/e34MmxoaYOHJgtdq+tBPCFBeQgYGBKqEIAPj7+6NVq1ZISEjAH3/8gXHjxinVT5w4UaWNXC7HBx98gP/+97/Yt2+fFIwoLqoOHjyIv//+G/Xr1y93bL/88gsA4I033lAbigDAgAEDEBERgaioKKVgRKG8dSKGDx+OvXv3YtOmTSrBiGJtDHX7BID4+Hhs27YNSUlJyM3NRVFREQBIa57Ex8crBSMK7dq1UwlFAKBDhw7w8vJCXFwcfv/9d5Xz/KR9+/YhPz8fffr0URuKAKXnZvHixYiKiqp2MKIwbNgwpdADAHR0dNCyZUtkZWWprdfV1UXLli1x/fp1pKenS8HIyZMnkZWVBXd3d7WhiGLswcHBSutyKDx48ABbt27FkSNHkJWVhfz8fKWAIj4+Xml7xedu586dCAgIgK7us/+6avrc7dixAwAwcuRIlVAEKD2ngYGBiIuLQ1RUlBSMlPXuu++qlHl5eVWqPj09Xe14xowZo/YvZGNjY3Tv3h3r1q3DkSNHVL4P5ubmKqEIALRu3Rr6+vrIzc3F7du3YWFhobKNJk2aNEFKSgq2b98ODw+PGv0fxf28fNzLU10zpTpKhMC9vIc1si/STkIIpc/jk6+JiIiIXjYvbTCSmpoKAGjRooXGbTw8PJCQkKCysKienp7Gi/LmzZsDgFKbAQMGwNHREWfPnoWtrS1ee+01dO7cGX5+fvD29la5ADp79iyA0hBA0wV0bm4uACArK0ttvZubm8bj6t+/P4yNjREbG4uUlBS4uLgAKP2VfM+ePQDUByPBwcEICwvTuF8AaheRBf45L5rq4uLiKrWAq+LcxMfHazw3BQUFADSfm6p45RX1zxNv2LBhufWWlqUrLufl/fO8dsXYs7OzNY5dEXQ8OfZz586hT58+uHr1qsaxPnnuJ06ciPXr12PdunWIjIxE79690bFjR/j7+2sc99PS9LlTHPvatWuxa9cutdvcvHkTgPr3rUGDBjAzM1MpV7wPmurVvQ9lxzNnzhyNn2nFYqnqxuPk5KS2jaLPq1evIi8vr0rByKRJk3Dw4EF8/fXXCA8PR69evdCpUyf4+/ujcePGld6POibGhk/VnneLUE3gHSNERET0InuafzO/tMHI/fv3AQCNGjXSuI2iTrGtgoWFhcZbn9S1MTIywrFjxzBt2jRs27YNu3fvxu7duwEAtra2mDZtGsaMGSNtrwg90tPTVX7pftLDh+p/uX3yLoayjIyMMGDAAGzYsAGbNm3CV199BaD0zoKHDx/i1VdflaYvKGzatAlhYWEwMjLCt99+ix49esDW1haGhoaQyWSYNm0avv76azx+/Fhtn4oLVHU0nWd1ygZCFQUfms5NVWg6j4owq6L6snd0KMZ++/ZtxMTElNtvfv4/0x6Ki4sxaNAgXL16FT179kRISAg8PDxgbm4OXV1dlJSUQEdHR7p7R6Fly5Y4duwYZsyYgf379yM8PBzh4eEAAF9fXyxcuBCdOnUqdxxVpel8KI79/PnzFe5D3ftWk+9D2fGcOXOmRscDQPq7oaqzDHv37o3ffvsNs2bNQkxMDFatWoVVq1ZBJpOha9euCAsLKzfILU91bwkESp9E8+3KzQCAQX26oLW7+lCYqCJzlm3CvbwHMKlrhCkThqu8JiIiInpZvbTBiImJCYB/fqVWR1Gn2Fbh9u3bGueFaWpjbW2NH374AStWrEB8fDyOHDmCXbt24ciRIxg7diwMDQ0xYsQIAKW38QPAkiVL1E7ZqQnDhw/Hhg0b8NNPP0nBSHnTaDZs2AAAWLBggbSmQ1ma7hRRKPtI1SdpOmfqKM7NpEmTsHDhwgq3f5Eoxj5w4EBpKkdlnD59GikpKWjatCl27dqlMhWlvHPv6+uLvXv34uHDhzhx4gSio6OxZcsWnD59Gj179kRCQoJ095OmEEFB3eNrK0tx7Lt370a/fv2qvZ+aYmxsjNzcXJw9exYeHh61PRxJjx490KNHD9y9excxMTGIiorCTz/9hIMHD6J79+5ISkqq0l0oCk+zxkjZu0X2Rf0P+6JOVWs/RPcfPJT+O2fZJpXXRERERLVJK9cYcXZ2BgAkJiZq3EZRp5hqovD48WOkpaWplAP//CKurg4oXUvB29sb3t7eCA4OxuTJkzF//nz88MMPUjDi7u6OXbt2lTu2p9W9e3c0atQIly5dwsmTJ+Ho6IiDBw9CT09P7doJGRkZAKBxCsjJkyfL7a+8OwUqOmdlubu7Ayj/fXtRVXfsinPv4+Ojdn2Ois49UHqXULdu3dCtWzdMmzYNnTt3xokTJ7Bx40ZpcVzFXRCawsK0tLQqjbssd3d3xMfHIzEx8YUIRtzd3RETE4PExMTnFoxUZc0QMzMzBAQEICAgANOmTUOrVq2Qnp6O3bt34+23365y3zWxxgjXFqGawjVGiIiI6N/mpQ1GevfujXnz5mH37t1IT09XWYA1Ojoa8fHxkMvl6NGjh0r7ZcuWYcmSJUplJSUlWLZsGYDSJ4JURocOHTB//nxcv35dKgsKCsLs2bPx888/Y/r06bCxsanq4VVIR0cHQ4cOxeLFi7Fp0yY4OzujqKgIffv2VfuLtKFh6Xyr7OxslQvJ6OhoxMXFldvfyZMnERsbC29vb6Xy48ePIy4uDjKZTO15flLfvn2hr6+PgwcPIiEhAa1ataqwzYuiU6dOaNSoEVJTU/Hrr7+ib9++lWpX9tyrs2jRoiqNQ1dXF23btsWJEyeUPneKdTPi4+NRWFiIOnXqKLX7/vvvq9RPWUFBQdi0aRNWrVqFiRMnSneQ1JagoCDExMRg8eLFGDRo0HNZmFbxPpadJlUZxsbGaNmyJdLT05Xer6qo7nxJri1CNYlrjBAREdGL7KnW5XuOjwauca+++qoAIHx8fMTly5el8nPnzglHR0cBQIwYMUIqz8jIEACErq6u0NPTE+vXr5fq8vPzxfjx4wUAYW5uLrKzs6W6hQsXioULF4qsrCyl/nNycoS/v78AIIYMGaJU99ZbbwkAwtXVVRw7dkxl7PHx8eLTTz9VqQMgKvu2nDp1SgAQDRs2FL6+vgKA2Lx5s9ptP/jgAwFA+Pr6Kh3bsWPHRKNGjYSBgYEAIKZPn67Ubvr06QKA0NPTE+7u7iItLU2qS0lJEc7OzgKAGDp0qEqfmo7lyy+/FACEjY2N+PXXX0VJSYlSfWpqqvjqq6/E9u3bK3Ue1Bk5cqQAINauXVuj9WvWrBEAhKmpqVi/fr0oKipSqr969aoIDQ0V33//vVSWnZ0tdHV1BQCxZs0aqTwvL0+MHTtWOvdPnquxY8eKn376Sdy/f1+pPCEhQdjY2AgASv0UFxeLJk2aCAAiODhYFP//s+WLi4vF8uXLNfYjRMWfu5KSEuHn5ycAiPbt24tz584p1RcXF4vjx4+LcePGiUuXLknliu+cnZ2d2v1Wt/7BgwfCxcVFABD9+vUTGRkZSvWFhYXiwIEDYujQoaKgoEAqP3z4sAAg/Pz8NB6rnZ2dAKCyz759+woAYunSpWrbDR48WERERCj1J4QQhw4dEsbGxgKA2Ldvn8Z+a1pBwSMxc/E6ETJ3pYhLTH1u/dK/1+ylG0XI3JVi9tKNal8TERERvaxe6mAkIyNDODg4CABCR0dHtGrVSri7uwuZTCYAiDZt2og7d+4oba+4yPrwww8FANG4cWPh6+srTE1NpdBk586dSv0otgUgmjRpItq0aSOaN28u9PT0BABhbW2tdDEohBAPHz4U/fv3l9pZW1uLNm3aCE9PT2FmZiaVP3mhVJVgRAghBRMAhImJiXj48KHa7a5cuSIaNGggAAgDAwPRqlUr4eTkJAAId3d38fHHH5cbjEycOFE4OjoKuVwuPDw8hIeHh5DL5QKAcHNzE7du3VLpU9OxFBcXi3Hjxkn1FhYWwtfXV3h5eUljfPKiv6qeVTAihBCzZs2SPmOmpqbC29tb+Pj4SGEFABESEqLUJiQkRKqztbUVPj4+wtjYWMhkMilsefJctWrVSvpsOzs7i7Zt24pXXnlF2rZTp07i0aNHSm22bNkija1evXrCx8dHNGzYUMhkMrF69epqByNCCHHr1i0pjFR8j9q2bStatmwp6tatK5VfuHBBavOsghEhhLh06ZJwdXWV+nVychLt2rUT7u7uQl9fXyrPz8+X2jxNMLJp0yZpn82bNxd+fn7Cz89P+g4rvtd16tQRzZs3F23atJGCKk3h4bN0+MQZETJ3pZi/6mdR9P8hGdHTYDBCRERE/1bqH83ykrC3t0dcXBymTp0KFxcXpKamIjMzE61bt8b8+fMRHR0Nc3NztW3DwsKwatUqWFpaIikpCXK5HH369EFMTAxef/11pW3Hjx+Pr776Cn5+fgCAhIQEZGZmwsXFBZMnT0ZCQoLKVB5DQ0Ps3LkTv/zyCwIDAyGEwJkzZ5CWlgYbGxuMHTsW+/btQ7du3Z7qHJRdaHXAgAHS7f5PatKkCU6cOIFBgwbByMgIycnJAIDJkyfjxIkTFU6NqF+/Pk6dOoVx48bhzp07SElJga2tLT7++GOcOHECDRo0qPSY5XI5VqxYgUOHDmHIkCEwNDREQkICLly4AHNzc4wYMQI7duzAf/7zn0rv83maOnUq/vzzT7z99tuwsLBAYmIizp49Cz09PbzxxhtYv349Pv/8c6U233zzDZYuXYrmzZsjJycH6enpaN++PX7//Xe88847avtZtGgRgoOD4enpibt37yI2NhZ//fUXXn31VSxZsgQHDhxQmS4zePBgREREoH379igoKEBKSgpatGiBP/74A+++++5THXeDBg0QHR2NdevW4bXXXsODBw/w559/IiMjA05OTvjwww8RHR0trf/zrDk6OiIuLg6LFy9Gp06dcPv2bZw+fRpZWVnw8PDA559/jtOnT6td16U63nzzTSxbtgytW7dGZmYmoqOjER0dLU2RCg8Px/jx4+Hq6oqcnBzExcXhwYMH6Nq1K9avX4+ffvqpRsZRGY8fF+HIqQQAQLdXvaCj4SlcREREREQEyISo4jMpiYjohZd5LRunzyZjYK/ODEaoRigez2tqXFfpcb2K10REREQvq5d28VUiItLM3tYK9rZWtT0MIiIiIqIXHn9GJCIiIiIiIiKtxTtG6IWWnZ2NoKCgSm8fEBCAKVOmPMMRERERERER0b8JgxF6oRUUFCAmJqbS2zs5OT3D0RAREREREdG/DYMReqHZ29uD6wMTERERERHRs8I1RoiIiIiIiIhIazEYISIiIiIiIiKtxWCEiIiIiIiIiLQWgxEiIiIiIiIi0loMRoiIiIiIiIhIazEYISIiIiIiIiKtxWCEiIiIiIiIiLQWgxEiIiIiIiIi0loMRoiIiIiIiIhIazEYISIiIiIiIiKtxWCEiIiIiIiIiLQWgxEiIiIiIiIi0loMRoiIiIiIiIhIazEYISIiIiIiIiKtxWCEiIiIiIiIiLQWgxEiIiIiIiIi0loMRoiIiIiIiIhIazEYISIiIiIiIiKtxWCEiIiIiIiIiLQWgxEiIiIiIiIi0loMRoiIiIiIiIhIazEYISIiIiIiIiKtxWCEiIiIiIiIiLQWgxEiIiIiIiIi0loMRoiIiIiIiIhIazEYISIiIiIiIiKtxWCEiIiIiIiIiLQWgxEiIiIiIiIi0loMRoiIiIiIiIhIazEYISIiIiIiIiKtxWCEiIiIiIiIiLQWgxEiIiIiIiIi0loMRoiIiIiIiIhIazEYISL6Fzhy5Aj69+8POzs7yGQyzJo1q7aHRFShyMhIeHp6Ql9fH/b29ggNDa3tIREREZEWYjBCRPQvkJeXh+bNm2PevHmwsrKq7eEQVejPP/9E//790bt3b8THx2PGjBmYMmUKVqxYUdtDIyIiIi2jW9sDICKipxcQEICAgAAAQEhISC2PhqhioaGh8PX1xTfffAMAcHNzQ1JSEubOnYvx48fX8uiIiIhImzAYISIiIsnde3m4eTtXpbyouFj6b2rGNZXXZdk1bgT9Onrl9hMTE4N3331XqaxXr15YsGABrl27Bltb26c4CiIiIqLKYzBCREREEr06etgeGY17eQ/U1j/ML8CPWyM1vnZ2aIJm9o0r7OfGjRsq074Ur2/cuMFghIiIiJ4brjFCREREEiMDfQzq41fttkEBfpDJZDU8KiIiIqJnh8EIERERKWlmb4tXvVtUud2Anp1gamxUqW2tra2RnZ2tVHbz5k2pjoiIiOh5YTBCREREKnr7tUHD+uaV3r61uxM8XB0rvX2HDh3w+++/K5X99ttvsLOz4zQaIiIieq4YjNALJTMzEzKZDPb29rU9FKKXSl5eHuLj4xEfH4/CwkJkZ2cjPj4eFy9erO2h0UtKT08XQ/r6Qy6veFqMmUldBHbvUKX9BwcH49SpU5g6dSqSk5MRHh6O7777Dp999ll1h0xERERULTIhhKjtQRApZGZmwsHBAXZ2dsjMzKzt4RABAHJzcxEWFgZzc3N89NFHtT0ctaKiouDv769S7ufnh6ioqOc/IPrXOBgTh/3H/ix3m9FD+8DJruIFV5+0d+9eTJkyBcnJybCyssKHH36ISZMmVXeoRERERNXCYIReKAxG6EX0b/pcCiFw8XIWXrFrDDkXyKRKKC4pwYqNu3H1Ro7a+o4+Hujbrf1zHhURERFRzeFUGiIiLZKacQ1rtkRixcbdKGEuTpWgI5djSF9/6OnpqtRZWtRDTz/fWhgVERERUc1hMEJEpCWEEDhwLBYA0LSxJe8YoUprUN8MffzbKZXpyOUY0s8ferqqgQkRERHRy4TBCAEAsrKy8M4778DKygqGhoZwc3PDN998g6KiInTp0gUymUxpnYKyZQkJCRg4cCAaNmwIQ0NDeHp6Yvny5SgpKdHYX2RkJDp37gwTExPUq1cP3bt3x+HDh2vseOzt7SGTyZCZmYmYmBj06tUL9erVg7m5OXr27Im4uDhp25iYGAQEBMDCwgJ169ZF165d8eefmufTl5SUIDw8HF27doWFhQX09fVhb2+P8ePH48qVK2rbJCYmYsaMGejQoQMaN26MOnXqwNLSEn379sX+/fs19nXixAkEBQXBxsYGenp6MDc3R7NmzTBs2DDs3btXadtRo0ZBJpNh3bp1avcVFRUFmUyGLl26aCwvLi5GWFgYWrduDRMTE8ieuHDOycnB5MmT4e7ujrp168LExAS+vr747rvv8PjxY5U+161bB5lMhlGjRqGgoABffvklnJycYGhoCEdHR3z99dcoLi4GAOTn52PGjBlwdnaGgYEB7Ozs8OWXX6rdr0JGRgbef/99aZ9mZmbo3LkzNmzYAHWzBGfMmAGZTIYZM2YgLy8PISEhcHR0hL6+PmxtbTFhwgTcuXNH5bw6ODgAAC5fvgyZTKb0p+zUmsjISAQEBMDS0hJ6enqwsLCAm5sb3nnnHcTExGg8juclNeMart7IgZ6uDvzatKrt4dBLpq2nG1wcm0ivu3f0RuNGDWpxREREREQ1RJDWS0tLE40aNRIARJ06dYSXl5dwdHQUAET//v1F586dBQBx+PBhqY2fn58AIObMmSMMDQ2FoaGh8Pb2Fvb29gKAACCGDRsmSkpKVPpbsmSJtI2lpaXw8fER5ubmQi6Xi/nz5wsAws7O7qmOyc7OTgAQixcvFrq6uqJBgwbC29tbmJqaCgDC1NRUXLhwQWzbtk3o6uoKCwsL4e3tLUxMTAQAYWxsLM6fP6+y3/v374vXXntNABAymUw0btxYeHp6irp16woAol69euLUqVMq7bp16yYACDMzM+Hq6iq8vb2FlZWVdB7CwsJU2uzevVvo6OhI423VqpXw8PAQ5ubmAoDo2bOn0vYjR44UAMTatWvVnpPDhw8LAMLPz09teefOnUVgYKAAIOzt7YWPj48wMzOTtjt+/LiwsLAQAIS+vr5wc3MTTk5OQi6XCwCia9euoqCgQGnfa9eulT4LHTp0EHK5XLRo0UI4OTkJmUwmAIgxY8aI/Px80b59eyGXy4W7u7to1qyZVP/OO++oPZ5du3YJQ0NDAUAYGRkJDw8PYWdnJ7UbMWKEyudv+vTpAoD46KOPRIsWLaT+XF1dpXPdunVr8ejRI6nN7NmzhY+Pj3TcHTp0UPpz48YNIYQQy5Ytk95PCwsL4eXlJdzc3ISxsbEAIMaNG6f2OJ6XkpISsTR8pwiZu1LsOXi8VsdCL6+79x+ImWHrxPINu0RRcXFtD4eIiIioRvD+Vy0nhMCIESNw8+ZNdO7cGVu3bkWjRo0AAH/88QfeeOMNPHr0SGP76dOno1+/fvjxxx9hZmYGANizZw+GDBmCzZs3w9/fH2PGjJG2P3v2rPTEgblz5+LTTz+FXC7Ho0eP8Mknn2DKlCk1enyffPIJZs2ahU8++QQ6OjrIz8/H4MGD8euvv2LChAmIjY1VqQ8KCkJkZCRmzpyJn3/+WWl/77//Pvbv34927dph1apV8PDwAAA8fvwYX331FWbNmoXBgwcjOTkZ+vr6Urvx48cjNDQULVu2VNpfVFQUhg0bhk8++QT9+