Task 2.ipynb

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    "# Task 2\n",
    "\n",
    "## Problem\n",
    "Implement a circuit that returns |01> and |10> with equal probability.\n",
    "\n",
    "Requirements :\n",
    "- The circuit should consist only of CNOTs, RXs and RYs. \n",
    "- Start from all parameters in parametric gates being equal to 0 or randomly chosen. \n",
    "- You should find the right set of parameters using gradient descent (you can use more advanced optimization methods if you like). \n",
    "- Simulations must be done with sampling - i.e. a limited number of measurements per iteration and noise. \n",
    "\n",
    "Compare the results for different numbers of measurements: 1, 10, 100, 1000. \n",
    "\n",
    "Bonus question:\n",
    "How to make sure you produce state |01> + |10> and not |01> - |10> ?\n"
   ]
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   "source": [
    "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
    "<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#Problem\" data-toc-modified-id=\"Problem-1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>Problem</a></span></li><li><span><a href=\"#Basic-task\" data-toc-modified-id=\"Basic-task-2\"><span class=\"toc-item-num\">2&nbsp;&nbsp;</span>Basic task</a></span><ul class=\"toc-item\"><li><span><a href=\"#Wild-Guess!\" data-toc-modified-id=\"Wild-Guess!-2.1\"><span class=\"toc-item-num\">2.1&nbsp;&nbsp;</span>Wild Guess!</a></span></li><li><span><a href=\"#Defining-the-variational-circuit\" data-toc-modified-id=\"Defining-the-variational-circuit-2.2\"><span class=\"toc-item-num\">2.2&nbsp;&nbsp;</span>Defining the variational circuit</a></span></li><li><span><a href=\"#Defining-the-cost-function\" data-toc-modified-id=\"Defining-the-cost-function-2.3\"><span class=\"toc-item-num\">2.3&nbsp;&nbsp;</span>Defining the cost function</a></span></li><li><span><a href=\"#Putting-it-all-together\" data-toc-modified-id=\"Putting-it-all-together-2.4\"><span class=\"toc-item-num\">2.4&nbsp;&nbsp;</span>Putting it all together</a></span></li><li><span><a href=\"#Analysis\" data-toc-modified-id=\"Analysis-2.5\"><span class=\"toc-item-num\">2.5&nbsp;&nbsp;</span>Analysis</a></span></li></ul></li><li><span><a href=\"#Comparing-different-optimizers\" data-toc-modified-id=\"Comparing-different-optimizers-3\"><span class=\"toc-item-num\">3&nbsp;&nbsp;</span>Comparing different optimizers</a></span></li><li><span><a href=\"#Visualizing-cost-function\" data-toc-modified-id=\"Visualizing-cost-function-4\"><span class=\"toc-item-num\">4&nbsp;&nbsp;</span>Visualizing cost function</a></span></li><li><span><a href=\"#Bonus-Question\" data-toc-modified-id=\"Bonus-Question-5\"><span class=\"toc-item-num\">5&nbsp;&nbsp;</span>Bonus Question</a></span><ul class=\"toc-item\"><li><span><a href=\"#Redifining-the-circuit\" data-toc-modified-id=\"Redifining-the-circuit-5.1\"><span class=\"toc-item-num\">5.1&nbsp;&nbsp;</span>Redifining the circuit</a></span></li><li><span><a href=\"#Redifining-the-cost-function\" data-toc-modified-id=\"Redifining-the-cost-function-5.2\"><span class=\"toc-item-num\">5.2&nbsp;&nbsp;</span>Redifining the cost function</a></span></li><li><span><a href=\"#Putting-it-all-together\" data-toc-modified-id=\"Putting-it-all-together-5.3\"><span class=\"toc-item-num\">5.3&nbsp;&nbsp;</span>Putting it all together</a></span></li><li><span><a href=\"#Final-measurements\" data-toc-modified-id=\"Final-measurements-5.4\"><span class=\"toc-item-num\">5.4&nbsp;&nbsp;</span>Final measurements</a></span></li></ul></li><li><span><a href=\"#Conclusion\" data-toc-modified-id=\"Conclusion-6\"><span class=\"toc-item-num\">6&nbsp;&nbsp;</span>Conclusion</a></span></li></ul></div>"
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   "source": [
    "#install/upgrade the dependencies\n",
    "!pip install --upgrade qiskit==0.20.1\n",
    "!pip install --upgrade PennyLane==0.11.0\n",
    "!pip install --upgrade pandas==1.1.2\n",
    "!pip install --upgrade numpy==1.19.2\n",
    "!pip install --upgrade matplotlib==3.3.1\n",
    "\n",
    "#clear output \n",
    "from IPython.display import clear_output\n",
    "clear_output()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import the required packages\n",
    "from qiskit import QuantumCircuit, IBMQ\n",
    "from qiskit.circuit import Parameter\n",
    "from qiskit.providers.aer.noise import NoiseModel\n",
    "from qiskit.providers.aer.noise.device import basic_device_noise_model\n",
    "from qiskit import Aer\n",
    "from qiskit.visualization import plot_histogram\n",
    "import pennylane as qml\n",
    "from pennylane import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "pi = np.pi"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To do the simulation with noise, the noise model needs to be loaded from IBMQ account.cNote that if account is not loaded in earlier execution, IBMQ.save_account() needs to be called with ones IBM id in it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
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     "name": "stderr",
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     "text": [
      "/home/nikhil/.local/lib/python3.8/site-packages/qiskit/providers/ibmq/ibmqfactory.py:192: UserWarning: Timestamps in IBMQ backend properties, jobs, and job results are all now in local time instead of UTC.\n",
      "  warnings.warn('Timestamps in IBMQ backend properties, jobs, and job results '\n",
      "/home/nikhil/.local/lib/python3.8/site-packages/qiskit/providers/aer/noise/device/basic_device_model.py:111: DeprecationWarning: This function is been deprecated and moved to a method of the`NoiseModel` class. For equivalent functionality use `NoiseModel.from_backend(properties, **kwargs).\n",
      "  warnings.warn(\n",
      "WARNING:qiskit.providers.aer.noise.device.models:Device reported a gate error parameter greater than maximum allowed value (1.000000 > 0.800000). Truncating to maximum value.\n",
      "WARNING:qiskit.providers.aer.noise.device.models:Device model returned a depolarizing error parameter greater than maximum allowed value (1.069163 > 1.066667). Truncating to maximum value.\n",
      "WARNING:qiskit.providers.aer.noise.device.models:Device reported a gate error parameter greater than maximum allowed value (1.000000 > 0.800000). Truncating to maximum value.\n",
      "WARNING:qiskit.providers.aer.noise.device.models:Device model returned a depolarizing error parameter greater than maximum allowed value (1.069084 > 1.066667). Truncating to maximum value.\n"
     ]
    }
   ],
   "source": [
    "#Load the IBM account\n",
    "provider = IBMQ.load_account()\n",
    "ibmq_16_melbourne = provider.get_backend('ibmq_16_melbourne')\n",
    "device_properties = ibmq_16_melbourne.properties()\n",
    "\n",
    "noise_model = basic_device_noise_model(device_properties)"
   ]
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Basic task\n",
    "\n",
    "Let's first make a basic variational circuit template with two qubits and just enough gates to produce $\\vert01\\rangle$ and $\\vert10\\rangle$ with half probability each. Here the proposal is that only two Ry gates on each qubit and a CNOT gate is enoigh to get to the target state. The circuit of which should look like the one shown below."
   ]
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   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
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       "<pre style=\"word-wrap: normal;white-space: pre;background: #fff0;line-height: 1.1;font-family: &quot;Courier New&quot;,Courier,monospace\">     ┌───────────┐     \n",
       "q_0: ┤ RY(theta) ├──■──\n",
       "     └┬─────────┬┘┌─┴─┐\n",
       "q_1: ─┤ RY(phi) ├─┤ X ├\n",
       "      └─────────┘ └───┘</pre>"
      ],
      "text/plain": [
       "     ┌───────────┐     \n",
       "q_0: ┤ RY(theta) ├──■──\n",
       "     └┬─────────┬┘┌─┴─┐\n",
       "q_1: ─┤ RY(phi) ├─┤ X ├\n",
       "      └─────────┘ └───┘"
      ]
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     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param0 = Parameter('theta')\n",
    "param1 = Parameter('phi')\n",
    "\n",
    "qc = QuantumCircuit(2)\n",
    "qc.ry(param0, 0)\n",
    "qc.ry(param1, 1)\n",
    "qc.cx(0,1)\n",
    "qc.draw()"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Wild Guess!\n",
    "\n",
    "Now its shown below that the above proposal is not just a wild guess by working the maths happening behind the circuit and thus proving that the above circuit should be able to solve the problem.\n",
    "\n",
    "Lets see how the RY gate operation looks like on the first qubit $\\vert0\\rangle$ (initial state).\n",
    "\n",
    "$$ RY_{\\theta}\\vert0\\rangle = \\begin{bmatrix} cos(\\theta/2) & -sin(\\theta/2) \\\\ sin(\\theta/2) & cos(\\theta/2)\\end{bmatrix}\\begin{bmatrix} 1 \\\\ 0 \\end{bmatrix} = \\begin{bmatrix} cos(\\theta/2) \\\\ sin(\\theta/2) \\end{bmatrix}$$\n",
    "\n",
    "Similarly for the second state it is,\n",
    "\n",
    "$$ RY_{\\phi}\\vert0\\rangle = \\begin{bmatrix} cos(\\phi/2) & -sin(\\phi/2) \\\\ sin(\\phi/2) & cos(\\phi/2)\\end{bmatrix}\\begin{bmatrix} 1 \\\\ 0 \\end{bmatrix} = \\begin{bmatrix} cos(\\phi/2) \\\\ sin(\\phi/2) \\end{bmatrix}$$\n",
    "\n",
    "To write the state of a composite system, tensor product of the individual states are taken which can be written as,\n",
    "\n",
    "$$ RY_{\\theta}\\vert0\\rangle \\otimes RY_{\\phi}\\vert0\\rangle = \\begin{bmatrix} cos(\\theta/2) \\\\ sin(\\theta/2) \\end{bmatrix} \\otimes \\begin{bmatrix} cos(\\phi/2) \\\\ sin(\\phi/2) \\end{bmatrix} = \\begin{bmatrix}  cos(\\theta/2)cos(\\phi/2) \\\\ cos(\\theta/2)sin(\\phi/2) \\\\sin(\\theta/2)cos(\\phi/2) \\\\ sin(\\theta/2)sin(\\phi/2) \\end{bmatrix}$$\n",
    "\n",
    "Now if CNOT gate is applied with the first qubit as control and second as target, the final state is,\n",
    "\n",
    "$$ CNOT(RY_{\\theta}\\vert0\\rangle \\otimes RY_{\\phi}\\vert0\\rangle) = \\begin{bmatrix} 1 & 0 & 0 & 0 \\\\ 0 & 1 & 0 & 0 \\\\0 & 0 & 0 & 1 \\\\0 & 0 & 1 & 0 \\end{bmatrix}\\begin{bmatrix}  cos(\\theta/2)cos(\\phi/2) \\\\ cos(\\theta/2)sin(\\phi/2) \\\\sin(\\theta/2)cos(\\phi/2) \\\\ sin(\\theta/2)sin(\\phi/2) \\end{bmatrix} = \\begin{bmatrix}  cos(\\theta/2)cos(\\phi/2) \\\\ cos(\\theta/2)sin(\\phi/2) \\\\ sin(\\theta/2)sin(\\phi/2) \\\\sin(\\theta/2)cos(\\phi/2) \\end{bmatrix}$$ \n",
    "\n",
    "By putting the value $\\theta = \\pi/2$ and $\\phi = \\pi$, the final state simplifies to,\n",
    "\n",
    "$$\\begin{bmatrix}  0 \\\\ \\frac{1}{\\sqrt2} \\\\ \\frac{1}{\\sqrt2} \\\\ 0 \\end{bmatrix} = \\frac{1}{\\sqrt2}\\begin{bmatrix}  0 \\\\ 1 \\\\ 0 \\\\ 0 \\end{bmatrix} + \\frac{1}{\\sqrt2} \\begin{bmatrix}  0 \\\\ 0 \\\\ 1 \\\\ 0 \\end{bmatrix} = \\frac{1}{\\sqrt2}\\vert01\\rangle + \\frac{1}{\\sqrt2}\\vert10\\rangle $$\n",
    "\n",
    "To get the probability of measuring specific state, the coefficients are squared. By this calculation, its proved that only two RY and one CNOT gate is enough for the given problem. It's also seen that if the optimization works perfectly the value of $\\theta$ and $\\phi$ should converge to $\\pi/2$ and $\\pi$ (or something similar) respectively.\n",
    "\n"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Defining the variational circuit\n",
    "The above circuit is imported from qiskit to pennylane and measurements are added to the circuit. The noise model from ibmq_16 melbourne is used for the same."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#device with noise model from ibmq_16_melbourne\n",
    "dev1 = qml.device(\"qiskit.aer\", wires=2, noise_model = noise_model, shots=1024, analytic=False)\n",
    "\n",
    "param0 = Parameter('theta')\n",
    "param1 = Parameter('phi')\n",
    "\n",
    "qc = QuantumCircuit(2)\n",
    "qc.ry(param0, 0)\n",
    "qc.ry(param1, 1)\n",
    "qc.cx(0,1)\n",
    "qc = qml.from_qiskit(qc)\n",
    "\n",
    "@qml.qnode(dev1)\n",
    "def circuit(x):\n",
    "    qc({param0 : x[0], param1: x[1]})\n",
    "    return qml.probs(wires = [0,1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Defining the cost function\n",
    "\n",
    "The outcome of our measurement is going to be the probability of the possible two qubits states, namely $\\vert00\\rangle$, $\\vert01\\rangle$, $\\vert01\\rangle$, $\\vert01\\rangle$. Among these the probability of $\\vert01\\rangle$ and $\\vert01\\rangle$ are required to be half each. So the cost function is defined as,\n",
    "\n",
    "$$J = (P_{00})^2 + (0.5 - P_{01})^2 + (0.5 - P_{10})^2 + (P_{11})^2$$\n",
    "\n",
    "Here when P$_{01}^2$ and P$_{00}^2$ is 0.5 each, the cost function is minimum, zero. And so the problem reduces to finding a variational parameters for which cost of the circuit is lowest."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cost_fun(params):\n",
    "    prob = circuit(params)\n",
    "    p00, p01, p10, p11 = prob\n",
    "    cost = (p00)**2 + (0.5 - p01)**2 + (0.5 - p10)**2 + (p11)**2\n",
    "    return cost"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Putting it all together\n",
    "\n",
    "Once variational circuit and cost function is defined, the final thing to do is optimization of the variational parameters. Here the gradient descent optimizer from pennylane is used for the same. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def circuit_opt(shot, iteration, in_param, opt):\n",
    "    #randomly initialized initial parameters\n",
    "    print(shot, 'measurements per iteration:')\n",
    "    params = in_param\n",
    "    dev1.shots = shot\n",
    "    optimizer = opt\n",
    "    costs = []\n",
    "    for i in range(iteration):\n",
    "        cost = cost_fun(params)\n",
    "        params = optimizer.step(cost_fun, params)\n",
    "        costs.append(cost)\n",
    "    print(\"The optimal parameter is \", params)\n",
    "    print(\"The final cost is \", round(costs[-1],4))\n",
    "    print()\n",
    "    return params, costs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above algorithm needs to be called in loop with varying number of measurements per iteration. The parameters to be passed includes:\n",
    "- number of measurements\n",
    "- number of optimization iteration\n",
    "- initial parameters for gate\n",
    "- optimizer to be used"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 measurements per iteration:\n",
      "The optimal parameter is  [-1.50357682  2.52926212]\n",
      "The final cost is  0.5\n",
      "\n",
      "10 measurements per iteration:\n",
      "The optimal parameter is  [-1.59757682  2.86526212]\n",
      "The final cost is  0.02\n",
      "\n",
      "100 measurements per iteration:\n",
      "The optimal parameter is  [-1.58845682  2.89452212]\n",
      "The final cost is  0.0248\n",
      "\n",
      "1000 measurements per iteration:\n",
      "The optimal parameter is  [-1.55563402  2.88571092]\n",
      "The final cost is  0.0102\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#Lets call the above function with a list of measurements to be taken per iteration and iterations per optimization\n",
    "shots = [1,10,100,1000]\n",
    "iteration = 75\n",
    "in_param = np.random.randn(2)\n",
    "#Using pennylane gradient descent optimizer\n",
    "opt = qml.GradientDescentOptimizer(stepsize=0.2)\n",
    "#Store the lists of cost convergence for each number of shots\n",
    "cost_shot = []\n",
    "#Store the final parameters value after the optimization\n",
    "final_params = []\n",
    "\n",
    "for shot in shots:\n",
    "    final_param, costs = circuit_opt(shot, iteration, in_param, opt)\n",
    "    final_params.append(final_param)\n",
    "    cost_shot.append(costs)    \n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analysis\n",
    "The circuit has been executed and variational parameters should have converged to the value which would give lowest cost and thus equal probability of getting $\\vert01\\rangle$ and $\\vert10\\rangle$.\n",
    "\n",
    "Let's first see how the cost function has varied with number of iterations for different numbers of measurements."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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Wu6LH96RF4DLsT0rZfWdxdHGebJIcuwt/MBK/jxYUanx2bXwftOUrBxa7CmaA0z+7dMulJh6juy/5oIZ0MkFh+An6uSKwbhE4IgE6HarQnOyn1JCZgzBTHoF2zrRRQxbr9OhnU2Yy5RqajAOLFCWSHFxglgtRlKWPwOt2ZlWorrtIsKyyiBrSZtep50t8NoUwwOllBuvWWT7fr84uyJRv2IoA84W49XBGgnQ4SoHELOO+hIzUkOGM3iI15DKmPNL1GTYIWdVmrr1FDUGqr8T8GtIvuZl4DuVryXbB++7AH4pXms2aGkpj3RXSANeViYM/GMnr+9Ubk5lM6N+KwG2dGnJFAvidShEQ7qOKIGhO06j9Zj4CC9SQCedvSg2ZzKb19ITR/nwg2ZRPvg8Zo62sWgQuh6nCzAf8IVUDyaoVokUZlXjTK/WefDaZkPLsLD6LfF5DpvepN9C/FUEW1JBTBghoiiBkrXT12QbTcg9pFKZRRq0RMlJDFumoXqGGUvpMfF+yvQbDPqK+lqySHLsJ7dlZXZdDU/pFZo7/ZMqjAAa4VJmsWWdlPeQs1vrrbdiKAGsP2y0DBJ3FhHBAuI8rgmypIUs+AnPO3+kQuJxms+mkQVcXemcmU64RG0xMQi3jCtQ4Ec8qL+119zw15HVbd1D7g3oZ01BDPfhsMkEfPqr/bgYtu77U4zTNfem2TBnep95AP1cEGodrgRqSQaTTSxA3oq9SQ1lSHPGSFN2LGjKyKOIL3if7CLTZVM+Z1XHu2ziSxOy+pcuoTu1DRw31pCJwZUcNpZMxPtMtQGrIokxaSYqY4z6feQQm71NvoH8rgixmbG4ZRDo9hEQfVgRBE4vAbawwNaeptTwC4x+VmbM5IzXUowlliX0mBxeYRVs5HAKP09oMP9tBORfI1kGtl1FbcCd5PxS4RZCBGtIr9XyF8qbK1PsKs38rgiyoIQ9KEQSFG0dfp4YMyj2o/ckz4SwUgdks0iQPISM11IP8aia/hJkCVduszrajg7KJwsw1tEqhamC35qDWy6i+Z4ro6v0BLkWmDL91/TudL6VsRw0VGKw680LhiFIEriJCwo0j0lcVQXbUUDwHoJvUkIGPwdQi6AV+1R8MIwSUejNQQ0bXkaacdmIfUb6+h6ihgE7pW3VQ62WEwrDWMiFbmfR+snz5a2LKqYAsp7wpAiHEn4UQdUKI7Sb7Py6E2CqE2CaEeFcIcUG+ZDFDnIdO/yAC4Qhegginh7Dw9GFFkCEePuWHb6w4jGAaNWRGDZn5CKLnKOtRH4GS0SxBLJ1lZH223bPUUAIF0gVqSH8O/X5Al5vQ+wNcyvuSQSZ9dn2+QnkLMcw2nxbBX4Cr0+w/BCyWUp4P/AhYkkdZDGGVGvIHlUWAq4iww40jEuwJ8XocsYXokwY0t1MgDCpvZkUNuc1jz7tCDZV4nLid+YnqMJPRLLjATIGqbVlSQ9FBOd8L7iTOfC1GDelkBKP7EE6rMHsD8RpO1iYOCdRQnkJ5UxRmAVBoeVMEUsq3gKY0+9+VUp6Kfl0NVORLFjPEojoyKYJAAKeQCLeXkPDi7LMWQRi3U+BMWoheCGE4a0zHjSdDGxBTHYwmUUMZ6IdsBrDuIpUbT84jMLeMPFnNtpWD0mpNnO5An/tgde1uvYza9+RzJuwvgAEuLpPF33pPUEMxhWm9KGG+4eptAaL4LPCy2U4hxJ3AnQDl5eVUVVXlrGO3A/YfPExV1XHTNk0tbXwIqG86jScMjnBnTmUoFBw45MeJNLw2JxEOHD5KVVVtvH2zeoH37tpOUcPutOeuORYgImHZ8ipcOkVT19CB20lKn7Vt6ge4edsOShr3xLZvP6KssfVrViFkiENHjlFVVZfVdWaLI9V+ZCjMpvVrAdi6fSeDmvfF9m87qmTasG41B7yJyiDQ0cGJuvaM70u7P0jtieP4m9S9WVa1gmKXSHtMd1DTqu7vgb27Od0UovlMJKOMTc3tuIMO9u0+A8DK1WuoHhifBBw+6kfIMBvWrAZg267djGw7kJ8LsIiDR/04ZJh1q1cBsGPXHqo6Dpq239Ok3uldO7bR3hLAH059N7st02E/TiKsW/2ukmn3Pqr8h3PaR7bodUUghLgCpQgWmrWRUi4hSh3NmzdP+ny+nPVfXPUqI0ePxeebadpm/5GjsAFGjR2Hq3MnntZWLsqhDIWCN5q3UdpwEqP7W7ryDUaMGonPNyu2rehgI6xezby5s7l08vC0597nOAj7dnHJZZfHTGKAn297hxFlXny+ixLanzjdAW+/yaQp0/DNHxfbvmfFAdi1myt9l1O2YQXDRg7H58uve+mpmo0MCrWweOElsOINJkyZhu+S8bH9B945BDt3csWiyxkUdQBq+P2eVQjA51tgen4pJaFXlzJ10nhGlnlhzw4uuuRShg/w5uuS2F5zGt55hzkXnE/djpNUdzYaPnc9XOuWUzF6MBfOHgub1nH+7LnMHTcktv/5us2UtTVxxeKFsPw1xk+cjO/ySXm7BitY2rCFAacbuMq3CN58hcqJk/Atnmza3rG3Htau5eJ5c9jQcoDalk58vstzKtOrTdsobarlKt9ieONlKidMxOebktM+skWvRg0JIWYBfwJulFI29oYMVmKFg50dADhcXiIOD276KDUUNC8gZxTWmI4bNzpe9ZHK+XeFGvI4e65kcwo1ZHANYOYszkwvaKW1tWQtdc78Xleqszi7hDIw9pX05DVYgSaTx8SvYdQeiJf7yFNCmdflMPW99QZ6TREIIcYB/wQ+KaXc21tyWPkRBANRReApRjq9uGQfdRaHzJedNOLj03HjqcebR9yYDaBqf+qg64qWpOi5CJtIbMDUvifsN6k1pLZlVlbJg486Z36vK9FHkGVkk6mvRO0vpAFOk8npEJaCCxIVZP4Syrwuh6nvrTeQN2pICPE44AOGCyGqgXsBN4CU8o/AD4BhwO+FEAAhKeW8fMljBksztugaxU53ESGnB48MIqUkKnefgdnsHIwVpnbfigzi51OPT+dgTBM1ZFBiQtvXUz8iTUaz4AJ/KIIn+sNOhpXIk8SQxZ6JuNHXR7Ke6xBOrxCjOSGFNMDp81QsKWVdAETeEsp073xPlhRJh7wpAinlbRn2fw74XL76twors6FQ1CJwur1KEYgQ/lAk5vXvKzCbnYPxoJsVNZRmhm+UiOVyOnA6hLEF4db9iHqo1tCQUo9ugLNGbykZM9MLidmsvUMNaQvuOBzmkxt179MnlMWVdGEMcIkyWVDK+kS7fEYNua3L1BPo15nFYG3GFvYri8DlLUK6ivAQLIiXPNdIt+yk0aCbVUJZmgSx7KyQ+KDbUyWbUwYTw2uwTqkZnV9rG7eE8kwNGSgfowXpNYTCEUIRmVZG/X0olAEuRaaMSjmp1lCeEsoS3uECCLO1FYEFrR+OrlHs8hQjXB68hAriJc810i07aTToZpdHkG4WaTaIGlshPU4N6QcTAwd1Wie7pVmofvDpIWpIVx7EjIbTQ19gMF2toUIb4BInDtkrZaPcl+7LVHjUkK0ILDwIvSLA6VUWQQG85LlGl6khi2sWq2Pig2IoHCEckWn6NLBCeoFfTRjgzKyUtArUIi+dhnbJNYyVj7nCSnYuG8kYSKbtCmCA0+ojgUWlrMuu97ocRKSK6sqtTOnfp96ArQgsZFWGo4vVu71FCLeXIhEsiIiIXCNbikN7gT1OK4ogNeQwkyIxtEJ6gV/VuPF4n9lRQ0Ylm5OP19qa+VJyjWz9EgnO5TS1hgptgEulqzJTQ1p2fb6UcqLCLAynuq0I3M5YJUYzyGjUkMdbjMOlknwC0W19CZmdnqk/fI/TkdbBGDvegE7I5GzOSA31EP2QYspn6efQ2pifPzFkEdLTNLlAtson2blsJGMCDVMgA1yiTJk5/0QaMD/+mhSnegGwC7YisPDCRkJq0Hd7i3G4+7Ai0JnRyTCiONJx4ynHGww2mZzNxlZIL1BDGRzU6dZttqQI9CGLPeYjUKW13U4RkzF5wZ2E9jrFIYQwrKGUQtsVwACXMnGw4K/RKzPtHLmVqecDHjLBVgQWTFgZUtSQy+PF4SoCINjZBxVBF6ghK/4B7Xitj9jxOm7c+JgMUUM9QD/oF3CJ92ke0poMr4XCYskhi5na5wIJSU0mReQS2iclzZnllcQotAIZ4NTkJgtqKJg4W4c8KIJgdnRVT8BWBBZmLpoiEK4iHJ6oIojmFvQldCWhzErEkHY8JJrZGakho7IWKc6//JZs1i/goslqNTtakxHSUz2G1FAPJJTpByO9HGllNLkPkYhU63YUEDWk1tROooYsRA3pFYfalgdqKIskt56ArQisZFVGncW4vDg9ihoKBfrWusXxhejNaZpgWBLWRVCka59yvKGPoJvUUA+UbE6W0dhX0k1qSKcQrdbE6S6ypUCSlXZyTH48vLRwoob0NZyUTFlSQya5L92BocIsgMATWxHosipNoS1W7/TichcD8bITfQXBsERK0lAc6lXRO9b9wXDKIjZmMCrPYM1ZnJ4a0rblCykDoEFwQaZEvEwy6ms2Wa2J010kWlaZHdQpCjGJ+jGkjnp5gEt9dtayvPNJDaUoTIvlPfINWxFYyKok7CeCAKcbV8wi6FuKIPPs3MjZa86NJ8PldOByCOPBI62PIH1CmbYtXzDmxo18BOZO9kwyJofR9sRs2jA6xkoegYmMKdRRAQxw+hpOYJEaCmZHmWUvU/L71PuWE9iKwBKH6wj7CeIGIXBFfQSRYN/yEejjyo1g6OxNQ4kYnyNxRmaJGjL0EcTph2SZco1Ubjz7WkOQabYdTWJy9pwTPCfUUBrrrhAGuOR32jI1lIWllL1Mmd+n3oCtCCzMhkQoQFCoBUdcHkUNaUlmfQVWaBogaSC37iMAUlL8LTmLdc8lxfnXAyWbDQe4LGsNqTbpHbFaaW11TP7zI7KlQDL5Sgz39/IAZ6a8MiX3pbxfOVRoRu9Tsu+tN2ArAgs/AhEJEBQeQGUXA0SCfYwaSjKjk2GkMNNx44bnSBocrNBR+udi5PxT5+kBiyCBG4/3J6VUmaLdzCPQH98TC+4kUCAWFGrK7DpJxk4DyqO3B7jUZ5c5uCDbaKruy5Tqe+sN2IrAwsN2hv2EohaBJ0YN9VWLIEtqyGIegXbu7PII0lsQPUINGfgI9MEFyfx+Moos+gj0vpYeo4bc1hWqvkhdXEaDZ+NOVC69OcCl+jWshcnm01mcqjB7Jm8kE2xFYCGr0hEJEIpaBMLd1xVBBmoo2VmclY/AmUItZeozHJGEoo78VOdfD1JDSX4JLbgg8zVoPLO1wUcdk39Hq/7ZmS24kyyjJltcxvTUkH57b8CIGtJvNzwmKU9FbculRWD8PvW2P8VWBBayKh2RIGGHUgQ4VdSQlmTWV5Bp2UlzH0EW1FByyGEmaihpNp1KT/QCNZR0H6xHW2WwCBIUQf7LM+ifndmCO8ntHQJc0bpS5ko9f7PpbJH67KwV10uNpsqDs9jkfeot5E0RCCH+LISoE0JsN9kvhBC/EULsF0JsFULMzZcs6WBl5uKSfiIOd/SLUgh9ThFkoDiMFGa6GjuG5zCjhizSUb1CDZkqH/W+ZHsNhn0k+Vp6ojxD8rPL5KDWBkhtOc5kX0ny2hSFMMClyGTJFxK/L1YspaxlyvA+9RbyaRH8Bbg6zf5rgKnRvzuBP+RRFlNYmbG5IkHCDmUJaBYBfVURZEsNZeUjSOX8nbpomUx99gb9kMqNmyinDIl4GXlpd9Kg3BPUkL7PDA5qf9BIRgPrzl04A1xqfkb633pydr1R7ku3ZUpRmL1vOUEeFYGU8i2gKU2TG4FHpcJqYLAQYnS+5DGDlVhhlwwQ0aihqEUQyzbuI8iW4oj/aLoXNZTOokhO8TdKatJvzwcyKZ9M9y02q8w4206ihnowoUz1mZkaSidjX6CGjLLrcx3Ka5RHoN/eW8jb4vUWMBY4pvteHd12IrmhEOJOlNVAeXk5VVVVORPiRKt6yJu3bqeoYbdhm+GRAG3BCFVVVTjCfhYB7WdO5VSO3saWmiAAmzaso6YkdVBr7FD3acv2nQxq3kcwGjVz/OhhqqqOW+rj9KlOmtoisft28Igfhwyb3sf9J0MAvLNqDYfLHOxpUj+W3Tu24zi5i1OdSqZtO3cxrGW/tQvNEjsOqfuybvW7FLsE+2qVTCtXr6V6oJP9zUqmPTu34603fn9cAvYdNL9PdY0deJ3E7sOpRj+nW8zvSy7QEQhRe7yaqqo6AMKBTqqPnzTt80hNJ5Fg/NmdqA4QCEVYvnw5Qgi2H1X3acPaNez3CvY0qPu0au16GoZYnyzkEluPKZk2rlvDoSIHuxrVs1qzbgPNB1Jl6gipd/rY4UNUVVUD4JBhDh49FrtP3ZapOvo7W7+WY8Xxd3rN+k20HOqd+wS9qwgsQ0q5BFgCMG/ePOnz+bp2okgEhFB/UVSfaod3ljNp2jn45lUaHrbnzRCidCA+nw8iYXgbSovcdFmOAsTxNUdh2zYWL7yU8oFFKfsbWv2w4g0mTp6Kb8EEznQG4bXXmD5tCr7LJ1nq41+1m6k7cip2315u2MqA5nrT+xjeVQub1zNrzlxmVQzGua8e1q5l/oVzmDdhKM3tAah6nfGTpuC7bGJXLz0tdsj9sGcPV/kW43E5EHvrYdNazrtgLheOH4L3QCOsXs1Fc+ewYPIww3MUL3+V8jFj8flmGu7/2ba3KS8rwue7CIBXm7ayv6Uub+9XOCIJv7KUqZMm4vNNBWDIlrcZOLgYn2+e4TFP1mxgcKgVn28xADvZDwf2sGDhIorcTva/fRB27uKKxQsZWOSm+GAjrF/NzPMv4NIpw/NyHZlweOUh2LET3+ULGVLqoexIE6xbxYzzZrFo2oiU9g2tfnjjDWZOV+84wIBVyxg+cjg+3wU5kenY6iOwfTuLF17GiDIvg481w9qVzJh5Pr7pI3PSR1fQm4qgBtCPvBXRbfnBjmfh6Tvgi+tg+JTY5kzmYigcwUOAgOYbcDgJ4UT0OR9BdtRQMnduBYbUUBofQwof3wv8qn4BF9WnCTWU7joy1N1JXhAo39RQcmntuIzpaw0lywjq3hfp/Av65C1tf29B69spwhw6dAhXWzv/e8NoBnTWsmtXQ0r7UETyvzeMZkhxC7t27QLgv64YisdF7Ht3cY43xP/eMJq6YwdoEAJHOML/3jCaQf46du1qzEkfRUVFVFRU4Ha7LR/Tm4rgeeBLQogngIuB01LKFFooZ/AMABmBjkS3RaZIgkA4gkeE8GuKAAgJN45IIG+i9gYsx8PHnKTpFYfxOVKTkLLyESQNYD1RslmTMRYtk6wQMyTiqX3pw0Gz5eu7C6Nnl8lBbSRj/Fzu2LH6ekn6vnoDmkwNJ08wcOBARo2tRNS1Mm5oCYNLPKntg2FkbUvCfmdtC16Xg/HDSnMiU92ZTk6e6WTG2EE4hFDjTlKf3YGUksbGRqqrq5k40bqVnDdFIIR4HPABw4UQ1cC9gBtASvlHYClwLbAfaAc+ky9ZACgeqj7bkxRBhkgCfzCClyAtrkRFIPqaIogOVGZlpd1OgRBxhWllAEyG150ae57O2ZzJMdsTJZtTB8BEB3UmBar2ZXLEGieUSSljCiiXMJLZ63LS3BHMSkZIzKdIpzB7A1oNJ7/fz7Bhw2JJgGalhrRqGPpbLkR8ey6g3Q2tC+1+5aoPIQTDhg2jvr4+q+PypgiklLdl2C+BL+ar/xSUDFGfSRZBplhhfyhCCUHQKYKw8OCM9D1qyO0UOE0Woo8nHRnTNFagDYjaAJcxaihDHoH2f74TyhLrACXnEWS2jDwuR9pSCykRObqaOB5XHhSBQe5DchE5IxkHeOPDRTL1Y1QvSb+/N6CXSQiBIzboGo+6ErVdEL/nDkROV8CTUuIQIqYANKWTyz66MnnoP5nFJhZBpqxKfyiMlxBCbxE43DjD5rOnsxFWQkH1g64Vbjz1eAcRqbhYSOWdjdrr+zIadPNOo6Rw4ybUUFofQaYY/dRaQ+rc+bkuo2dntOBOioym1JCWl2C0v3d9BHqZtOHRbMjVZuWOPFgEd9xxByNHjuSKS+YlWBxaX71cfLQfKYKiQSCcKRYBpI8V9gfDeEUwQRFEHB6cso9RQxbWFtAPul2ihgxm+GkplQw+gphMefcRGFBDaayUZKRTVsmltbX2+nPnGsaWVSYfQWpCmf5c5tRRb/oIEmXKaBFEt+tn1A6RG4vg9ttv55VXXkGSSPdp/+dz3W0r6D+KQAgoHpJiEUD6GVvAr8pNO9zxkMqww4NL9jGLIJjecQskLPVnZQA0Ol71Fef8u0INeZyJM9leoYaCxn4LI6QbZJNLa6v2+aVVzJ3F2SWUQaKS7slrsIJkmeI0jHF7aWIR5GKMXrRoEUOHKlZC/6ZoXfW2RXBW5BHkCiE5GGd7I8kMWrofQdDfDoDDrbcIvDgjfUwRJJnRRkighixw46nHp9IqmQZQ1S4+6LqSSlLkP8ImeYAzCaPNoNAaW40tSMPZeZ4X3DH2EWQZ2WRQc0m/PxZc0Os+Ah01JAR/evsQx061J0wmNIQiEn8wTLHHGbMe/KEI4YikxGP+2zh3zEDuvd44R8QIyRaBQ4iYf6K30G8sgtPPP8++R9oJHk/NEEw3YwsGNIugOLYt4vTglsFeN+dyia5SQ0VZ+QiMHIwWooaC+vaOlDb5zSNIlDE5uMAfiqhEszQOunQx+kYLAvUYNeROHNgz1hpKpxCTqCMrFU3zDaM8FQHmTgJ9mzwiOR4jV1ZHd9BvLALv9OkAdBxqJDlaN91sKBilhpw6i0A6PXhEK1oyTV9Aptk5JA66XaKGDGb46ZysLqcDp0MkWhBJ9zvfJZv9oTBDSuNvTPIAZ12BmkelaW3i7XuHGtIW3HEYRI6lFKkz9PckK+n8l9NOByOZPr94MoOKXYwdUpLSvrHVT01zBzNGD8QdVfg1zR00tweYOWZQTmSSpEb1OIQw9Vv0FPqNReCdMgWH10n7sbbUfWlmbCG/WqTe6Yn7CKTTg4dgr5q9uYaVZSf1P+wuJZQZOH+zs0JSB918l2w2HuAcSddgnVIzOr/WRn9+yCM1lEb5aLH2eoTCEUIRmVZGo/uQb2stE4xkShcFpG3WD9OOPMzWk43HQrAI+o0iEA4HxROG0REtZKZHWmdedCUypydODeH04CXYq2ZvrmFl2Un9oNu1PILsoobUMY4Ms848U0NGA5xb7yuxqMxMBnXD2XmeF9wxKg+STMPpoSmHdDIarU3RE+sqpIPRxEFFARm312blDj2HH80j6C4NfNttt7FgwQIO7d/HwgvO4eGHH07oo7ctgn5DDQEUT6ugYW8t4VP1OIfEi06ly6oMB5RF4NJZBDi9eAj1+qpCuYQ/FGFoaReooSzzCNSxYUJh5YTLPIg6k3wEBtRQnmsNGSsfa/QWpOffjdZt7hVqKMH5m1ijxsy5rJcxYEbb9bazONlHIDB1zGpjcXKcvyRK6XRDlscffxyAvbUteJwOJgyPl6zIh9WRLfqNRQBQfO4UkILODasStqebsYWDykfg1isClxeP6GPUUBcTyoyiL9IdD+oHalWRJFghBoNuj0QNGfaZPTVkNKtMSw3lLaEsO7+EoXPZKKEsDYXWGzB6No501FA04904zj83MkmZaHFoffS2RdC/FMH5KsSrY+OGhO3psiojAUUNubw6asgVtQj6GjWUBcXhD0XwOB2GjkXT43V0glVnc0ZqyJ3fwcaY+3Zm7ecAY/7d2HGb3wV3slU+Zs5lvYyG/ptCiBpKkkmkKRkRkakDYq5LQChlk7jNtgh6ELsad3F/45u4BwZp37ojYV86njkSUtSQxxu3CBwub990FmdBcVjhxlOO1w02Vp3NiVZIL1BDGRzUVtZtThcOauRryb+PILG0tl7GTgPlY6Q4hBB4XMnvQ4FRQwZKOq2z2KDIX65LQESkUfionUfQY2jqbOKfJ1dyZlSYjt0HkZH4C5pu5hLRnMW6zGLh9uLtgz6CbKmhbPwD2vFaX0bcuPExGaKG8jjrDEckwbA0GOCSrJRMiXhu8xm+WdkMtS9/1JA3KfchXZE4s6S55LySFAotQ25CvpFcwwnSl4yISKOIntyWgDBTNr2dWdxvFMG8UfModhaxY6yDSGsHgcOHY/vSxTvL6AI0wqWzCNxFeEUQfzA1AulsRVcSyrKJGNKOBzUjtUwN6ctaGFgt2qCcj+Q+owVcVJ/JVopVi8Aq7ZJvZ7FxqGdGGU3uQyQiCYTNfAS9Qw0Z1XCCTBZBKn+vHZ2rJxHBQNlgHsnUU+g3isDr9HLx6It5fYJKDurYtDm+L83MRUadxbFF61HUEEAg0DdKUccXos9M0wTDknDEWvuU4xN8BDmihnQlm3MNMxkTfSXdpIYMFGK+F9wxs6yykVE7xh+M6MJLU6mhdBVN8wmjGk5A2nIOqiBc4rZcWgRaGGqqs9i8EF5Pod8oAoBFlYvZXO6EYhcdmzfFtuuzKpMhQ9EaMTqLQKOJQtHyE2c7gmGJlFigOKJOz1AEfzBsuoiNGfTlGbJzFqenhrR9uYbpAKgLLrCaiKe1TenDoGZTvhfcMbas0tFXJgox6itJTx31jiIwe3bpkreM+HtHzFncPXnuuOMOysvL+dBVC2LKpqmpife+970sumgWn/3ojZw6dap7nXQD/UoRXD72cqQQnBrrpmPz5tj2dFmVIhyd9TvjFoHToyyCYDTr+GyH9dm53tmbmRtPhsvpUCtG6QcPSz6C9All2r5cw8oAZ8SNJyO5QFtCHxbop1zD2LJKI6PJs9JkNKWOejGhzKiGE6Qv5yClTFiUBvQriHVPE9x+++28+NJSJUO0jwceeICrrrqKlRu2cfFli3nggQe61Ud3kFdFIIS4WgixRwixXwjxbYP944QQy4UQm4QQW4UQ1+ZTnlGlo5gWcbJ9dBj/vv2Ez5wBUkPhEqAtUq+3CKKKQMsxONthFFduhARnrwVKxPgcjmgeQRbUUIKPIJV+0F9DLmHOjWdfa8hMRm1bcj5GPp3gOaWG0lh3vVlryOydzmQRGJV/gO5bBIsWLWJItAy1ds5//etffPrTn8Yh4PpbbuO5557rXifdQD7XLHYCDwHvBaqBdUKI56WUO3XNvgc8KaX8gxDiXNQ6xhPyJRPAIsdAVoxt5HKgY8tWBly+MG1WZcwi0C1M4/KoglWhPuIjyIamgXhCmH7pQqvQyjNk5SyOLm9pFsqpZOpBaiglj8AiNWQ4yKaW1lbH5C8/wqxAXDoZNZlSZTQPBS5Eamhg1fcpO7EN6XWmzP7HBENqtq6bbBRJyaRAWFXZdZgo/FHnwzWZZ/PJC9/U1tYyevRoas90MmzESGpray1fX65haUonhLjFyrYkzAf2SykPSikDwBPAjUltJDAw+v8g4LgVebqDRUWj2DtGIB0iRg+l+xGIcIAwDnDEXw5XX7MITMzoZOgVphVu3PAc0ZluNnSUPxQxdf7llRpKy42rSKVANlFDBsrKLB8jnwvuGMb8p1GoZrNrTcZOUwrNaep7yzfMnl3a9Mc0dSRycQVGC9+AshCSM5p7GlandN8BnrKwTY+xwDHd92rg4qQ29wGvCSG+DJQC7zE6kRDiTuBOgPLycqqqqiyKnYqJZ5x4XZL6kcUE3nyTHeefx4ETKgz07XdXM2ZA4osT6DhDADdrdH0Oq9/P+cCxw4e6JUuh4FiL+iHv272LquZ9pu321an7tHL1Wk6d8TNAtmV9/ZGgn6M1JyjtUOtCbFy/hsNF5gPpyZoAncEwbyxfoWQ9cpiqqprY/j31SqZVa9ZROzi3JcF3NqrBZNf2rUSOx899ojpAIBzhtTerAKg5dpiqKvM5zPFWdX83bd2Op353wr6DR/04ZDjlPgY726k52ZmX96v+VAdlbpFw7mB0sN69dz9V4aMJ7XccUnW41q5+l2JXfLBqae6ksUOyeu16APbsTLy+mqMq0OKN5VV4nD07yB08rZ7dnl07GDV1GC0tLQCcvuwHNHVGGD/QkRK9c6wlQpETRpTE38dQRHKsJcLwYgdlnjTXED1/OrR1qEWuOjs7aQn7GTFiBPv27aNkSDn1tScZPnx4TM7uorMzu3cnrSIQQlwDXAuMFUL8RrdrIJCLIPrbgL9IKX8uhFgA/E0IcZ6UMmEqJKVcAiwBmDdvnvT5fF3vUa7isp1r2D3Wg2/PMS5YtIjArjrYsoFZcy7kvLGJdcdfWv2/hCMeEvrcF4QdMGrEULolS4Fg87FmWLmSC2fPwjd9pGk7x9562LiW82fPwbl7MxVjhuLzzc6qr8Gb3mLw0FImTB4GO3bgu3whQ0uTV4iIY1t4Hy8c3MuF8xfAG8s495yp+C6dENvv3t8AG9Ywc9ZsLp40LCtZMkHuroN165g/by5zxg2Jbd/Jfti/hzkXLYDXlzFj2lR8CyeanudYUzu8s5zJ06bju7AiYd/Shi2UNjekvEdDt79DWakHn29+Tq8J4IHNbzFqaAk+37zYNiklvLaUMeMm4PNNS2i/PbIP9uzlKt/ihEixp45v5PSJM5w76zxYs4Z5c+ewYHL8GRx0HYK9O7l4wUIGlSRSrvlGyaEmWLWKeXNmUxSqp6ysDAC/8ENnByWlA2JrDmgQrWfweFyUlcXXKgiFI9ByBo/XS9kAL91BUbRMTWlJMWVFbj74wQ/yzDPP8G9f+hrPP/04N9z4wZic3UVRURFz5syx3D4TNXQcWA90Aht0f88D789wbA1QqfteEd2mx2eBJwGklKuAImC4FcG7jJKhXN7ewdbRASKtrfj37U+bVekIBwiKpIEqGkEUCSVRQ0dWwfZ/5kXsfMLqspPJPoIuUUPuLKmhKGVxpjNo2L5nqCFjB7WZTMlIn6xlHHWUdx9BktM9XjLCWMbkkhR6GU0jn9JEIuUbZo7+dOGgEYOs31zlEdx2221c6bucIwf3M33yRB5++GG+/e1v8/rrrzN/9kzWvLOCu++5p1t9dAdpLQIp5RZgixDi/6RUq7ULIYYAlVLKTEGv64CpQoiJKAVwK/CxpDZHgauAvwghZqAUQX32l5EFioeysKOTX1U6gQht776LZ9H1gPEL64z4CTuSFEHUcSyDSc7id38LJ7fBeR/Kh+R5QyxyJcOA5tENulZq7BghFmkStNhndNZ2ukMZoKbx73lRBGZhk4kyZbqG9HkEJj4Cl5P2QH4y182enZnyMSpJocmYUC4kCwd0vmEmU7qBXabJI+ium+Pxxx/nTGeQww1tTB4xgNJooMWyZctobg9wtKmdIUNyYw10BVZ/ya8LIQYKIYYCG4H/FUL8Mt0BUsoQ8CXgVWAXKjpohxDifiHEDdFm3wD+TQixBXgcuF3meyHgkiEMjkQYPW4StaOKaF2+PG2RL6cMEhZJZm3UItDKT8TQ3ggdTXkRO5+wHjWk/bDDXcos1s6hRQ05BLgyVC/VZq7x2bf1+PfuIl0eQaJMVp3sxhE5Rvc9nxE3Zs/OLHdBKQ4zGcNpLKfetAiM3+lYyYikUUZKqSwCgzyCdBVLs4G5szi39Yy6AqvO4kFSyjNCiM8Bj0op7xVCbM10kJRyKSokVL/tB7r/dwKXZSNwt1GsYnkXDZzCykl7KV+zAW9HK2A8Y3NGAoSdSdxgNKfAUBEEWiEUSChJUegwM6OToe3vjFFDXbMImjsCsQEwU6REbNDtyEAN5YFGMR3gNOXUYayckhHPqDahhgyjhvKtCMwH9mxlNI0qSlPRNN/QU4/6tM/YoJsUB6R9M5qXpKtPlA2Sw0c15LrCaVdg9ZfsEkKMBj4CvJhHefKPkqgiKB7DhikOCIdxr18DGP9Q3TJAxIwaMlIEcNZZBWYz32Ro+1v8Gk3TRR+BpkgsVC+N8/GhhO/J+3uDGorJlOE6HA6Bx2k8sJv7CJz5TSgz6tNE+aSTUSs3oh2feL5epIZMnp3ZoGs2SGvH5GKyHilgi8CqIrgfRfEckFKuE0JMAszjDAsZUYtgWsRJx9SxtJe5EaveAaC0fnM8kzgKlwwScSRZBFq5iXAgvi0Sho6o26T9LFMEWVJDZrNzK9DzylaOT7EITAbl/NYaMlEEWdwHc/49De2Sh5l0vLS2ifLJUkZIp6R7kRoKGltzZoOuNkgbGagiTenqbGBqESTJ0Buw9EuWUj4lpZwlpbwr+v2glPLD+RUtT/CWgcOF6DyFb/yVrJ0UIbR6JePlSa5a+XHYFk+NCIUjeAgincYWgdArjY5mYgbm2WYR5CiCxwr0vLKVqKOMUUN5XMRFG0xSyz+k91sYwazujrmzOD/UUCCN0jelhtLICL0T0ZUJ6UpMQOoM34y/147JxRWYKRszuqonYTWzuEII8awQoi7694wQoiLzkQUIIZRV0N7EVeOuYt0UCa2tXNW4AYGE5ngyTcBMEUS/x8pPQJwWgrPXIrBQAA7gTIfxDNAK9PVprM2kNSvEuM8Y/54XH4FJtIw7+T5Yuw5T2sU0szgfVo650jdTPulkBPP7kO8lN9PBTBFoSWTJReTSU0O5tQgcKQ5pbX+3u+gyrE7pHkHlDoyJ/r0Q3XZ2omQodDQxe+Rsjp4zlJDLwYUnoxmRLSdjzfzBCB5CSFeyszhqEeipIb0iONssAi2UM8NC9Nr+2AwwyxXK1DHOLH0E6Wed+oqmuYZ5dE2iTEUWr6MrUUO55o3TKX2zshaZqaEgnjQKs7fyCJwGNZzMBt0Yf29wLkH3aZs77riDcyePMyxDfd650/n8x26iqelUVDbJV77yFaZMmcKsWbPYuHFj9zq3AKu/5BFSykeklKHo31+AEXmUK78oHgrtp3A5XCyY7GPnBAcVJ+rVy9EaL/zkD0XwEoDkqKHod0fERBGcdRaBceGzZAgh8Loc3fQRKPqh0yQkMaV9bPZtrnzyRaMop6rRAJhd1BCoXAOzOj5m15SPBXeM1kjW95mtjKDuQzqF2Vt5BEYyabPxFIuA/FoEt99+O489/RzoagppZah37d7D/MsW8aufPwjAyy+/zL59+9i3bx9Llizhrrvu6lbfVmD1l9wohPiEEMIZ/fsE0JjxqEJF1CIAuLLyStZMjuBpCxE440q0CEJhPCKUUHkUAIeDEC4cfcUiyCIU1OtymDoHrR4fkdAWsJaQlilqCPJIo2TkxrOghsxm22kSyiD3s+lM1JDRimKZZDzTGTJRLL0bNWQkc8wiSNpuFtGjHdNdi2DRokUMHjI0gRTSl6G+4ebbWPri87Htn/rUpxBCcMkll9Dc3MyJEye6J0AGWM0juAP4LfBL1D18F7g9TzLlH8VDYrP2BWMW8MA5RfBqOy3Hi/BWJFoEAwnSmawIgLDDbWwRFA+B9t5baagrMJv5GsHrdtLSzaghgJaOIEMt1J+Jhaym6TNfETbp4ucTZbJg2Rg4YuOltY2UW3w2nct803RrT5j7MdLL2GJmEeSxRHgmmMn84Pr/ZtPJHXhcjoRaQ+GIpDMYpsjtxJmkDTqDYaSEYo/xc54+dDrfmv+tjDIlWyFaGeqIlAwfWU59vSrEWFNTQ2VlvDpPRUUFNTU1jB49OmMfXUU24aOfllKOkFKORCmGH+ZNqnyjZJiatUtJkauIcyfN5mg5nDo+EFrrVCgomo8giDBSBMKDUyYpAlcxDKw4+ywCi6GcoFkE3fERxHllqwOo1l7/PblN3qihNDPdbO6DkYxmpbW19kqG3F5XPHnQeGDPKqFM92zSUUe9ZhEYyGRmESTvzwfM2CVBPIO5t2DVIpilry0kpWwSQlgvbVdoKBmqcgACbeAdwJVFY1gzVVC5UhLqkLjaG2HASEUNEUK4i1JOEXZ4cAaD8Q3tTUrBlAw5C30E2SmCujP+6P9do4ZARZpYGkB1kSlGzj9Njrw5i9Ny49FaQxl8K+oYJ42tgYRtaR23sYibHFNDaZIH09YaSiPjmY4QI8pSfyP6Nap7GmaTm2/P/zbbqk8zoszDqEHFse1avZ9p5WUUJSnJo03ttAdCTB81MPl0WUGVsIijvLycEydOMHr0aBrrahk2Qrldx44dy7Fj8Qr+1dXVjB07tlt9Z4LVKZ0jWmwOgGjNobytbpZ3RJPKtJn7okCETVMEQkLrCW/MT+APBHGLMA4Tasgpg3EnUnujUjDFQxP9BWcBrMb0g/rxa2s7d4caCoSzSyhL1z5f5Rgy+QgC4QgepwNHhnpJcRkTB3WzpCd9H7m3CLpADaWpNQTmz0YLLuitqCGzd9qI89d+xkYWgYMchXYmneOGG27gr3/9KwDPP/0477/mutj2Rx99FCklq1evZtCgQXmlhcD6YP5zYJUQQsu2ugX4z/yI1AOIlpmgvQkGj2NQw35KBwmaBzgYeKKIwdHIoWBAVSlxeIpTThFxevASwB+KqBlEe2PUIhh69lFDFkM5IXH22h2LwOrxLofAEf3hmiqCvPkIwgwuSa0ZpZVstrI6mQYjaijtoJynRDmz+kmaHNqKYnrlZmYxFiW8Cz37bDIhnZVrFAUUMYnxBxAO8wXvreK2225j2ZvLOdXUSEVFBT/84Q/59re/zUc+8hEefvhhho8ay5K//h2Aa6+9lqVLlzJlyhRKSkp45JH8R+pbUgRSykeFEOuBK6ObPpS09vDZhSSLgLqdTBPD2TipjsG7vYxpPo4AQv6oInCnWgTS4cFDKFERDJmgzt1xCiIR8zVOCwzZ+ghi/3fDR5B8LjOoWaWTjjThpvkq2ZxuMNEibCwrUIPZdq9QQ2nzCOIz/KLo0qyhsFomNJ2vRB1r8mzyuORmOqjfpfGzMbQIdPuSkQuL4PHHH+dAvSpuOXnEgNj2ZcuWAbD7xJlYaWohBA899FD3OswSln/JUsqdUsrfRf/OXiUAiRaBvwWajzIqPI1NkxwQcNCxdSsAweji9E4DRRBxevESjJu9HU1xi0BGwH+6Ry4lF8iWGor/33VqCKwrEq2dWfv8OYtTF3CJ9+lM+MwEoxj9TLNzTYZcIr2PIDUTOEYDpvGVmJ1P215o1JBREblImszi3NUaImV5TH0f3bU6uoOzY8qaa8QsglNQpzKK2zzncrDyIsIC6jYoPRcOqBXIXAbUkHR68YiQirsOB6HztFIExTolc5bAKl8P2VM7uTheOyadj8Ao/r27SEf9ZJIppb2BHyOQga/Xt8kV/DH/ThrlE44P3FZkNNuvbe8NiyDdsxNCpJahzpBHIOl+ddBkZ7Eeuapw2lX0U0UQ9Xu3N0GdGvTriifhjFzPngo4saMagHDUR+D0pEZE4PTgIahecq3qaMnQuLXRcSr1mAKFP5iNjyD+48+0Mpfh8V2wKDLNvs2cnN2FmlWaKx+wfg80GfWDSbqqr3nzEZiUjFZyaHH/8T6tyGi2X9teaD4CY2exWpTGrAw1dD+pzGgFtLhMtkXQ83C6wDtI0Tl1O8FdSmvRGMKBoQSnFDO4NkTNwa2Eg8oicBtYBLi8ykcQjMSjhM5SiyCb9Ye1H5fbKVISbywdn6WPQN+up+kH5TvJQA1ZTcTTRdjoz6/OkS4GP08+gjRF5PTKJx2VpA+bNaXtTHIT8o10z85hsOJYRJrnEORqvQBpsCZyvA/z3IaeQP9UBBCP96/bCSOn43G78QcjXD5zEgDLnnyQSFQRuLwGFoFLswjCiYogZhEUqCJoOgTbnk7YlG7mm4z4oJw9LaQ/HrIYRHvVR5AjasiA809f7iE/5Rli61Mb5mOkKp90q9dpC+7ojzU6Z+84i40X3wHz8FEz/j5XFkE6ZaMimbp3/u4gr4pACHG1EGKPEGK/EOLbJm0+IoTYKYTYIYT4v3zKk4DiaJhn7U4YOSPG4Y6Zeg7tAySRVRup7zwOgMsgoUzEnMXJFoGOdipEvPlj+Oe/xbKnIduEMo2m6dqrkxdqyJ17+iESkelzF7L2EaQ6YtPSLjGaJve1hryu1EqhCX2GrMmoP6anabtMyDZ8VM3Wjc+VM4sAae4sJrUERU8ib4pACOEEHgKuAc4FbhNCnJvUZirwHeAyKeVM4D/yJU8KSoZC/V5ob4CRM2P0gigbzdBRHcw6FOGdjiolp5EicCtncYoiKBoMwlGYFkEoAPteU1FNuqQ35SPIcnbeZUWQB2dx9NnlsmRzII1TFeIDe7bXYDjb7sk8gjShwkZRQ5kWLcr0PhhFS/UE0tGdwihqCHP+Xruy7rxdd9xxB5fPmsKVl86LbdPKUE+dOpVP3nw9zacyl6H+61//ytSpU5k6dWosGS0XyKdFMB/YH13NLAA8AdyY1ObfgIe08hVSyro8ypOI4qFw+qj6f+SM+MylbBTDR3VSHIDw0Z0ccLviS1PqIFxeRQ0F9dTQUJU7UDS4MC2Cw2+D/4z6v60e0Bc+y5Ia6sJ6xeq4/PgIIlLV7skVMq3jHL8P3aCG0vgI8lWeIW1IrMH6AZnvg+YrMVMUzrxEdKVDKBwhHDFejhOURRBJiRpKx98bl67OBp/+9Kf5w98SKVmtDPW+ffu4bJGPJb/9BWBehrqpqYkf/vCHrFmzhrVr1/LDH/6QU6dyE5SSzzIRY4Fjuu/VwMVJbaYBCCFWAk7gPinlK8knEkLcCdwJqj5HVVVVt4Wb0tSOtsTau/ubOVEdJBCKsGn/cS4o9yOdDi7cL3hk/kBu3LSVtn1nEo4f1XiasYTYtHU7M9q2MNpZzDvvrAJgPkW0HtnNzhzImUtM3fu/aBVLNr/7Bs1D6mOFz2qOHaGqKnOp25qjql5OsLO9S88hrBusd+/cjqtuV8ZjzjQrX01TQ51hn8eOqJpPbyxfQbErN4W7mjvV4HXk4H6qQkdS9p9uUjI1N9Rbug/7a1XC28pVazg2UA2e26Nyr1+zmjJPqtxOAXsPHKLKWdOlazDC0Wo/MhQ2lPnIGaUANmzeCifU0LClXsm9Y+tmOo6mKhAtxPrY4UNUVVWn7D/V4Od0q3F/+UJnSL1j1UcPU1VVw6BBg2hpaYntD4UihMMkbAsEI0iZuC12vqA6X1tbO2F/196v2XPm8O6Ow0gpY308++yzLF26lJaWFm665aPc+sHraGn5EU8//TS33HILra2tzJw5k6amJvbt28fbb7+Nz+fD7VZVe30+H88++yy33HJLqsydnVnd896uF+QCpgI+oAJ4SwhxvpSyWd9ISrkEWAIwb9486fP5ctD1Gqh5EUqGcen7PsjmFQfgwB7OXfA+HFu+z4BzJ3DxgRq+fkUJd194AYNHJ9bYO9PyOp7aIJOnTafySAm0jiQm1/4KStwuRuZEzhwhEoEN/w4jZkD9LmZPGQvn+2j1h+C1V5kxdQq+RZMynuag6xDs3cmwwQPx+RZ2SRTXG0sJRSTzL5zD/IlDM7Z/9uQmNtQeZ0LlWHy+81L2H/Uehj07mH/JpQwbkJr81xUca2qHquWcP3MGvgtTV2V9oW4La09WM65iDD7f+RnPJ/fUwaZ1nD97LnPGKT/S3rcOwK7dXLn48lhWqR5Fb77CqDEV+HznpuzrKp46vpFBwTMY/Yb217XCuyuYOv1cfBeMAaBz+wnYsJEFF1/EjNGpRdcGb36Lk+0tzJw+Dd8l41P2v35qG7tPnzTsL19oagvAG69z7jlT8V06gV27dlFWpop5n/yv/yK4bSciEqHJE7/n4Sh91WRgLYWlxBkIc8btpN2AP/LOmM6o//f/0soUiqiJhUOImCz19fVMnToVgIqKShob6igrK6Ouro5p06bF2o0bN47Tp0/T1NTEpEmTYtsnTpxIU1NT7LseRUVFzJljvS5oPqmhGqBS970iuk2PauB5KWVQSnkI2ItSDPmHFt0z8lyIljEA6CwaCUDpueWMaPQz8IzguZoVKYc73V68IoQ/GIrXGdKfu9B8BMc3QcsJuPB29T1KDaWLKzdCnBPuGjWkju1axE0mmiaXNEp3ufGU9umoIdM+cu9oTR8Sm+qgThduqsmYdn8v5BFkena9Ue05E6vkcIj81sDOgHxaBOuAqUKIiSgFcCvwsaQ2zwG3AY8IIYajqKKDeZQpDi26Z6SabcV+BI5icJcwoKKUOuCmnQH+MXYpn7rwKzhE/MVyRh3IwYA/qgiG6849FE5u75HLsIzdL4JwwqyPwGvfVesukPmHnoxMnLClc7idaoWyLOr06D/N9udy0OzMuY/AIEY/FDEtra31kY8VytLF/KfIGPNjZIgaSuN36OmooXS+l1H/7//B6U7qWzoZN3ZQjP/fV9uC2+lg/PDSlGM6g2H21rYwfGgJgwyKEFqBUSCDvgx1fe1Jhg4bgZTStAz12LFjE+ie6urqnFlaebMIpJQh4EvAq8Au4Ekp5Q4hxP1CiBuizV5FLYO5E1gOfFNK2TM1nGMWwQxAP2OTMKAcT0krreVDuGKN4FTDcVbWrEw43OlRFEQ40HF2WAS7X4IJC5VspSPiFkGG8MBkZDubT3+O3EUNQW6Tr+LF2XJXawiSZ9vpnfT5iMFPX0jPSFllsIwsPButomlPIdM77dBKRui2yXQJZdHP7jwJo8vXl6F+8vG/c8X7rkFK8zLU73//+3nttdc4deoUp06d4rXXXuP9739/N6SKI68+AinlUmBp0rYf6P6XwNejfz2LURfA+IUw5SpAF+cdjRwSrXVUX3oO059bxWdWenhi6hNcXnF57HDNIggFOuOL0mgoHgLBdgh2gkHoaY+jYR807IGLPqe+lw6HtgbAghmdhO4mlCWeI0s6KtNMNocURHcHwGQUGc22M+Rv5INW8YciDC42XiLUOMTVYtRQhv36iqb5RqZnF88LIDbKR9LF+Ocgj+BTn/g4y6uqaDYpQz2mopIf/+ZhIlKalqEeOnQo3//+97nooosA+MEPfsDQoZl9bFbQ287i3kPpMPjMS7GvCT+CAeVQux3/4EqGTG3j8vWCV85dQfX8airKlOMwVpo60AKB1riFAYnZxe4xPXI5abE7ep3Tr1WfeosgjRlthEycsKVzZJmU1hvUUGZuPFsfgXH5hnQKNR/lGfzBMN4yY4d6trWGNBnT7tedM3nlr3whk8yx5Sp1miCdRZCLzOJHHv07B+pbmTi8lLKiuCLWylA3tvqpae5Akr4M9R133MEdd9zRdUFMkE9n8VmFBGde2ShoqUVEAgw5vxPHsGF87tUIT+5+In6AS830i9qji90nWARRRVAouQR7lsLo2TAoGv2SC2qoWz6C7HIReoUaCmYYALtYayg5oSzdfcwHNRRIk0egLbiTqKzCCKFqS5nJCBastR6sN5RpcmM0sEcs5BF0xyKIrYBm4qnOVfZyV2ErgigSsioHlEOghdJwCxGvm9Hf+Q6TTkqaHn8Cf1itUaAlmRV1GiiCQqo31FILx9bC9Ovi2xIUwVlADWUabAwyYruLdDV2EmTK1iIIZkkN9aCPQPXpSKGGzEpSaDJqx6Xb35MOY+vUUHzQTVcZNBcWgVb2Op9WR3fQf6mhJCTMXMpGATAyXEvY4Wbgtddy9LE/88FlO3lj09N8YN7HIbqOcalfDagFYxE0HoDq9fHv1esAGaeFQCmCYDsE2jLOfJPR3VpD2jkcQi1Daal9hnIO+SjHkJkb75qfI9VHkIYacjlo7giY7u8KMjuonVnLqB2Xbn+PWgSZnMXRT+0qpZSxMtRGEEIg6F5ROG2AT1eGWpOlN2ArgigSqKEB5QCMkrWEHR6EEEz98YPsu/4DnHroD/DIx8EZVQSBaFWMQrEInvwU1CaFrg4/JxYmCyhFANBWjz+kriP7PILuRQ15XU7TWaZR+3R95jVqKEe1huIlI7KIGnLnfr3fjH4JlyOl1lAmGbXjzM4H8XDcnoCRRaAvIZE86GoRROnmJao+UXeoIfMV0CC3FkFX5LQVQRQJJuxwZRGMkg20ONT/RZMm0XzFbM6r2sz2g6s5z6WooYGBArIImg4qJbD42ypfQMOA8kSbNKYIGvCH1PX1VK0hdawjKx9DrziLMyTaZesr0Uo2pziL0/oI8kQNpeszyUFtRUbtOOPz9QI1lOQjKCoqorGxkWHDhiGEiA262ngZ4+/TTEwcIjfho/m2CKSUNDY2UlSUXbSirQii0H7YD725n5dLO/kD4BEhIo54AsnMf7ub2jc+wfq//pLzPqsiXt1tqj7Pp5/YR0TEc+H+JLwsW7uTx/et6bFruLrlaT4OfH3vDOoPNuj2NCS0mxio5X7gF8+9w8sBlYYeG2Rb6+HN++HqB8CTmlyTK2oom+Mz+wjU9j+9fZCl2zLXS7KC6lMdCec2lSnL63h+83G216j1rPecbOGiNCU2vC4HJ8908smHc/cOZVqW1OtysupAY6zPXSdaGFxiHG6qyagdl27/D1/YwSCTsNVc43iz9uyUTBUVFVRXV1NfHw+QqG/xc6pG4HAoyscfiuAvcdNgUOoD4OTpTk4CB02c5pkQjkiCYYnjdJHhgk6BUIQ6nUxmKHY7DcuR6FFUVERFRWpZlHSwFUEUowYV8f6Z5dS1+DkZLCGICzchiktKYm2GXnAhO6cMZ+zr22j8dDvDgNGOZlrkAM4EJBCKtT0jBlIUOq1q+fQQZret5JBrEofCIyBs3u+J8AAAvP5GBhS7uOa8UQwfEFV4+1+HjY/CeR+GSb6UY4eVerhtfiWXTx3RZTk/cP5oJo9IVTJmmDNuMNdfMMaw1g3AoGI3118whupT7Tm734NL3Hx4boXhAi4AsyuVTDPHDLJ8zo9cVMnGo6diMk4pH8A1540ybf/ec8vZW9uS03do3vghXD51uOn+D84ewys7Tsb6rBxazHtmlJu2XzxtBMebOygzGZymjyrj8qnDafWHeuy3MLDYzXWzRsfyJdxuNxMnToztP90R5BePb6KlMxjb5nY6uP/GKUwfZfyOvfTqHlYeaDDcZxWjBxXx29tmGCqCls4gv/i/TZzRyWSED80ZyydnT+iWHEYQveWc6CrmzZsn169fn7lhd/GLmXCmGiovhs++Ftu894k/Eb7v5+z4xnu4ueZRcLhg8Hj4ysbE4/+4EAaOhY/9I/+ygprJ/3waLLoHrvhO+rbBTvjPcrjy+7Do7sR9y38CKx6AG34Hcz+ZP3lt2LDRoxBCbJBSzjPaZ4ePmqEsOgtKWotgyoc+RfsAN5GX3iYMEAkl+gc0FA/tWR/B3lfUgjPTP5C5rbsIvANj2cUJaI6WXD59LHWfDRs2+iRsRWCGAVGT3ZXodHF4PISvu4KZe/y8E4nSG0aKoKfrDe1+CQaNg1GZSyID0TIT9anbTx1Wn6dTa8vbsGGjb8JWBGbQLAJXajr++Z9TdMqhQ4pr745FEEtQ6w78rXDgTWUNWC1lq0sqS8CpqEXQfLT7ctmwYeOsgK0IzBCzCFIVQXFFJafmTmT6DicHHK7EOkMaSoZCZ7NaEMYEq46vYsH/LeDomW4OugfehLDfGi2kwUgRBDvVmgVgU0M2bPQj2IrADDEfgXGBrsmf+XcGtcPy+sHIYgNFUDxUcfadzaZdvHTwJYKRIKuOr+qerHuWqoqn4xZYP8ZIEZw+BkgoGw2na9IqMRs2bPQd2IrADDGLwHghilG+a2kdHGHSeg//fWIdEZk0aMayi40Xlw5FQqyoViufbajb0HU5wyHY8zJMuxqcWUQDl45Q6yhEdNm4Gi00YSFEgtBa23W5bNiwcdbAVgRmiPkIjDP0hMvFufPOUN4sGf23zXxnxbcJRnQxwBmyizfVbaLZ38xg72A21m7sekbh0XeV1ZENLQRKEchIoqJqPqw+J0TXXbDpIRs2+gVsRWAGzSJwmi9NVzrGyajZZ7hon6T4sZf4j+X/QUdIZTVmqjf05tE38Tg83D7zdmrbaznedtyaXK11cHJb/G/z40pZTb7S6pVFhY8mFenpoVNHFBVWEQ01th3GNmz0C+RVEQghrhZC7BFC7BdCfDtNuw8LIaQQwjDZoVdQOgLcpcaOYA0uD0OntTHwfT4+8o6kpWoFn3nlM+xq3BVfE9nAIpBSsvzYci4ZcwkLxy4EYGPtxpR2KQiH4HcXqWQ17W/L/8HkqwzLQWS8PoitXQyo0NHB49Qf2BaBDRv9BHkrMSGEcAIPAe8FqoF1QojnpZQ7k9qVAV8Feq4ojxU4XfD5FTAwzQpjTi9CwOh7v4//WC3fWnqYe0cf49amW/no5A/yJYdgYHvqEsx7T+2lprWGO2fdydQhUynzlLGhdgPXT74+vUwNexUNdOlXoHJ+fHvlJdlf34CR6lNvETQfgSHjwVsGRYPtXAIbNvoJ8mkRzAf2SykPSikDwBPAjQbtfgT8FOjMoyxdw/Cp6WfaLg8IB44ho6j47W9wuzz85OUh3Drhw/zjwHNcXzGGlxs3pxz25tE3EQgWVyzGIRzMHTmXDbUWHMYntqjPOZ+EGdfH/wZ0oe6PrgJpDKeOqHIZAIMrobmLFsH+N+Ky2rBho+CRz6JzYwH9SFINXKxvIISYC1RKKV8SQnzT7ERCiDuBOwHKy8upqqrKvbRdwEX+EG5XGe++9RYAnk98giEPPcT7Hh9HxQfvZumR/+KepjUcevn3nFscXw/g+ePPM9E7kW1rtgEwuHUwK86s4IVlL1DmLDPtb8q+lxjt8PL29mMgLPoUzCAjLMbB0Z3rOdRZhSvYysLOZg40hTlWVcV5wWKKju9mfRfu9SWr7qJ1wAS2n//d7slow4aNHkGvVR8VQjiAXwC3Z2orpVwCLAFVdM7n8+VVNsvYPQRCpcTk8fk4eeYM/O1v3HTbrdziL+FjRQ4eP/04Ty16ilGlo6hpraH6mWrunnc3vpnquMF1g/nXy/+ieEoxvvE+8/4O/hTGzsZ3xVW5kX/DCMYPL2G8z6dm8Cth8rwrmXyuD9qXwpbHyfpeSwlvN1Pk6sj+WBs2bPQK8kkN1QCVuu8V0W0ayoDzgCohxGHgEuD5gnIYZ4KrOKW8xMi7v4F32jSOf+f/4ZKD+XlkKIFwgHveuodgJMjyo8sBuGLs5bDyN/CLmcz8680UScmGpV+GX54HdbtS+4pE4ORWGH1B7uTXJ5VpOQR6ash/Bjqasztn52kIB+C0HXFkw8bZgnwqgnXAVCHERCGEB7gVeF7bKaU8LaUcLqWcIKWcAKwGbpBS9kCN6Rzhiv+n/nRweL2M+dmDRFpaOP5aCxPaW7h3wb1sqtvEbzf9ljePvcmUARWM+8ft8Pr3YfgU3DOu53z3UDaWDVEO2p3Pp/bVdBACrflTBFrV0SFRRTAoqsOzjRzSopA6T0Pnme7LaMOGjbwjb4pAShkCvgS8CuwCnpRS7hBC3C+EuCFf/fYoJl8BEy9P2Vw0bRoj77mHtv0tNK2u59qIl1vKL+WR7Y+w/uQ6rqzeCWdOwC1/hU8+B9f9krnnfoTdkTbaRs+CQytS+zqxWX3mzSI4DEWD4mGvmiLI1mHcpgtHLeTw0/YmtYaDDRs28ptHIKVcKqWcJqWcLKX8z+i2H0gpU6a8UkrfWWUNZMCQj3+MAedXULfWxeFP38kXn3qOczsCSODKCh98aS3M/GCsWujc8rlEZIQtY2bAsbUQaEs84YktKrltxPTcCVk6Ih41pI8YAkUNQfYhpPqyFIUcfvrCV+DJT/W2FDZsFATszOI8QQjB2D8/RfkXP0HIW0HdyqH88O9D+eWxi5lx0yPxmXcUs0fMximcbCgpVXV+jq5OPOGJLVA+E5w5XPe1dLiimwLt8RyC2L4RKmM5W65fP8su5MzkpsNQuyO+crkNG/0YtiLIIxxlgxn65e8y+fU3qfj9QxRPnMrYv6+k/r8fTKktVOIuYfrQ6WwMNIDDnUgPSZl7RzHocgnq1KA9ZEJ8nxAwqCJ7aqi1FoRTWS+FTA211oL/dM+uImfDRoHCVgQ9AOF0UnbllYx79K8M+eQnafrLX2j4/e9T2s0tn8u2xp0EKubBobfiO04fU8Xhcq0ItOzik9sh1JlIDYFSBNkO5m116rwDx3Y9IS3fiIShPUqJNR3sXVls2CgA2IqgByGEoPw732bQTTfR8Nvf0fToown7Lxx5If6wn21jzoXjm+OVQbUs3ZxbBNHCczVR14zeIgDlMM7aR1CnLI3BXTi2p9DeqCqvAjQd6F1ZbNgoANiKoIchHA5G/+h+yt73Pmr/6yc0P/NMbN/80fPxOr284gwCEg6/o3ac2KLolpEzcyuMRg1VRxVBskUweJyiUIJZVP9orYMB5Wr95EKlhvSF9myLwIYNWxH0BoTLxZifPUjpZZdx4vs/oPHhPyOlpMxTxpWVV/JywyYC7pI4PXRii4oWchuvjdBllEQtguOb1KdWdVSDFkJ6pgbLaI1SQ4MqoOUkhALdlzPX0Ec22YrAhg1bEfQWHB4PFb/7LWXvex91Dz7I8W98g0h7OzdMuYHTgdOsGHc+HIw6jE9syT0tBOApAc8AFTlUNjpV0QyqUJ9Wo38iEZWXMGBkNPxUwpkCpIe03InB42xFYMMGtiLoVTiKixn7y18w8u5vcOaVVzl8621cGBzLiOIRPF/khoY9arbeWpsfRQBxeiiZFoLscwk6m1Xoa+lIXWZyASoCzSIYtwAabR+BDRu2IuhlCCEY9rnPUblkCcHaWo5+5FY+Li/mnY4aGh0OePd3qmG+FUGyoxhU5A/COtevce8xi4DCjBxqrVN1okbNUsrLDiG10c9hK4ICwYCFlzHx6adwDhzIpb9azrCmEEsHD4cdzwICRp2Xn45jisDAInC6FWVkdTDXZtpa+CgUpsNY82MMm6y+Nx3qXXls2Ohl2IqggOCprKRyyRKcEbjvaSdveAaBDMOwKWrVsHxACyE1ooYgGgZqcTDXuPcB5eDyqnWfC9Ei0HIdhk5S3+0QUhv9HLYiKDB4J02k4g9/YMiZCB96so3dDneMFtpav5Ubn7uR+969j45QR246TEcNQTSXIEuLIOZ3yOLYnoQW4jp4PCC67jCOhHMqlg0bvQVbERQgSubOYehP7mfKcTi+agiREefz+O7H+fQrn6bZ38wz+57h40s/zqHTOaA0tDWZtdlxMgZXwukaFRGUCa11qjyGvoJpoSqC0hEqSmpQZdcUwcnt8J+joH5v7uWzYaOHYSuCAsXoD9zEqlumM/qQi+1f+RORr/+IL+4Yy9NTf8ofrvo99e313Prirbxy6JXudXTBrfDpF2DgaOP9gypUJFDryczn0rj3aEVVVaKi2poS6SmEQyqzeEC5+j50Ytcih46tUQvw2Gsz2+gDsBVBAWPSv32Zn9ziYPlMyXT/EC577gANH/sMk37/Ck9e9yTThkzjm299k0d3PJr5ZGbwlMLEReb7B0WTzKyEgWrcu4bB49RgqV+joLfR3gBIGBClr4ZO6ppF0LBPfZ46nCvJbFhFwz44tq7rx0cisO1pCAdzJ9NZDlsRFDAur7icC268gwU/f4QLl61kylsrGHr77Zz+5z/x/ONl/nz1n5k/aj6P7nyUiMzTrHv4FPVpZebbWqtyCDQUYi5BLLIpahEMmwwdTfG6TlbREKWEmg/nTDQbFvHqd+Hpz3T9+KOr4JnPwt5uWtN9CLYiKGC4HW6+Me8bzB89X30fOZKR93yTsquvpu5nP6NzxTvcNPUmattr2Vy3OT9CDJ0Ew6bC7pcyt22tT7IItFyCAlqXoFUX2QS6yKEsrYKYRXAkN3LZsI763cr31Hm6a8c37k/8tJFfRSCEuFoIsUcIsV8I8