/dXekzx1KlTUVxcjClTpmDatGlK+4uLi0NSUlKNnquYmBjUq1cPUVFR8PPzA1B6FwcA3Lx5E/3798ft27cREhKCL774AsbGxgCA9PR0DB8+HIcOHcLMmTMxZ84clX1v374dzZo1Q0pKCpycnAAABw8eRO/evbF69Wrk5OTg7t27SE5ORrNmzQAABw4cQEBAAH788Ud8+umncHV1lfZ3/vx5DB06FEVFRViwYAEmTpyIOnXqAADi4+MxZMgQbNy4EZ06dcLYsWNVxrNs2TJ4eXnh0qVL0jk/d+4cevbsiTNnziA8PFz63E6ZMgVvvvkmHBwcYGVlhWPHjqnsr6ioCF988QUAYPny5Rg7dix0dHQAlH7Hjh49itu3b1fxHfnHd+G/4H5efrXbA0BRcREe5pd+lxPOX8LZC+lPtT/SXgLA7Tv38O33m2t7KEREREQSE2NDTBw5sFpt+VQaLad4xKeenh4yMjLQuLHy4xYXLFiATz/9FABw+PBhaRpGly5dEB0djYYNG+Ly5cswNDRUajd79mx88cUXcHR0xMWLF6UpGaNGjUJ4eDgCAgJUpoIIIdCyZUskJiY+9dM/7O3tcfnyZfTr1w+7d+9WqktOToabmxsAqK2/cOECmjdvDjMzM+Tm5krlSUlJaNGiBaysrHDu3Dk0aKB6C/nrr7+OiIgIbNy4EcOHD6/UWNesWYPRo0fjm2++wWeffSaVGxgY4NGjR7h79y5MTU0r3I/i3K5duxajRo1SqVe8108+vrXsY163bduGoKAglbaTJ0/G/PnzMXr0aPzwww8q9deuXYOrqyvkcjlycnJgYGAAoHQqzdtvvw2ZTIZTp07Bx8dHqd3QoUOxZcsWjfVDhgzB1q1bERoaiuDgYKl88ODB2LZtG2bNmoWpU6eqjCcuLg4+Pj545ZVXkJaWJpXPmDEDM2fOhL6+PlJSUmBnZ6fUbtGiRZg0aRL69++PXbt2SeUVPZUmOzsb1tbWqFevHv7++2+V+qc1Z9km3Mt7UOP7JSIiIiL6tzA1rospEyp3DfYk3jGi5fbt2weg9IL+yVAEAMaOHYspU6ZoXOfh3XffVQlFgNI7K6ZPn4709HSkpqbCxcUFAPDbb78BACZMmKDSRiaTYcKECXjvvfeqfTxPGj16tEqZq6srDA0NkZ+fr7bezc0NhoaGuHv3Lm7fvg0LCwsAwC+//AIAeOONN9SGIgAwYMAAREREICoqSiUYycrKwk8//YTY2FjcunVLuhPn7t27AErvdCirSZMmuHjxIrZu3ap2nDXN1NQUAwYMUFu3Y8cOAMC4cePU1tva2sLX1xdRUVGIjY1Fhw4dlOo9PT1VQg8A8PLywpYtW8qt37p1K9LT/7m7obCwEHv27IFMJlN7N4iinZ2dHS5evIisrCyVz3avXr1UQhEAaNeudHHJS5cuqd2vJg0bNoSBgQFyc3Oxf/9+vPbaa1VqXxETY9XvWFWUvVvEpK6RytoxREREREQvu6f5NzODES2XkpICANIdFE8yNTWFra0tMjIy1NY3b95cbXm9evVgbW2Na9euISUlBS4uLsjNzcXNmzfL7U/T/qrrlVdeUVvesGFDXLlypcL6vLw8KRg5e/YsgNIwqWPHjmrbKe4wycrKUirfvHkzRo8ejYcPH2oc65NTLT7++GO89957GDNmDBYuXIgePXqgY8eO8Pf31xjMPA1nZ2dp+kdZDx48kIKJ//73v5DL1a/ZnJqaCkD12IHy34fy6i0tLQEAeXl5UllaWhoKCgqgq6urMcgB/jmf6oIRxXSdJymmkZXtrzJ0dHTw4Ycf4ttvv0WPHj3g5eWF7t27o2PHjvDz86vUHT/lqe4tgUDpnVjfb4zAlfwcdPT1QN+u7Z9qLERERERE/zYMRrSc4gJQcQGqTqNGjTQGIxW1u3btGu7fv6/UV3ntFBemNaVu3bpqyxW/mFdUX3ammSL0SE9PV7qDQZ2yAUhGRgZGjRqFwsJCfPTRR3jrrbfwyiuvwMTEBHK5HIcOHUK3bt1U7soZP348zMzMsGDBAsTFxSE5ORlLliyBjo4OAgMDERYWhqZNm5Z/AqpA07koO53oxIkTFe5HXfjzLN6HoqKiSj3ppSrjUYQ+1ZlhOGfOHNja2mLZsmWIi4tDXFwc5s2bB319fbz55ptYsGAB6tevX+X9Ak+3xgjXFiEiIiIibfA0a4wwGNFyigU0b926pXEbxV0e6uTk5FTYzsTERKkvRX/qLk7L66u2Kca/ZMkSTJw4sdLttmzZgsLCQgwePBiLFi1SqS9vUc5hw4Zh2LBhuHXrFo4ePYpDhw5h8+bN2LlzJ1JTUxEbGystyqouRCjrwYPqrVFR9n27d++e9H7WFsV46tev/1QLmtY0uVyODz74AB988AGuXbuGo0ePYv/+/di6dSvWrl2Lq1evlvto5vLcz8uvkTVG7j/QfMcSEREREZG2YjCi5RRrf1y4cEFt/f3793Ht2jWN7c+fP6+2PDc3Fzdu3FDqw9zcHJaWlsjJycGFCxeUnsCioGkcLwJ3d3fs2rULiYmJVWqnuNtG0/SbkydPVriPhg0bYuDAgRg4cCCmTp0KNzc3JCUl4fjx49LCqYqgSVO4VHYR0qowMzND48aNkZWVhcTERLRvX7tTMZo1a4Y6derg77//xo0bN2Btbf3M+6zqmhy2trZSqPXxxx/Dw8MDBw4cQEZGBhwcHKrcf3XnS3JtESIiIiLSFlxjhKqtV69emDdvHnbt2oXr16/DxsZGqX7VqlUaF14FgB9//BHTp0+XnkKisHz5chQXF8PBwQHOzs5K/a1fvx7Lly9H7969Vfa3bNmypzyiZycoKAizZ8/Gzz//jOnTp6ucK00Ui9NmZ2er1N2+fRvh4eFVGoe1tTUcHBwQHx+P69evS+WKx+D+73//U2lTXFyM1atXV6mfsoKCgrB48WKEhoZi27Zt1d5PTTAyMkLv3r0RERGBhQsXYsGCBc+8T8V7qHh8cVW4u7tLTzi6fv16tYKR6twSyLVFiIiIiIgqR/0qiqQ1unTpAl9fXxQWFmL48OFKU2MOHDiAmTNnQk9PT2P7O3fu4D//+Q/u3bsnlUVGRmL27NkAgJCQEKVfqSdNmgQdHR38+uuvWLBgAUpKSgBAWn8jOTm5pg+xxnh6euKtt97CvXv30K1bN7XrWyQkJGDy5MlKdZ06dQJQGhbFxsZK5VevXkW/fv3UTnG5d+8ehgwZgoMHD6K4uFgqF0Jg69atOHfuHIDSp68oBAQEAAB2794tPUUGKF3bZfz48dLiqNXx2WefwdLSEtu3b8fo0aNVplDl5+djz549ePvtt6vdR1XMmjULRkZGCA0NxdSpU5U+f0Dp+du8eTMmTZpUI/01bNgQJiYmyMnJQVJSkkr9+fPnMWbMGJw8eVJpKlNxcTHCwsKQm5sLAwMDuLu718h4KiMt4xquXM+Bnq4O/Nq0em79EhERERG9bBiMaDmZTIYNGzbA0tISUVFRaNq0Kby9vdGsWTO89tpr8Pf3L3fqxMyZM7Fnzx5YW1vD19cXjo6O6NOnDx4+fIjBgwerPE61VatW0i/8n376KWxsbNCmTRtYWVnhu+++w5w5c57p8T6tlStXon///khOTkbHjh1hY2ODtm3bonXr1jA3N4enpyfmz58vLTgLlD4K+dVXX0Vubi7atGkDV1dXeHp6wsHBAUlJSfj2229V+ikpKcHWrVvRvXt3mJiYoFWrVmjTpg1sbGwwZMgQFBcXIyQkROnpPs7OzpgwYQJKSkoQFBSEpk2bwsfHB40aNcLWrVsxb968ah+3lZUV9u7dC2tra6xZswbW1tZo3rw52rdvD1dXV5iamiIwMFB6/POz1qJFC2zfvh0mJiaYM2cOGjRogJYtW6Jdu3ZwcnJCvXr18Oabb+L48eM10p9MJsPQoUMBAN7e3vD29kaXLl3QpUsXZGdno7CwEKtXr0b79u1hZmaG1q1bS+c+ODgYABAaGgpzc/MaGU9FhBA4EFMawrVt3RwmxkbPpV8iIiIiopcRgxGCi4sLYmNjMXLkSJiZmSEpKQm6urqYM2cOduzYUe66BO3bt8fJkyfRo0cPZGRk4Pr16/Dw8MB3332HzZs3q2370UcfYc+ePejYsSPy8vKQmpoKLy8vHDhwAEFBQc/yUJ+aoaEhdu7ciV9++QWBgYEQQuDMmTNIS0uDjY0Nxo4di3379qFbt25SGx0dHfz+++8IDg6GjY0N0tPTkZOTg6FDhyI2NhYtW7ZU6cfExAQbN27EyJEjYW9vj6tXr+LMmTMQQqBPnz7YvXs35s6dq9JuyZIlmDdvHlxcXHDz5k1cuXIF/fr1Q2xsLDw9PZ/q2H18fHD+/HnMmjUL3t7eyMrKwunTp/HXX3+hbdu2mDVrFg4fPvxUfVRF7969ceHCBSkgysjIQGxsLPLy8uDv74/Q0FBs2bKlxvpbtGgRJk2aBFtbWyQmJiI6OhrR0dEoKCiAs7MzVq9ejSFDhsDa2hrp6elISEiAgYEBBg0ahKNHj+K9996rsbFUpKioGJYN6kG/jh7vFiEiIiIiqoBMVOe5lKRVunTpgujoaBw+fBhdunTRWEZEL5aCR4Uw0K9T28MgIiIiInqh8Y4RIqJ/KYYiREREREQVYzBCRERERERERFqLj+ulF9q+ffukJ9xUxtSpU9U+BpiIiIiIiIhIHQYj9EK7efOm2sfilrc9ERERERERUWVx8VUiIiIiIiIi0lpcY4SIiIiIiIiItBaDESIiIiIiIiLSWgxGiIiIiIiIiEhrMRghIiIiIiIiIq3FYISIiIiIiIiItBaDESIiIiIiIiLSWgxGiIiIiIiIiEhrMRghIiIiIiIiIq3FYISIiIiIiIiItBaDESIiIiIiIiLSWgxGiIiIiIiIiEhrMRghIiIiIiIiIq3FYISIiIiIiIiItBaDESIiIiIiIiLSWgxGiIiIiIiIiEhrMRghIiIiIiIiIq3FYISIiIiIiIiItBaDESIiIiIiIiLSWgxGiIiIiIiIiEhrMRghIiIiIiIiIq3FYISIiIiIiIiItBaDESIiIiIiIiLSWgxGiIiIiIiIiEhrMRghIiIiIiIiIq3FYISIiIiIiIiItBaDESIiIiIiIiLSWgxGiIiIiIiIiEhrMRghIiIiIiIiIq3FYISIiIiIiIiItBaDESIiIiIiIiLSWgxGiIiIiIiIiEhrMRghIiIiIiIiIq3FYISIiIiIiIiItBaDESIiIiIiIiLSWgxGiIiIiIiIiEhr/R+DBDzohaOnDwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1395.35x702.333 with 1 Axes>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subexperiments[0][6].draw(\"mpl\", fold=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "987547e4-296a-41e4-ad82-41f4139a87a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1395.35x702.333 with 1 Axes>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subexperiments[0][2].draw(\"mpl\", fold=-1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8178f3dc-f23f-4e0f-9fd5-f8895bb61dbb",
   "metadata": {},
   "source": [
    "#### Transpile each subexperiment\n",
    "\n",
    "Currently we need to transpile our circuits before submitting them for execution. Therefore, we shall transpile each circuit in the subexperiments first."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "12f869b8-f27d-4764-9064-da40977d1b3a",
   "metadata": {},
   "outputs": [],
   "source": [
    "service = QiskitRuntimeService(channel=\"ibm_quantum\")\n",
    "backend = service.least_busy(\n",
    "    operational=True, simulator=False, min_num_qubits=127\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64bdf404-91ad-4119-9e51-2a34bfe876aa",
   "metadata": {},
   "source": [
    "Now we need to transpile each of the circuits in the subexperiments. For that we first create a pass manager, and then use it to transpile each of the circuits."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dcf80dc6-049a-4238-9665-eef90b48cefa",
   "metadata": {},
   "outputs": [],
   "source": [
    "pm = generate_preset_pass_manager(optimization_level=2, backend=backend)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "3d36b9ac-ba82-428d-9e99-ac8f5c55c9ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "isa_subexperiments = {\n",
    "    label: pm.run(partition_subexpts)\n",
    "    for label, partition_subexpts in subexperiments.items()\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "962c4745-a235-4ef8-b56b-d3026be67fb6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1729.79x618.722 with 1 Axes>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "isa_subexperiments[0][0].draw(\"mpl\", fold=-1, idle_wires=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37491b7a-3cec-44d5-8248-35e5b487f248",
   "metadata": {},
   "source": [
    "### Step 3: Execute using Qiskit primitives"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80063438-8c31-4741-b70d-292839d875d5",
   "metadata": {},
   "source": [
    "Now we shall execute each circuit in subexperiment. `Qiskit-addon-cutting` uses `SamplerV2` to execute the subexperiments."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1c2c1ad6-bfc8-49e3-b2e8-bab1d1f8d1c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "with Batch(backend=backend) as batch:\n",
    "    sampler = SamplerV2(mode=batch)\n",
    "    jobs = {\n",
    "        label: sampler.run(subsystem_subexpts, shots=2**12)\n",
    "        for label, subsystem_subexpts in isa_subexperiments.items()\n",
    "    }"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa4b7aa1",
   "metadata": {},
   "source": [
    "### Step 4: Post-process and return result in desired classical format"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94ade1e8-b650-4d5b-8885-83951fae85c2",
   "metadata": {},
   "source": [
    "Once the circuits have been executed, we now need to retrieve the results and reconstruct the expectation value for the uncut circuit and the original observable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "5de87b89-8e9f-4cf2-98cc-2f4ee76010b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Retrieve results\n",
    "results = {label: job.result() for label, job in jobs.items()}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5a39d14d-deb3-4e1a-8822-28eb577d8309",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9674376845359803"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reconstructed_expval_terms = reconstruct_expectation_values(\n",
    "    results,\n",
    "    coefficients,\n",
    "    subobservables,\n",
    ")\n",
    "reconstructed_expval = np.dot(reconstructed_expval_terms, M_z.coeffs).real\n",
    "reconstructed_expval"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "779217c9",
   "metadata": {},
   "source": [
    "#### Cross verify\n",
    "\n",
    "Let us now execute the circuit without cutting and check the outcome there. Note that for execution of the uncut circuit we can directly use `EstimatorV2` for calculating the expectation values. But we shall use the same `Primitive` throughout. So we shall use `SamplerV2` to get the probability distribution and calculate the expectation value using the `sampled_expectation_value` function.\n",
    "\n",
    "First we need to transpile the uncut `mbl` circuit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "8aa1cfd0-aed6-4a96-a82e-d828e976aa3c",
   "metadata": {},
   "outputs": [],
   "source": [
    "sampler = SamplerV2(mode=backend)\n",
    "\n",
    "if mbl.num_clbits == 0:\n",
    "    mbl.measure_all()\n",
    "isa_mbl = pm.run(mbl)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7f45111-4836-45d4-b8f9-56742b0aa34d",
   "metadata": {},
   "source": [
    "Next we construct the `pub` and run the uncut circuit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1441aa8c-0efd-41d3-8709-f0c2730f2b8e",
   "metadata": {},
   "outputs": [],
   "source": [
    "pub = (isa_mbl, params)\n",
    "uncut_job = sampler.run([pub])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "58439307-a52f-43db-9e57-001ba38beafd",
   "metadata": {},
   "outputs": [],
   "source": [
    "uncut_counts = uncut_job.result()[0].data.meas.get_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f092266c-b557-4966-bc67-c48fece33ad6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9498046875000001"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uncut_expval = sampled_expectation_value(uncut_counts, M_z)\n",
    "uncut_expval"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d92e410c-54ff-4208-be67-3afddf694b6c",
   "metadata": {},
   "source": [
    "We note that the expectation value obtained via wire cutting is closer to the ideal value of $+1$ than the uncut one. Let us now scale up the size of the problem."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "359397f3-5a00-4e55-84b3-34b779680dc5",
   "metadata": {},
   "source": [
    "## Part II. Scale it up!\n",
    "\n",
    "Previously, we showed the results for a 10-qubit MBL circuit. Next, we show that the improvement in expectation value is also obtained for larger circuits. To show that, we repeat the process for a 60-qubit MBL circuit."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a985f7b9",
   "metadata": {},
   "source": [
    "### Step 1: Map classical inputs to a quantum problem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "9f0fe181-1500-4e98-874e-0222e859d420",
   "metadata": {},
   "outputs": [],
   "source": [
    "num_qubits = 60\n",
    "depth = 2\n",
    "mbl = MBLChainCircuit(num_qubits, depth)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8649f4fe-3d51-4dc0-9fc9-814afa36aa76",
   "metadata": {},
   "source": [
    "We create a random set of values for $\\vec{\\phi}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "10bdb44d-c1b3-4c97-9d36-5c8055cbbd38",
   "metadata": {},
   "outputs": [],
   "source": [
    "phis = list(np.random.rand(mbl.num_parameters - 1))\n",
    "theta = [0]\n",
    "params = theta + phis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "879126bf-184b-4f8f-8e95-4c1c6d747706",