W2D/V8XQuwUQmwVQiwTQpjEMNrQIBwOxvzkvyiaMYPj37ibhZ3jKHIW8fKhl/PX6fQPwOG30y9kry8voUErUZHsMN78f/DU7b0Tv58c2RRTBFnIEmiPL9jTbCuCHkWwMz6xqNvVtXNo4cJ2VnkMeVMEQggn8BBwDXAucJsQ4tykZpuAeVLKWcDTwH/nS56+BEdxMRW/fwhRWkLDV77BJxrPwfG3Zzn2la+w/73v49gXv0T4TA4Xjp9+HURCsO918zYdTSrnQZtpg1oD2TsokRqKhGHZj1Si3O8XwLu/VQ7cnkKbLvsZYMhEsg4h1WaSw6aqa7O55p5D0wEguqhT3c4uniP6rO1EwhjyaRHMB/ZLKQ9KKQPAE8CN+gZSyuVSyvbo19VARR7l6VNwjxpF5e9+R6iujvf9YQMfXNbG6R1b8E6bRutbb3Hk4x8nePx4bjobe6Ea4He/aN5GKy+hzbQ1DK5MTCrbvwxajsPVP4VJPnjte/Cnq+DkttzImgmtdarQnqdUfXcXqSzobBSB5h+Y+l61rkEh+UD6Ohp04bpdtQgaNUVgFxzUkE9FMBbQcwLV0W1m+CyQR36j76H4gguY+K/nGPXnJXzhnoE89SMflQ/9jnH/u4TgyVoOf/RWOnd2cdakh8MB51wL+9+AkN+4TbITVkNyLsHGvyplMe8OuO1xuOUvcOY4PPx+VbY632hNimyC7ENIG/cDAiZfqb7b9FDPQfPNlJ8PtV14t6VUCsDhUhOSQHvmY/oBXL0tAIAQ4hPAPGCxyf47gTsBysvLqaqq6jnhzhJMHDiTlw+8zOX+y3EJF86v/QdDfvcQB277GGduvx3/7Avi8f1dwNBAJbMCrWz91+9oGnZhyv6RtSs4F1iz8xAdh+NUydRWQXnDId6pqsLjP8Ule16muuIGDr7zbrTFEIrOu5/5a7/Iice/xr5pn++yjFZwQfUeHBEvm3Tv0DR/CcMbtvKuxfdqxs53GFhUzpb9TVwC7Fn9GicKyB/elzFj59sM8o6kyTGaETWrWLl8eVbvtcffyKWhDpoHzWTw6R2se+1J2gZMyJ/AZwnyqQhqgErd94rotgQIId4DfBdYLKU0nG5KKZcASwDmzZsnfT5fzoU92+GsdvKFZV/ANcWFr9IHQPA976H63+/C8T//Q/Hs2Qz/wl2UXn45oisKIbQA9vyCWZ6j4PtG6v53t8EuuPjK65VvQIN7Cxxfiu+SObD+EZBhxt34XcYNn5p0/jWM3fR3xt7ygHEBPD0iYVj9e5h1azwfwCp2BGH4NBLeIfcWeP1VJaNedjPs/h5UzuKS930Y1n2Bc0Z6Ocd+J3sGe34AFeczZtp74OXX8M2bAWWjrB9/+B1YBYPn3QzLdnDR5GEww5c3cWNorYNdLyhLuBsTsnwhn9TQOmCqEGKiEMID3Ao8r28ghJgD/A9wg5SygLKOzj5cMuYSBnkHJUQPuUeOZPwTjzPq3h8QrKvl2J2f5/DNt9DSFYvK5VWc+O6lxpnCrXXg9IJ3YOL2Qbpy1BsfhXGXQrISAFj0TRAOWGEhXuDoauVbePJT2TtqW2sNqKEsQkgjEWjYD8OngdOlIqNsaqhnEIkoamj4NBg5Q23L1mGsPeMp71GfPRU5tP7P8NLX49RWgSFvikBKGQK+BLwK7AKelFLuEELcL4S4IdrsQWAA8JQQYrMQ4nmT09nIALfDzXvHv5flx5YnFKRzeDwMue02przyCqN//CPCLS1U//td1D74IDKcZdG06depqBttsXs9tEJuybMdTRFs/YeK+Jj7KeNzDxoLF30Otvxf5vo9WnLb0Xfhjfusyx8KqMSxZD9GNorgTDWEOuLKbPB4O5egp9ByHILt6t6PjAYgZuswbjqo6mGNPFct1dpTDmPtnT25tWf6yxJ5zSOQUi6VUk6TUk6WUv5ndNsPpJTPR/9/j5SyXEo5O/p3Q/oz2kiHayZcQ0eog7eq30rZJzweBt98M5NfepEhH7uNpof/zLG77koJM5VScqDZZJY09b3qR2QUPdRWZ0zTaElla/9XWQvn3pjaRsPCr6kFY6r+y7wNqB9V2WiY/3lY9TvY/s/07WMyRpPJkiObhkxUn1YGBS1qZfi06LET7DITPQX9vS8drrLYs3UYNx5Q1KPT1fXyIl2BpghObO6Z/rKEnVnch3Bh+YWMKB7B9975Hov/sZgrnryCK5+8ko+88BF+ueGXrDu5jpADRv3gB4y67z7a3l3F4Y/eiv9QPJ76qb1P8cF/fZCVNStTOygaBBMvh10vqugLPTSLIBmlI8HpUbPo829R6ySbYcAIuOQulWNwIs3MSVvD+X0/hor58K8vQd3uDHcHXQ5BkpyeEigbEw8rTAfNtI8pgvGqfpG/NfOxNrqH5Hs/ckYXqKFDMDS6INGwyT2jCFrr4UzUPVqgRQptRdCH4HQ4uXfBvdw45UbeM+49+Cp9LKpYRKm7lEd3PModr97B5f+4nPvevY+BH7mZ8Y/8mXBzM4c/fDO1D/yUlmMH+eOWPwLw5+1/Nu7knGsVxdOQRN9opZ2T4XDEM4zNaCE9Lv2yUjjL/9N4f6BdreU8+gJweeAjf1UD+T8+AZ0Zkuj0S2kmw+rssGGvKrNdMkx91xb06UoZjZaTfW+ZzEgk8yy9blfX1nJo2KsSFLXnVz5TlZuwWt1WCx3VqMChk9QAHczB2h5tjSoM2ggno4P/sClKESRPosxQv6fHkhVtRdDHsLhyMd+75Ht8f8H3uXfBvdx36X08cvUjvH3r2/z6il9zZeWVPLPvGR7b9RglF13ExKefYsAVV9D0t79x7P3X8bHHT3Ib81l7ci3bG7andjD9A4BIpGMiYTUrNrIIAEbMUElpY2ZnvoDiwbDgy6ogmNHAXLtDJXFpazgPHAM3P6KUU9VP0p87nSIYPsXaoKI5KzVfiLagT1foob/fDP/6YvbHFTJ2/BP+sMC8OujJ7SqjfM0fsz93w17lH9Du/cgZymdgtfBfay0E2xIVAXSP2otEVDTcb2bDI9cYD/KaFTDnE6o+kpXggvq98PtLYN2fui5bFrAVQT/BAM8Arhx3Jf+58D/xVfj4zabfcOTMEdxjxzL25z9j5EvP8NrFHuYdcnDTT97lh4/Da//4KTL5xR44RmUEb34sPqtrb1SDs9EAC/Ch/4FPPmtd2JkfVJ8HV6Tu02ZXo2bFt028HC74GKx7WC2iY4ZYnSEDOcctUAvZ1xooPz20wUiDpgiyjRxqb4LabXBkZWGt19BdHH5HfW78i/H+jX8FJGz4i/WZsQZNCWvI1mGsTSyGJSmCrkYONeyDv14HL/6HylQ/ddh48nJiq/JDaeXe09GeGjY9qn5T+5d1TbYsYSuCfgYhBN9f8H08Tg8/WPmDWPnqvzW+zJ99EYpe+Bsj77mHiWe8XPPb9ey9/gOcfuEFZFBnos79lMoWPlilvusXrTeCt8xafL6GYVOUM/iQgSI4sQWKh8bpJg2+b6kfzltpwk/b6hW14C5K3Tcxmsto1KeGjmZ1rfrBqGQYuEuzjxyq2aA+O09Do0lI4dE1sGZJduftbVRHLYHt/0yl6oIdKnqsZJhSqMfWWD9v5xloOZGohEdMV59WHcbaIB2zCLIIEkjGpsfgD5epicMNv1OLO0H8N6HHiS0wehaMnAnCmdlPEArA5sfV/0feVd/zDFsR9EOMLBnJPRfdw8a6jTy++3Hq2+t5bNdjXDvpWqZXzmHYHZ+h4pUX+OP1bk61N3H8m/ew/6r30PDHPxJqalL0UPFQlRcAujpDxoqgNdDKaX8WJYOFUAPzobdSZ8uaozg5THXwOJj3Gdj0d/MfdmuteQLawNFqgDeyQjRoxeb0ikAI5TDO1iI4ttb4fz1W/BRevideOrvQ4W9Rztsp71GUzY6kaK5dLyjFd+NDqt6T9v5YgdG99w5QPhqrDuPGA6q0hLbYUvEQ9R5nqwikhDd/DKPOgy+ug7mfVJOXgWNTJxIdzXDqkHpn3UWKzsqkCPa+oqjWOZ9UVNbxjdnJ1wXYiqCf4sbJN7Jw7EJ+vfHX3L/6fkKREF+8IM5Xjxg4miE3fYgv3R5g4G/+G+/UqdT/6tfs913B8Xvvx1/+AbVGQVtDWu79tP80N79wMx9f+nEC4SxmNpMWK8pJ/yMPBdTsT/MPJOPyu1V4a9UDxvtb6839GKBM9yPvmjvokkNHNXQlhLR6raqXUzRY/Z+MUEAtoIKEvWdJCa6aDcoqu+QuRdskD/QbH1UUydT3w3kfVtFhVtcUSI4Y0lA+07oiaDqoFIdTV1ChK5FDjQdUTsOcT0BZ9H2KTV7eTpy8aMUUtXd29AUqhDQdLbbxURXF9p77AJF+cpIj2Iqgn0IIwb0L7sUpnFQdq+LD0z5M5cDKhDafnvlpAjLEM8MPMe7hPzHppRcZ9KGbOLP0ZQ4+sIxjKwbQ8uwvue/QM/xrQGmKIghHwnzrrW9xsu0kR84c4bFdj1kXUONT9TOs+l1q/WQzRVBWDhffCVufNOaNW2uNI5tifS5WMzCNtklGw16laLRIIQ1aUplVzjsShur1UDkfKi4ydqzWrFezaoS1RYF6Cu/8Epb4jK9Vu46x8xR9WLNBOfdBDZ6H31azZ4cD5n5aXd/2Z6z127BXzeY1OkfDyBnKWjArhqiHPmJIg1G0WMgPv5uvnMBGOFSlPjU6UcPERaocu97PpM3+R+kUQVu9eYHF09WquOOcT6hcidGzlGWcZ9iKoB9jVOkovnfJ95gyeAqfn5Va7G3ioIlcOe5KntjzBG3BNryTJzP6vvuY8uYyhn/hC7Q3lFD9438y4392srxmCI+9+AvCHfFQvIc2P8TK4yv53iXfY1HFIv5n6//Q0NFgTbhBFSreW/8j0H5UZooA4LL/UD4Jo/DTNpNcBw0TFpJ2BtawT80g9TNKUNRQsE1ZMFZQtwsCrVB5sVIG9btSF/059JYquXHBrXBgeWHkKYSDsOr3cHyT+ktG9VrF2xcPhlkfVfkjG/+m9m36m+LHL/iY+j52ruLMrdJDDXuV5eV0J24fea5aKyPTamPJoaMahk5Sg2+wM75t94sqRHnT34zPdXAFDKxIPdckzc+U9M4OHBunJLV314we2hSdLM35hPqcuFjd1zxXSbUVQT/HByZ9gGdvfJYRJcYz5TvOu4OWQAtfW/412oPqZXQNGcKIr3yZwz/5AH+7wsHUesnnX44w97tPsGfuhRz4wHWsvfcrPLlqCR+e+mFunnYzd8+7G3/Iz+82/c66cBMXweGV8YVrTmxR2clDJpofUzIUFnxJ8dE1Om412KloiHRF6kqGpp+BJUcMaTALIT3yrnFug0YFVV6kLAJILdtxcIUaNGZ/HMJ+OPCmudxmkFIdpx/kuoO9r8aT8pKtFCmVo1i7npKhMON62PqEUmKb/w+mvV/5YkBRKXM/pRSKlSia5IghDVrkUCaHcVu9Ur7DJiduHzoZkIk+Hk051WxIzQ2IRJRlM2lxqp9q4Bi1WJHeitV8WhrKzwOEsSKIhJXymbQ4Xnhx0mIIazRh/mArAhtpMWvELO6/9H7WnFzDXW/cRUugBYCDzQf5/qkVHJsT5rzrjjPxc+Vs+MpVPHMp7PM2U/rk6/z+DxH+7XUI1tQwcdBEbp1+K//c9092NVoM95u0GAItcWfZiS0qbNSR4bW95C7lBHzzx/FtWnmJqEUgpUwNjQXzGVg4qGaURoORRhXpFUHNBhVXvtygXMaxdarOzZCJKr8CkUgPBdrUoDpxkQprLR7SNXpo76vwt5tghYnPxAJCkRBBzWey8VEYMEoVDkyWp3G/quNUOT++be6n1LYXvqJoueSEwlkfUYUKzWbeGsIhlSdipISHTVGUUSY/QXLEkIbkENJTh1Xkz3kfVt/3LE1sX7tNXZNGXSZD72cKtKnJg14ReAfEE8uScbBKRePp79O4BYqOzDM9ZCsCGxlx09Sb+O9F/83W+q187rXPUdNaw9eqvkaxq5ifjVyER0BRxVg+8YXfUfG1b/LNG09z75eGUHrdtZx5+p/sf//VHLr5Fj7043f49ZIIzdffxqGbb6Hx4YcJHD/OjsYdLDu6jI5QB+HWNlrffofm554jMuZiAJr3v8rrh15h6Zl9LB08nKUHl/L6kddjFkoKigaqukUHlqkfJSRENkkp+e473+WuZXfFBzkNE01mYKeOKArCUBFEo1D0s0pNCW19InVGXr1WDZhCKFlHnpvoMD66SvlCJi5WNNS0a1QkSTZZppFIXIY1/wMttdaPjUJKyVeXf5Xrn7ue+rptsP91mPNxVS+qfldi/L0WClqhUwQTFikluf0ZpUCmvDexg5jV8I/02b3NR9QzMbr3Lo/ankkRaLKmKIKkENJNfwcEvPd+NWAnKzxtQE72D2iYtFhZHjUbVfIcMpXKHH2BsSLY9Del9KdfF9/mKVVWVrqw5hygIBamsVH4eP+E91PsKubrVV/n+mevJyzDLHnvEsrDTtj8j5ij+DPnfYZzhp7DiOIRTB4yleBXT9D010fx79+Py+ulaKSXLc07cXY20Pngz6h78GfsqYBDowSdxx1MOBnBEVEz9SMTRvOvRRN49siThI4+CcMGwpmN8LayEEaWjOQbF36DayZek7rGwvx/g1UPwbL74TMvJ6xV/PqR13nhoIr7/sWGX/Ct+d+KHzd+gZphHnoLplwV365VjTSalXoHqBm+lktweKWiZKZdrQbw3S/C+Terfe1NavY8++Px4ysvgu3PqsHb4VB9O9ww7hK1f/oHVFXWI+/GeehM2Pmcmr36vqNKe7/zC7jmp5mPe/sXiuOfdwdv17wdK2D4hWVf5BEkA+Z8Qt2fV76lBsnLvqKOO7ZW5YroB2uHQ4VALv8xzP5Yqm8FlPN4+9Pw56uVb0fDlPfAgi8qn4BZxJCGkTPgyCplORj1AWqgF8640tZQMlQNvk0Ho9TMY6rvQRXqvq96SPlviger9gdXKDk0iisZEy4HhBq4i6LHGCmC7U+riLvS4WrbmeOqhtf8f1Ml3/WYuEjlx3ScUrLmAbZFYMMyFlUs4g/v+QOl7lK+fuHXuXj0xYrauOSLMPOmWLtLx1zK1CFqwHSPHk35t7/FuD/9L5UP/Y6LljzBq588h8/e0sDXv1DEmusmMUmM5JotTgaUDubFS9386FYHP7vZRWvDCT709wD3Levk75W38Hz1cZ6//Fc8/8HnWfLeJQwrGsa33v4Wt79yeyrd5C6Gxd9Us+v9y2JJby3eUh5Y+wAzhs7gtum38fddf+eVw6/EjzOagbU1wGvfV4PIyJnGN0cLIZUS3vyRmgHf/Gc1I9Y7RLWEKz2FUjEf/KeVgxLUYFM5P76u8uQrVVVWq/RQOKQoqREz1DoPcz6u6uHr1442wsZHYdkP4cWvEdr9Ej9f/3PGDxzPb32/Zp//FF+fMJXgoEp1H0adnyhP9ToVLZRM2837DMz8EMy/07jPCYuUA9ldogbiSBj8Z+CNe1V0Us3GeNjusCnG55hxvQrnfPNH5tfWdFDJnexshmjk0IH4etoaNXPOB5QVuP8N9T0UUMrYjBYCpVhGna+e4YktKkqtLElpJDuMw0F46jNKtos+l3rOSYtVWO5hg0KQOYJtEdjICheNuogVH12BQ0R/8ELA1RnKRuvgcrj4zZW/YW/TXi4Zcwml7lLF1UcinOt0clGwnWVHl7GzcSfOT17I4F/+hWlVG/Ae/AfFwwfiHnMUd0WE0RUVPHbln3m+5lV+s+k3fPTFj3LdpOv4/KzPM6bNTcf27XhGX4R3UCXizR9FayTBr/f+g8bORn575W+ZNmQaOxt3cu/KezlnyDlMHBSlCSYujs/AvAPh6TuUj+GzrxlnJYNy7tVsUIPG0VVw7c/UQK7NiJsOKRri2Fo1Mx0zJ36sphSOrVWrbZ3YAr5vx/d7SpQy2P2SmtVnWuFq6z9UtvJH/w4OJyy6B7Y8oa7pht8mNH2r+i0qyyqZ2NYML90dDYFs5plXv8rBwcX86opf4Qs5uLehkR+MGMZ9q+7jx5f9GDH9OpWv0VqnZrB1u4xLjJcOh1tMwjBBKY6b/pC6fdeLsPRu+NNVKqa+dIQaZI0w8yZlRa38lZqYnGtQzb7pQIwWklKyvWE7M4bNwOWIlqM+tkaVvygZriw5gIp5KklSs+iOb1TRYRMX09DRgEM4GFpkINOkxYqOaz1pnPw4Oloe5cQWZXW+9n04tho+/HCqMxuUgnWXqGuccV3q/hzAVgQ2skZMCXQRlWWVVJbFcxaEEOB0AlDiLuH6yddz/eTr1c5fX0zLl6ZRv72E0weLifxUV0JCCC6orOQvk85hZ0kzjc8+T3X1s7TrIi09o4YycNgBBh7/G7sZxslnnuSnrZMoeuIejnk8/PjqK/iS8zBfr/o6j137GC6Hi53DKthcVsrp5d/lPR0wdefbcMX9EBmBW0rjpT4Hj4ed/1JU1OBxKk4eFCVS9V+Ke77q+8oXMOq8+GwfOOr28PLwcl7d+Qe8h57ga0Ue5idz0NOvhT0vETi+gc2OCKtPrGZVzbs0nTrOZy/+ErdMu0XJFfKrAXrMnDjXPLgS5n0W1i5R4bXDJuMP+/nJmp/wzL5nGOAq5Ven2ri4dDjc/AitbXX8/sVbmBd2cmX5JfDCl7kp5Obk+Xfy+21LcAonCwePo8LjonLnc5QNmwLIRCunu5hxnaoh9cZ9yppJNwsHuPoBNbA+9wVFFekpPCmVIq6YT3uwne+t/B6vH3mdc4acw/cu+R6zh06CbU8reuaSu8DlIRAO4Ha4Eedco3wcIT8cXEGbcPBwyy4efeZ+vE4v9196P1eNvypRlomL2bhhCTuCtSwYtpjJye9M8RD1vpzcqvpd8we4+K44fZgMl0c5jfPoJxCGkRMFjHnz5sn16w1WyLLRd/Gn90L1WuS8zxJZ+AMCNTUEjx3Df+AA/n378e/fR+DwERzlIzg6rphlA6vZP0ry3tA05u8IULRlH+hec8fAgZTMmUO4uZmOLVuQDgcbJ0lOThhEaX0L5Y1hRjfBYANfdGTMCJrnTeXQ+cM5NKGYqSOmM7d8LlMOvIvjxa8CELjhtxyouJCD9bsJ+Ttxr3kE96kaXJd/h9BL9xEsnUa4dCqyoYlj7cfZK+poLoVRxQ6Ol3qpD3dyafl8PjXjU4wZMYm24QNYcWgpb751H6tLB0BniMXbBddvcTGitpNV0wX7b5rLV27+GaN2vKBm0p/4Z6KPo7UOfn0BnHMtx6/+EV+v+jo7GnfwqRmf5N1d/+Cw9PNf593FNfO+yK82/IqHtz/MEzUnmTn5/Sr66MLPIK/5KT9Z+xMe3/14wj0pFW5Kgx0UD55IiWcAQ4uGctv021hUsahL62PvatzF8TZd2GbTYcIuL+HSYYQiIUKREBKJy+HCJVy4HC5Gl47mPPdgxJLFynr43DLluwFF7T04maNXfoevnlrDwdMHuW36bbxx5A1q22u5edgc/mPjC4SA5R/4MW+c2saaE2sY7B3MpQMmsGDHK8y/9rdUrf0ND4lmmoTkmonXcPTMUXY07uCj53yUu+fdTZGriCNnjvDLdQ+yrDo+aFeWVeKr9HFl5ZXMLZ+rJlL/+KSimYLtMGoW8tMvUOc/xciSkcb3bOWvaVh2H+4vrmHQMBNfSQYIITZIKecZ7rMVgY2Cx5s/hrceVLSGyZoGMhJBRPnpuvY6/rrjr7x08CUaOxsZ3e7m6m1tHPO4+OCHvsOihR+LtfUfPMjpZ5/lxNNP4DnVSuegYsS4sQxy1uGO1LB32Ai2DCtnX+cxSgIw+4Dk/CMSTwgCLvC7QAoQCFwygiMscIbBlcOCop1uODoC6kY6GBuSjN/rwBEIUzR+OMWTR9Kwcg/CH2b9DCcjzw/hG16J8/olRDo7ibS3E6qvJ3jyJME1z3LiyHbeGF3CzvEOPjn7cq4M+jm94ym+OuMSNrdU84XyW3hj27NcNvwiPusvQm56Eoc7QtHXX8A5VTmvWwOt1LTWcOytn1B9uIraQaMItHcyJDSLETtOUHqknrriAJSP4ILzrmLqOQtwlJbgKCpCeItwFBfhHDgQ56BBCI8ndp0batby6KqH2H9oA84IhBwQcqq/gAs6vOoTIUBKBnTAiDMw/LTEFYYBM2Zyy0wflyz9LmKSL+q4BVpO8s6Wv3JP5XgcLg8PLnqQBWMW0BZo4+G3fsGqVU9S0Rim1SM4VQpFI0ZxwbRFHHe2sOrkak4H4mUw5nqG8c2rfsM5kZEEWs7wl/2P8djhZxg3fArnjb2QZ/b/E4/Tw2cDLq6p2c2qa3/Em01bWXtiLcFIkNGlo7lu0nVc11TPpHd+w76B5bx+wcfYvb6KomP1yOmT+eAN3+TSyoUxhVDfXs+f1/yUpw6/zG2jLuMb13StEGGvKQIhxNXArwEn8Ccp5QNJ+73Ao8CFQCPwUSnl4XTntBVBP8SJLSoe/nNvpIb/pUEoEmJ97XpePriU1/c9x4LiMfz8o68YtpXhMLKzE0dplLLZ+ChU/RTueBkGj+NYyzE2121mZMlIxjiHMmDLIfwbNtLador6jnrqW09wqvkwnrLhDBpSyZBBoxg2aBSuohKkQxJZ/TvCshO3M0DJrb+jbOpFlIwYBUC4oYHQltcIPfMtIiGBuOAjnJ68kJcOv0xzfTVzzgyhsi6Mc89BpL+DgeM6GDKljeJhKpw07Bcc2jeQ1j0leIPmM/CAG5pLYPgZiUMKhENSNCyAGDSGQJubwMmTONIMB+5x4yg+bybuikoinR1Eag8hd71GsM1FxykvRCSiuBjvjOmcrj2GqGvEE0w/voSK3MiyUoLtrZS0hdK2BcDpQJSWqmq4HamJcn4XNIxyM8DbTqTTAe0OitqgOCAIugSeQUNwDxqMKPISrK4hkrRUqx7C7cY1qpyAo4kG92nKApKBjCdY24T0G5e0CHldeMoG4XJFcETO4JhwIY7SUiLFXk5GTnGk5Sgn22uJIBkUlIw5CRUNiVE7p0vgyHnDqXz/TWylmmXVbxIUERb6/dwy7UrGf9TAp2IBvaIIhBBOYC/wXqAaWAfcJqXcqWvzBWCWlPLfhRC3AjdJKT+a7ry2IrDRFUQiYYRwZEdVSJnZMWu1/ev3Kmdm6Qi4e19qO38rPFCpokPuXGG4iI+UEjrbEC5j116gqZENTy9h3+kD7GjbT71sIeCGUwME/qGlzJ68kEVjL+eqwRcht+6kbf0G2jdsRDicuMdV4qqoYLU4xICRY7hkwmIcXg/C6yXU2Ejnjp107thB5/btBGtrcRQX4ygpwdF5EqcnSMnCKyn90J2UzJ4dm+X7Q37+teFvvLPpX5SGnAwVAxhEMWURD21NtbQ2nCTc3ExpRwRKipky8ULOn7qQ4uHlCI8bGQgiQyFkIID0dxJubSXS2kaktRWcDtxjxkT/xiJcTlp3bGfn6pdo3raRsuYAZ0oFnYNLcIwYStnwsZw/YCqOtg7CZ1qIdLTjHjMG76TJeCZPwjN+PNLvJ9TQSLixgVBDI8Hak4ROnCR4YDuhYwcRbvDMvQrPBNXeMWAA0t9JpKOT9tZTdLaepizoJNLeRqStjXCb+oy0tavPjnaQEAmHCIT9BESE0NRxjL5oEcPmLcA7aRIt69ex54W/41m7nZLO1LF52L99jpHf+Ib1d1KH3lIEC4D7pJTvj37/DoCU8ie6Nq9G26wSQriAk8AImUYoWxHYOCvReAB+O1eFJN72f8Zt/rBQZZbecyhz9nQGSCnZc2oPG2s3MnXIVGaPnI3bYRA62V08e5fKcfjiOhiRPXftD/upbqmmoqwCr9Ob+QALCIaDHDlzhIqyCopcJlFe2aDjFDw4RYX53tEzlWA7O1tZu/xxxjuHM9QzBMJhZCiMZ8IEis7JvY8gn1FDYwF94HI1cLFZGyllSAhxGhgGJFQmE0LcCdwJUF5eTlVVVZ5EtmEjf6iYfAdnSqZxxuT9HTbiBlxD2qh9K3flBMYwhrbaNlbuzk8MeonnUkaOD3F4ew0IkzV7LeAYGXIcuoBqqnN2rjGTP0t7yViae3LsKZ7KIeAQqKg6pxNOHFd/OcZZET4qpVwCLAFlEfh8vt4VyIaNLsFnaf+MfIuRc3ySCb0tQt7h620B8op8ZhbXAPoC9xXRbYZtotTQIJTT2IYNGzZs9BDyqQjWAVOFEBOFEB7gVuD5pDbPA9HMG24G3kznH7Bhw4YNG7lH3qihKOf/JeBVVPjon6WUO4QQ9wPrpZTPAw8DfxNC7AeaUMrChg0bNmz0IPLqI5BSLgWWJm37ge7/TuCWfMpgw4YNGzbSw64+asOGDRv9HLYisGHDho1+DlsR2LBhw0Y/h60IbNiwYaOf46yrPiqEqAeOZGxojOEkZS0XKM4GOW0ZcwNbxtzAljEzxkspRxjtOOsUQXcghFhvVmujkHA2yGnLmBvYMuYGtozdg00N2bBhw0Y/h60IbNiwYaOfo78pgq4t7dPzOBvktGXMDWwZcwNbxm6gX/kIbNiwYcNGKvqbRWDDhg0bNpJgKwIbNmzY6OfoN4pACHG1EGKPEGK/EOLbvS0PgBDiz0KIOiHEdt22oUKI14UQ+6KfQ3pZxkohxHIhxE4hxA4hxFcLTU4hRJEQYq0QYktUxh9Gt08UQqyJPvN/RMuh9yqEEE4hxCYhxIsFLONhIcQ2IcRmIcT66LaCed5ReQYLIZ4WQuwWQuwSQiwoJBmFEOdE75/2d0YI8R+FJKMe/UIRCCGcwEPANcC5wG1CiHN7VyoA/gJcnbTt28AyKeVUYFn0e28iBHxDSnkucAnwxei9KyQ5/cCVUsoLgNnA1UKIS4CfAr+UUk4BTgGf7T0RY/gqsEv3vRBlBLhCSjlbF/deSM8b4NfAK1LK6cAFqHtaMDJKKfdE799s4EKgHXi2kGRMgJSyz/8BC4BXdd+/A3ynt+WKyjIB2K77vgcYHf1/NLCnt2VMkvdfwHsLVU6gBNiIWh+7AXAZvQO9JFsF6sd/JfAiIApNxqgch4HhSdsK5nmjVjI8RDTYpRBlTJLrfcDKQpaxX1gEwFhIWB27OrqtEFEupTwR/f8kUN6bwughhJgAzAHWUGByRimXzUAd8DpwAGiWUoaiTQrhmf8KuAeIRL8Po/BkBJDAa0KIDUKIO6PbCul5TwTqgUeiNNufhBClFJaMetwKPB79vyBl7C+K4KyEVNOGgojvFUIMAJ4B/kNKeUa/rxDklFKGpTLDK4D5wPTelCcZQojrgDop5YbelsUCFkop56Ko1C8KIRbpdxbA83YBc4E/SCnnAG0kUSwFICMAUZ/PDcBTyfsKRUboP4qgBqjUfa+IbitE1AohRgNEP+t6WR6EEG6UEnhMSvnP6OaCkxNAStkMLEfRLIOFENoqfL39zC8DbhBCHAaeQNFDv6awZARASlkT/axD8drzKaznXQ1USynXRL8/jVIMhSSjhmuAjVLK2uj3QpSx3yiCdcDUaISGB2WqPd/LMpnheeDT0f8/jeLkew1CCIFaW3qXlPIXul0FI6cQYoQQYnD0/2KUD2MXSiHcHG3WqzJKKb8jpayQUk5AvX9vSik/TgHJCCCEKBVClGn/o/jt7RTQ85ZSngSOCSHOiW66CthJAcmow23EaSEoTBn7h7M46pi5FtiL4o6/29vyRGV6HDgBBFGznM+ieONlwD7gDWBoL8u4EGW+bgU2R/+uLSQ5gVnApqiM24EfRLdPAtYC+1Gmube3n3lULh/wYiHKGJVnS/Rvh/ZbKaTnHZVnNrA++syfA4YUoIylQCMwSLetoGTU/uwSEzZs2LDRz9FfqCEbNmzYsGECWxHYsGHDRj+HrQhs2LBho5/DVgQ2bNiw0c9hKwIbNmzY6OewFYGNsxpCiHejnxOEEB/L8bn/n1FfhQ4hhE8IcWlvy2Hj7IGtCGyc1ZBSagPeBCArRaDL6DVDgiLQ9VXo8AFni6w2CgC2IrBxVkMI0Rr99wHg8mjt969Fi9A9KIRYJ4TYKoT4fLS9TwjxthDieVQ2KkKI56IF1nZoRdaEEA8AxdHzPabvSyg8KITYHq3b/1Hduat0dfIfi2ZmJ8s8RQjxhlDrJ2wUQkzOcM4Xdcf+Tghxe/T/w0KIH0bPsU0IMT1aGPDfga9FZb8893fdRl9DphmRDRtnC74N3C2lvA4gOqCfllJeJITwAiuFEK9F284FzpNSHop+v0NK2RQtT7FOCPGMlPLbQogvSVXILhkfQmW2XgAMjx7zVnTfHGAmcBxYiaox9E7S8Y8BD0gpnxVCFKEmZOnOmQ4NUsq5QogvRK//c0KIPwKtUsqfWTjehg3bIrDRZ/E+4FPR0tRrUKn9U6P71uqUAMBXhBBbgNWo4oRTSY+FwONSVTytBVYAF+nOXS2ljKDKcUzQHxit4zNWSvksgJSyU0rZnuGc6aAVAdyQ3JcNG1ZhWwQ2+ioE8GUp5asJG4XwocoW67+/B1ggpWwXQlQBRd3o16/7P0z3f2MhEidsybJp/eWiLxv9FLZFYKOvoAUo031/FbgrWkIbIcS0aDXNZAwCTkWVwHTUcpwagtrxSXgb+GjUDzECWIQqHJcRUsoWoFoI8cGoXF4hREmacx4Bzo22G4yqtJkJyffCho20sBWBjb6CrUA46oD9GvAnlDN4oxBiO/A/GM+YXwFcQohdKIfzat2+JcBWzVmsw7PR/rYAbwL3SFUa2So+iaKjtgLvAqPMzimlPAY8iaqq+iSqymomvADcZDuLbViFXX3Uhg0bNvo5bIvAhg0bNvo5bEVgw4YNG/0ctiKwYcOGjX4OWxHYsGHDRj+HrQhs2LBho5/DVgQ2bNiw0c9hKwIbNmzY6Of4/x41Lfbd3PwUAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels = [str(x) for x in shots]\n",
    "for i in range(len(shots)):\n",
    "    plt.plot(range(iteration),cost_shot[i], label = labels[i])\n",
    "plt.xlabel('iteration count') \n",
    "plt.ylabel('cost') \n",
    "plt.grid(which = 'both', axis = 'y')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's seen that the rate of convergence does depend on the number of measurements being done per iteration. But after a  threshold, it does not have significant effect on convergence as the curves for 100 and 1000 measurements are almost similar. With less measurement counts curves do deviate a lot and so its not smooth. Do note that with single measurements being done the cost has only two levels: '1.5' and '0.5'. It can be explained by the fact that if a single measurement is done, the outcome being $\\vert01\\rangle$ or $\\vert10\\rangle$ the other 3 probabilities is directly zero and so the cost function is 0.5 or in the other case it is 1.5.\n",
    "\n",
    "At the end of optimization for each measurement rounds the values of variational parameters when fed in the variational circuit should give us equal probability for $\\vert01\\rangle$ or $\\vert10\\rangle$. Lets check the same by plotting histograms. Here 1024 measurements is being done for better comparison, as the optimization is already done with varying measurements."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 measurements per iteration used for circuit optimization:\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10 measurements per iteration used for circuit optimization:\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100 measurements per iteration used for circuit optimization:\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1000 measurements per iteration used for circuit optimization:\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_graph(params):\n",
    "    prob = circuit(params)\n",
    "    result = {'00': prob[0], '01': prob[1], '10': prob[2], '11': prob[3] }\n",
    "    return plot_histogram(result, figsize=(6, 4))\n",
    "\n",
    "images = []\n",
    "for i in range(len(shots)):\n",
    "    images.append(plot_graph(final_params[i]))\n",
    "    print(shots[i], \"measurements per iteration used for circuit optimization:\")\n",
    "    display(images[i])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparing different optimizers\n",
    "\n",
    "The optimization done above was using basic gradient descent. There are many more advanced optimization algorithm that can make the variational parameters converge faster. Lets now compare the convergence of some different optimizers. For this the number of shots per measurements will be kept fixed.\n",
    "\n",
    "1. Gradient descent optimizer: The basic gradient optimizer is the most fundamental optimizer where parameter is updated by the product of stepsize and gradient at the point.\n",
    "2. Momentum gradient descent optimizer: The momentum gradient descent optimizer also considers the gradient of previous iteration and improves the convergence of basic gradient descent.\n",
    "3. Adaptive gradient descent optimizer: The adagrad optmizers update the individual parameters by taking the partial derivative separately."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Basic GD optimizer\n",
      "100 measurements per iteration:\n",
      "The optimal parameter is  [ 1.58267949 -2.91992345]\n",
      "The final cost is  0.005\n",
      "\n",
      "Momentum GD optimizer\n",
      "100 measurements per iteration:\n",
      "The optimal parameter is  [ 1.62544517 -3.1779949 ]\n",
      "The final cost is  0.0474\n",
      "\n",
      "Adagrad optimizer\n",
      "100 measurements per iteration:\n",
      "The optimal parameter is  [ 1.53582026 -2.79065513]\n",
      "The final cost is  0.0254\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#Lets call the above function with a list of measurements to be taken per iteration and iterations per optimization\n",
    "shots = 100\n",
    "iteration = 75\n",
    "in_param = np.random.randn(2)\n",
    "#Store the lists of cost convergence for each number of shots\n",
    "cost_opt = []\n",
    "#Store the final parameters value after the optimization\n",
    "final_opt = []\n",
    "optimizers = []\n",
    "\n",
    "optimizers.append(qml.GradientDescentOptimizer(stepsize=0.2))\n",
    "optimizers.append(qml.MomentumOptimizer(stepsize=0.2, momentum = 0.8))\n",
    "optimizers.append(qml.AdagradOptimizer(stepsize=0.2, eps=1e-08))\n",
    "\n",
    "labels = [\"Basic GD\", \"Momentum GD\", \"Adagrad\"]\n",
    "\n",
    "for i in range(len(optimizers)):\n",
    "    print(labels[i], \"optimizer\")\n",
    "    final_param, costs = circuit_opt(shots, iteration,in_param, optimizers[i])\n",
    "    final_opt.append(final_param)\n",
    "    cost_opt.append(costs)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range(len(labels)):\n",
    "    plt.plot(range(iteration),cost_opt[i], label = labels[i])\n",
    "plt.xlabel('iteration count') \n",
    "plt.ylabel('cost') \n",
    "plt.grid(axis = 'y')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is seen that the momentum optimizers converged the fastest for the given problem. Though for longer iteration count there does not seem significant difference."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizing cost function\n",
    "\n",
    "Let's go back to the final state vector we derived previously and see how many solution does it have. \n",
    "\n",
    "$$\\begin{bmatrix}  cos(\\theta/2)cos(\\phi/2) \\\\ cos(\\theta/2)sin(\\phi/2) \\\\ sin(\\theta/2)sin(\\phi/2) \\\\sin(\\theta/2)cos(\\phi/2) \\end{bmatrix} = cos(\\theta/2)cos(\\phi/2)\\vert00\\rangle + cos(\\theta/2)sin(\\phi/2)\\vert01\\rangle + sin(\\theta/2)sin(\\phi/2)\\vert10\\rangle + sin(\\theta/2)cos(\\phi/2)\\vert11\\rangle$$\n",
    "\n",
    "It was required to make the coefficients of $\\vert00\\rangle$ and $\\vert11\\rangle$ to be 0 so that there is no probability for those states to appear in final measurement, and accordingly it was required for the coefficient of $\\vert01\\rangle$ and $\\vert10\\rangle$ to be equal to $\\frac{1}{2}$ when squared individually.\n",
    "\n",
    "And the way cost function is defined, the theoretical definition of cost function can be written as,\n",
    "$$J(\\theta, \\phi) = \\{(cos(\\theta/2)cos(\\phi/2))^2\\}^2 + \\{0.5 - (cos(\\theta/2)sin(\\phi/2))^2\\}^2 + \\{0.5 - (sin(\\theta/2)sin(\\phi/2))^2\\}^2 + \\{(sin(\\theta/2)cos(\\phi/2))^2\\}^2$$\n",
    "\n",
    "To make the first coefficient zero, either of $cos(\\theta/2$) or $cos(\\phi/2$) needs to be zero and the former being zero would also make the coefficient of $\\vert01\\rangle$ zero, that needs to be avoided. So the later one is equated with zero, cos($\\phi/2$) = 0 which gives $\\phi = \\pi$ or $-\\pi$. This would also make the coefficients of $\\vert11\\rangle$ zero and so two of the four coefficients are dealed with.\n",
    "\n",