   "metadata": {},
   "source": [
    "Next we construct the cut circuit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "060efb46-24e3-41d8-aabe-7ff9eb49f7c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "mbl_cut = MBLChainCircuit(num_qubits, depth, use_cut=True)\n",
    "mbl_cut.assign_parameters(params, inplace=True)\n",
    "mbl_cut.draw(\"mpl\", fold=-1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf55debd",
   "metadata": {},
   "source": [
    "### Step 2: Optimize problem for quantum hardware execution"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba70a8ab-bbcc-4bed-a5be-48a1e534902d",
   "metadata": {},
   "source": [
    "As shown for the small scale example, we partition the circuit and the observable for the cutting experiments."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "41691a7d-d2c9-40e5-a4fb-4c0cba64f355",
   "metadata": {},
   "outputs": [],
   "source": [
    "mbl_move = cut_wires(mbl_cut)\n",
    "\n",
    "# Define observable\n",
    "observable = PauliList(\n",
    "    [\"I\" * i + \"Z\" + \"I\" * (num_qubits - i - 1) for i in range(num_qubits)]\n",
    ")\n",
    "new_obs = expand_observables(observable, mbl, mbl_move)\n",
    "\n",
    "# Partition the circuit into subcircuits\n",
    "partitioned_problem = partition_problem(circuit=mbl_move, observables=new_obs)\n",
    "\n",
    "# Get subcircuits\n",
    "subcircuits = partitioned_problem.subcircuits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "156baa5b-9aa6-4081-b295-541ca7ce8fee",
   "metadata": {},
   "outputs": [],
   "source": [
    "subobservables = partitioned_problem.subobservables"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71529591-2385-4dba-ac7f-bc3e56ca74dc",
   "metadata": {},
   "source": [
    "We also create a `SparsePauliOp` object for the observable with proper co-efficients."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6199cca9-887e-41cd-a89b-f5bda8026a69",
   "metadata": {},
   "outputs": [],
   "source": [
    "M_z = SparsePauliOp(\n",
    "    [\"I\" * i + \"Z\" + \"I\" * (num_qubits - i - 1) for i in range(num_qubits)],\n",
    "    coeffs=[1 / num_qubits] * num_qubits,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83e1ab21-d755-4e24-9150-32c65db9671f",
   "metadata": {},
   "source": [
    "Next we generate the subexperiments and transpile each circuit in the subexperiment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "34d13e5e-f5e2-4b9e-be65-7da075f3fb37",
   "metadata": {},
   "outputs": [],
   "source": [
    "subexperiments, coefficients = generate_cutting_experiments(\n",
    "    circuits=subcircuits,\n",
    "    observables=subobservables,\n",
    "    num_samples=np.inf,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "91d7da19-ed57-4927-a28b-71415edf9131",
   "metadata": {},
   "outputs": [],
   "source": [
    "isa_subexperiments = {\n",
    "    label: pm.run(partition_subexpts)\n",
    "    for label, partition_subexpts in subexperiments.items()\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a27bba81",
   "metadata": {},
   "source": [
    "### Step 3: Execute using Qiskit primitives"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4bcdee21-7efa-49e0-94a5-81131e2a32ed",
   "metadata": {},
   "source": [
    "We use the `Batch` mode to execute all the circuits in the subexperiments."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "73311dac-faef-48b4-b833-2bb8dbb35401",
   "metadata": {},
   "outputs": [],
   "source": [
    "with Batch(backend=backend) as batch:\n",
    "    sampler = SamplerV2(mode=batch)\n",
    "    jobs = {\n",
    "        label: sampler.run(subsystem_subexpts, shots=2**12)\n",
    "        for label, subsystem_subexpts in isa_subexperiments.items()\n",
    "    }"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "daac83ec",
   "metadata": {},
   "source": [
    "### Step 4: Post-process and return result in desired classical format"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e19e4a53-5951-4032-bc38-fe0e8219de53",
   "metadata": {},
   "source": [
    "Let us now retrieve the results for each circuit in the subexperiment and reconstruct the expectation value corresponding to the uncut circuit and the original observable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "1ab6f47f-3f4e-462c-b516-fac0e6bf5259",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Retrieve results\n",
    "results = {label: job.result() for label, job in jobs.items()}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "5a501461-6097-4391-851a-266149fd8d99",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9631355921427409"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reconstructed_expval_terms = reconstruct_expectation_values(\n",
    "    results,\n",
    "    coefficients,\n",
    "    subobservables,\n",
    ")\n",
    "reconstructed_expval = np.dot(reconstructed_expval_terms, M_z.coeffs).real\n",
    "reconstructed_expval"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c3944af-557c-4e59-807f-99f94315962a",
   "metadata": {},
   "source": [
    "#### Cross verify\n",
    "\n",
    "As in the small scale example, we shall once more obtain the expectation value by executing the uncut circuit, and compare the result with circuit cutting. We shall use the `SamplerV2` to maintain uniformity in the use of Primitives."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "8c001186-79ad-47ae-8b35-54d91fd9ef05",
   "metadata": {},
   "outputs": [],
   "source": [
    "sampler = SamplerV2(mode=backend)\n",
    "\n",
    "if mbl.num_clbits == 0:\n",
    "    mbl.measure_all()\n",
    "isa_mbl = pm.run(mbl)\n",
    "\n",
    "pub = (isa_mbl, params)\n",
    "uncut_job = sampler.run([pub])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "f061f692-2828-41cd-826c-3515ac9a4335",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9426757812499998"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uncut_counts = uncut_job.result()[0].data.meas.get_counts()\n",
    "uncut_expval = sampled_expectation_value(uncut_counts, M_z)\n",
    "uncut_expval"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64ae72e7-7b4e-4a69-bab4-1ef37b0e4e60",
   "metadata": {},
   "source": [
    "#### Visualize\n",
    "\n",
    "Let us visualize the improvement obtained in the expectation value by using wire cutting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "19dad6bb-544e-45b9-90c4-3555681e4e5b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plt.gca()\n",
    "methods = [\"cut\", \"uncut\"]\n",
    "values = [reconstructed_expval, uncut_expval]\n",
    "\n",
    "plt.bar(methods, values, color=\"#a56eff\", width=0.4, edgecolor=\"#8a3ffc\")\n",
    "plt.axhline(y=1, color=\"k\", linestyle=\"--\")\n",
    "ax.set_ylim([0.85, 1.02])\n",
    "plt.text(0.3, 0.99, \"Exact result\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "545962c9-17c6-4ab9-8cdd-6e7a486df2c6",
   "metadata": {},
   "source": [
    "#### Inference\n",
    "\n",
    "We observe that both in the small and large scale problems wire cutting leads to a better result than the uncut one. Note that no error mitigation techniques have been used for these experiments. Therefore, the improvement in result that has been obtained is only due to wire cutting. It may be possible to further improve the results using different mitigation methods together with circuit cutting.\n",
    "\n",
    "Moreover, in this notebook, we computed both the subcircuits on the same hardware. In [\\[5\\], \\[6\\]](#references), the authors shows a method to distribute the subcircuits on different hardware using noise information in order to maximize the noise suppression, and parallelize the process."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "487af7a3",
   "metadata": {},
   "source": [
    "## Appendix: resource scaling consideration"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "53bccce5-2348-495b-9b42-0002e0052611",
   "metadata": {},
   "source": [
    "The number of circuits to be executed increases with the number of cuts. Therefore, while many cuts can produce small subcircuits, thus further improving the performance, it also leads to a significantly high number of circuit executions, which may not be practical for most cases. Below, we show an example of the number of subcircuits corresponding to the number of cuts for a 50-qubit circuit.\n",
    "\n",
    "![wc-5.png](/images/tutorials/wire-cutting-to-improve-performance/5c6ea4da-bbd8-47f9-ac48-e438cc59a11d.avif)\n",