    "The coefficients of $\\vert01\\rangle$ and $\\vert10\\rangle$ needs to be made $\\frac{1}{\\sqrt2}$ so that their square gives $\\frac{1}{2}$. The value of $\\phi$ being $\\pi$ or $-\\pi$ gives sin($\\phi/2$) = 1. And so the value of $cos^2(\\theta/2)$ should be $\\frac{1}{2}$. Which on solving gives $\\theta = \\pi/2$ or $-\\pi/2$.\n",
    "\n",
    "Therefore to conclude the possible solutions of the cost function are: $[(\\pi/2, \\pi), (-\\pi/2, \\pi), (\\pi/2, -\\pi), (-\\pi/2, -\\pi)]$. Let's now try to visualize this by plotting the surface of cost function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cost_theory(theta,phi):\n",
    "    p00 = (np.cos(theta/2)*np.cos(phi/2))**2\n",
    "    p01 = (np.cos(theta/2)*np.sin(phi/2))**2\n",
    "    p10 = (np.sin(theta/2)*np.sin(phi/2))**2\n",
    "    p11 = (np.sin(theta/2)*np.cos(phi/2))**2\n",
    "    cost = (p00)**2 + (0.5 - p01)**2 + (0.5 - p10)**2 + (p11)**2\n",
    "    return cost\n",
    "\n",
    "x = np.arange(-pi,pi,0.1)\n",
    "y = np.arange(-pi,pi,0.1)\n",
    "X,Y = np.meshgrid(x,y)\n",
    "Z = cost_theory(X,Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "/* Put everything inside the global mpl namespace */\n",
       "/* global mpl */\n",
       "window.mpl = {};\n",
       "\n",
       "mpl.get_websocket_type = function () {\n",
       "    if (typeof WebSocket !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof MozWebSocket !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert(\n",
       "            'Your browser does not have WebSocket support. ' +\n",
       "                'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "                'Firefox 4 and 5 are also supported but you ' +\n",
       "                'have to enable WebSockets in about:config.'\n",
       "        );\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = this.ws.binaryType !== undefined;\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById('mpl-warnings');\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent =\n",
       "                'This browser does not support binary websocket messages. ' +\n",
       "                'Performance may be slow.';\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = document.createElement('div');\n",
       "    this.root.setAttribute('style', 'display: inline-block');\n",
       "    this._root_extra_style(this.root);\n",
       "\n",
       "    parent_element.appendChild(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
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       "    this._init_toolbar(this);\n",
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       "        fig.send_message('supports_binary', { value: fig.supports_binary });\n",
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       "        fig.send_message('refresh', {});\n",
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       "        fig.context.drawImage(fig.imageObj, 0, 0);\n",
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       "    this.ws.onmessage = this._make_on_message_function(this);\n",
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       "\n",
       "mpl.figure.prototype._init_header = function () {\n",
       "    var titlebar = document.createElement('div');\n",
       "    titlebar.classList =\n",
       "        'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n",
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       "        'style',\n",
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       "    titlebar.appendChild(titletext);\n",
       "    this.root.appendChild(titlebar);\n",
       "    this.header = titletext;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = (this.canvas_div = document.createElement('div'));\n",
       "    canvas_div.setAttribute(\n",
       "        'style',\n",
       "        'border: 1px solid #ddd;' +\n",
       "            'box-sizing: content-box;' +\n",
       "            'clear: both;' +\n",
       "            'min-height: 1px;' +\n",
       "            'min-width: 1px;' +\n",
       "            'outline: 0;' +\n",
       "            'overflow: hidden;' +\n",
       "            'position: relative;' +\n",
       "            'resize: both;'\n",
       "    );\n",
       "\n",
       "    function on_keyboard_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.key_event(event, name);\n",
       "        };\n",
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       "\n",
       "    canvas_div.addEventListener(\n",
       "        'keydown',\n",
       "        on_keyboard_event_closure('key_press')\n",
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       "    canvas_div.addEventListener(\n",
       "        'keyup',\n",
       "        on_keyboard_event_closure('key_release')\n",
       "    );\n",
       "\n",
       "    this._canvas_extra_style(canvas_div);\n",
       "    this.root.appendChild(canvas_div);\n",
       "\n",
       "    var canvas = (this.canvas = document.createElement('canvas'));\n",
       "    canvas.classList.add('mpl-canvas');\n",
       "    canvas.setAttribute('style', 'box-sizing: content-box;');\n",
       "\n",
       "    this.context = canvas.getContext('2d');\n",
       "\n",
       "    var backingStore =\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        this.context.webkitBackingStorePixelRatio ||\n",
       "        this.context.mozBackingStorePixelRatio ||\n",
       "        this.context.msBackingStorePixelRatio ||\n",
       "        this.context.oBackingStorePixelRatio ||\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n",
       "        'canvas'\n",
       "    ));\n",
       "    rubberband_canvas.setAttribute(\n",
       "        'style',\n",
       "        'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n",
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       "        for (var i = 0; i < nentries; i++) {\n",
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       "            } else {\n",
       "                // Chrome <84 implements even older version of spec.\n",
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       "                height = entry.contentRect.height;\n",
       "            }\n",
       "\n",
       "            // Keep the size of the canvas and rubber band canvas in sync with\n",
       "            // the canvas container.\n",
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       "                // Chrome 84 implements new version of spec.\n",
       "                canvas.setAttribute(\n",
       "                    'width',\n",
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       "                canvas.setAttribute(\n",
       "                    'height',\n",
       "                    entry.devicePixelContentBoxSize[0].blockSize\n",
       "                );\n",
       "            } else {\n",
       "                canvas.setAttribute('width', width * mpl.ratio);\n",
       "                canvas.setAttribute('height', height * mpl.ratio);\n",
       "            }\n",
       "            canvas.setAttribute(\n",
       "                'style',\n",
       "                'width: ' + width + 'px; height: ' + height + 'px;'\n",
       "            );\n",
       "\n",
       "            rubberband_canvas.setAttribute('width', width);\n",
       "            rubberband_canvas.setAttribute('height', height);\n",
       "\n",
       "            // And update the size in Python. We ignore the initial 0/0 size\n",
       "            // that occurs as the element is placed into the DOM, which should\n",
       "            // otherwise not happen due to the minimum size styling.\n",
       "            if (width != 0 && height != 0) {\n",
       "                fig.request_resize(width, height);\n",
       "            }\n",
       "        }\n",
       "    });\n",
       "    resizeObserver.observe(canvas_div);\n",
       "\n",
       "    function on_mouse_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.mouse_event(event, name);\n",
       "        };\n",
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       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousedown',\n",
       "        on_mouse_event_closure('button_press')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseup',\n",
       "        on_mouse_event_closure('button_release')\n",
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       "    rubberband_canvas.addEventListener(\n",
       "        'mousemove',\n",
       "        on_mouse_event_closure('motion_notify')\n",
       "    );\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseenter',\n",
       "        on_mouse_event_closure('figure_enter')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseleave',\n",
       "        on_mouse_event_closure('figure_leave')\n",
       "    );\n",
       "\n",
       "    canvas_div.addEventListener('wheel', function (event) {\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        on_mouse_event_closure('scroll')(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.appendChild(canvas);\n",
       "    canvas_div.appendChild(rubberband_canvas);\n",
       "\n",
       "    this.rubberband_context = rubberband_canvas.getContext('2d');\n",
       "    this.rubberband_context.strokeStyle = '#000000';\n",
       "\n",
       "    this._resize_canvas = function (width, height, forward) {\n",
       "        if (forward) {\n",
       "            canvas_div.style.width = width + 'px';\n",
       "            canvas_div.style.height = height + 'px';\n",
       "        }\n",
       "    };\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n",
       "        event.preventDefault();\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus() {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'mpl-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'mpl-button-group';\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'mpl-button-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        var button = (fig.buttons[name] = document.createElement('button'));\n",
       "        button.classList = 'mpl-widget';\n",
       "        button.setAttribute('role', 'button');\n",
       "        button.setAttribute('aria-disabled', 'false');\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "\n",
       "        var icon_img = document.createElement('img');\n",
       "        icon_img.src = '_images/' + image + '.png';\n",
       "        icon_img.srcset = '_images/' + image + '_large.png 2x';\n",
       "        icon_img.alt = tooltip;\n",
       "        button.appendChild(icon_img);\n",
       "\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    var fmt_picker = document.createElement('select');\n",
       "    fmt_picker.classList = 'mpl-widget';\n",
       "    toolbar.appendChild(fmt_picker);\n",
       "    this.format_dropdown = fmt_picker;\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = document.createElement('option');\n",
       "        option.selected = fmt === mpl.default_extension;\n",