    "\n",
    "We note that even for 5 cuts the number of subexperiments is around 200k. Therefore, circuit cutting should be used only when the number of cuts is small."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "a9b1019b-826b-4224-91f3-faf1c7a2fbd6",
   "metadata": {},
   "source": [
    "### One example of cut-friendly and cut-unfriendly circuits each\n",
    "\n",
    "#### Cut-friendly circuit\n",
    "\n",
    "As noted earlier, a circuit is cut-friendly when the circuit can be partitioned into smaller disjoint subcircuits with a small number of cuts. Any hardware-efficient circuit, i.e., a circuit which requires little to no SWAP gates when mapped to the hardware coupling map, is, in general, cut-friendly. Below, we show an example of an excitation preserving ansatz, which is used in Quantum Chemistry. Note that such a circuit can be partitioned into two subcircuits with a single cut irrespective of the number of qubits.\n",
    "\n",
    "![wc-6.png](/images/tutorials/wire-cutting-to-improve-performance/c122a418-b914-41e7-a1aa-00eb1eec5b87.avif)\n",
    "\n",
    "#### Cut-unfriendly circuit\n",
    "\n",
    "A circuit is cut-unfriendly if, in general, the number of cuts required to form disjoint partitions grow significantly with the depth of the number of qubits. Recall that with each cut an extra qubit is required. So with the number of cuts, the effective number of qubits also increase. Below we show an example of a 3-qubit Grover circuit with a possible cutting instance.\n",
    "\n",
    "![wc-7.png](/images/tutorials/wire-cutting-to-improve-performance/b31dc57e-e6d7-49fd-9f46-304a328b3764.avif)\n",
    "\n",
    "We note that three cuts are required, and the cut is more vertical than horizontal. This means, that the number of cuts is expected to scale linearly with the number of qubits, which is not amenable for cutting."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66bffbc1-8ab0-4890-b306-439699f511dd",
   "metadata": {},
   "source": [
    "## References\n",
    "\n",
    "\n",
    "[1] Peng, T., Harrow, A. W., Ozols, M., & Wu, X. (2020). Simulating large quantum circuits on a small quantum computer. Physical review letters, 125(15), 150504.\n",
    "\n",
    "[2] Tang, W., Tomesh, T., Suchara, M., Larson, J., & Martonosi, M. (2021, April). Cutqc: using small quantum computers for large quantum circuit evaluations. In Proceedings of the 26th ACM International conference on architectural support for programming languages and operating systems (pp. 473-486).\n",
    "\n",
    "[3]  Perlin, M. A., Saleem, Z. H., Suchara, M., & Osborn, J. C. (2021). Quantum circuit cutting with maximum-likelihood tomography. npj Quantum Information, 7(1), 64.\n",
    "\n",
    "[4]  Majumdar, R., & Wood, C. J. (2022). Error mitigated quantum circuit cutting. arXiv preprint arXiv:2211.13431.\n",
    "\n",
    "[5]  Khare, T., Majumdar, R., Sangle, R., Ray, A., Seshadri, P. V., & Simmhan, Y. (2023). Parallelizing Quantum-Classical Workloads: Profiling the Impact of Splitting Techniques. In 2023 IEEE International Conference on Quantum Computing and Engineering (QCE) (Vol. 1, pp. 990-1000). IEEE.\n",
    "\n",
    "[6]  Bhoumik, D., Majumdar, R., Saha, A., & Sur-Kolay, S. (2023). Distributed Scheduling of Quantum Circuits with Noise and Time Optimization. arXiv preprint arXiv:2309.06005."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e0e5ad4",
   "metadata": {},
   "source": [
    "## Tutorial survey\n",
    "\n",
    "Please take one minute to provide feedback on this tutorial. Your insights will help us improve our content offerings and user experience.\n",
    "\n",
    "[Link to survey](https://your.feedback.ibm.com/jfe/form/SV_3BLFkNVEuh0QBWm)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c38b07a",
   "metadata": {},
   "source": [
    "© IBM Corp. 2024, 2025"
   ]
  }
 ],
 "metadata": {
  "description": "Use wire cutting to partition circuits into many smaller subcircuits.",
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   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "platform": "cloud",
  "title": "Improving estimation of expectation values using wire cutting"
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