       "        option.innerHTML = fmt;\n",
       "        fmt_picker.appendChild(option);\n",
       "    }\n",
       "\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', { width: x_pixels, height: y_pixels });\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_message = function (type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function () {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function (fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1], msg['forward']);\n",
       "        fig.send_message('refresh', {});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0,\n",
       "        0,\n",
       "        fig.canvas.width / mpl.ratio,\n",
       "        fig.canvas.height / mpl.ratio\n",
       "    );\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch (cursor) {\n",
       "        case 0:\n",
       "            cursor = 'pointer';\n",
       "            break;\n",
       "        case 1:\n",
       "            cursor = 'default';\n",
       "            break;\n",
       "        case 2:\n",
       "            cursor = 'crosshair';\n",
       "            break;\n",
       "        case 3:\n",
       "            cursor = 'move';\n",
       "            break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_message = function (fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n",
       "    for (var key in msg) {\n",
       "        if (!(key in fig.buttons)) {\n",
       "            continue;\n",
       "        }\n",
       "        fig.buttons[key].disabled = !msg[key];\n",
       "        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
       "    if (msg['mode'] === 'PAN') {\n",
       "        fig.buttons['Pan'].classList.add('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    } else if (msg['mode'] === 'ZOOM') {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.add('active');\n",
       "    } else {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message('ack', {});\n",
       "};\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function (fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = 'image/png';\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src\n",
       "                );\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data\n",
       "            );\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        } else if (\n",
       "            typeof evt.data === 'string' &&\n",
       "            evt.data.slice(0, 21) === 'data:image/png;base64'\n",
       "        ) {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig['handle_' + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\n",
       "                \"No handler for the '\" + msg_type + \"' message type: \",\n",
       "                msg\n",
       "            );\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\n",
       "                    \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
       "                    e,\n",
       "                    e.stack,\n",
       "                    msg\n",
       "                );\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "};\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function (e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e) {\n",
       "        e = window.event;\n",
       "    }\n",
       "    if (e.target) {\n",
       "        targ = e.target;\n",
       "    } else if (e.srcElement) {\n",
       "        targ = e.srcElement;\n",
       "    }\n",
       "    if (targ.nodeType === 3) {\n",
       "        // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "    }\n",
       "\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    var boundingRect = targ.getBoundingClientRect();\n",
       "    var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n",
       "    var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n",
       "\n",
       "    return { x: x, y: y };\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys(original) {\n",
       "    return Object.keys(original).reduce(function (obj, key) {\n",
       "        if (typeof original[key] !== 'object') {\n",
       "            obj[key] = original[key];\n",
       "        }\n",
       "        return obj;\n",
       "    }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function (event, name) {\n",
       "    var canvas_pos = mpl.findpos(event);\n",
       "\n",
       "    if (name === 'button_press') {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {\n",
       "        x: x,\n",
       "        y: y,\n",
       "        button: event.button,\n",
       "        step: event.step,\n",
       "        guiEvent: simpleKeys(event),\n",
       "    });\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.key_event = function (event, name) {\n",
       "    // Prevent repeat events\n",
       "    if (name === 'key_press') {\n",
       "        if (event.which === this._key) {\n",
       "            return;\n",
       "        } else {\n",
       "            this._key = event.which;\n",
       "        }\n",
       "    }\n",
       "    if (name === 'key_release') {\n",
       "        this._key = null;\n",
       "    }\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which !== 17) {\n",
       "        value += 'ctrl+';\n",
       "    }\n",
       "    if (event.altKey && event.which !== 18) {\n",
       "        value += 'alt+';\n",
       "    }\n",
       "    if (event.shiftKey && event.which !== 16) {\n",
       "        value += 'shift+';\n",
       "    }\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n",
       "    if (name === 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message('toolbar_button', { name: name });\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";/* global mpl */\n",
       "\n",
       "var comm_websocket_adapter = function (comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function () {\n",
       "        comm.close();\n",
       "    };\n",
       "    ws.send = function (m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function (msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data']);\n",
       "    });\n",
       "    return ws;\n",
       "};\n",
       "\n",
       "mpl.mpl_figure_comm = function (comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = document.getElementById(id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm);\n",
       "\n",
       "    function ondownload(figure, _format) {\n",
       "        window.open(figure.canvas.toDataURL());\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element;\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error('Failed to find cell for figure', id, fig);\n",
       "        return;\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function (fig, msg) {\n",
       "    var width = fig.canvas.width / mpl.ratio;\n",
       "    fig.root.removeEventListener('remove', this._remove_fig_handler);\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable();\n",
       "    fig.parent_element.innerHTML =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "    fig.close_ws(fig, msg);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.close_ws = function (fig, msg) {\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width / mpl.ratio;\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message('ack', {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () {\n",
       "        fig.push_to_output();\n",
       "    }, 1000);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'btn-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'btn-group';\n",
       "    var button;\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'btn-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        button = fig.buttons[name] = document.createElement('button');\n",
       "        button.classList = 'btn btn-default';\n",
       "        button.href = '#';\n",
       "        button.title = name;\n",
       "        button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message pull-right';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = document.createElement('div');\n",
       "    buttongrp.classList = 'btn-group inline pull-right';\n",
       "    button = document.createElement('button');\n",
       "    button.classList = 'btn btn-mini btn-primary';\n",
       "    button.href = '#';\n",
       "    button.title = 'Stop Interaction';\n",
       "    button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
       "    button.addEventListener('click', function (_evt) {\n",
       "        fig.handle_close(fig, {});\n",
       "    });\n",
       "    button.addEventListener(\n",
       "        'mouseover',\n",
       "        on_mouseover_closure('Stop Interaction')\n",
       "    );\n",
       "    buttongrp.appendChild(button);\n",
       "    var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
       "    titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._remove_fig_handler = function () {\n",
       "    this.close_ws(this, {});\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (el) {\n",
       "    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
       "    el.addEventListener('remove', this._remove_fig_handler);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (el) {\n",
       "    // this is important to make the div 'focusable\n",
       "    el.setAttribute('tabindex', 0);\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    } else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (event, _name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager) {\n",
       "        manager = IPython.keyboard_manager;\n",
       "    }\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which === 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "};\n",
       "\n",
       "mpl.find_output_cell = function (html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i = 0; i < ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code') {\n",
       "            for (var j = 0; j < cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] === html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel !== null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target(\n",
       "        'matplotlib',\n",
       "        mpl.mpl_figure_comm\n",
       "    );\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib notebook\n",
    "ax = plt.axes(projection='3d')\n",
    "ax.plot_surface(X, Y, Z, rstride=1, cstride=1,\n",
    "                cmap='viridis', edgecolor='none')\n",
    "ax.set_xlabel('theta')\n",
    "ax.set_ylabel('phi')\n",
    "ax.set_zlabel('cost')\n",
    "ax.set_title('surface');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Of the four minima in the above surface, two $[(\\pi/2, \\pi)$ and $(\\pi/2, -\\pi)]$ corresponds to $\\frac{1}{\\sqrt2}(\\vert01\\rangle + \\vert10\\rangle)$ and the other two $[(-\\pi/2, \\pi)$ and $(-\\pi/2, -\\pi)]$ corresponds to the $\\frac{1}{\\sqrt2}(\\vert01\\rangle - \\vert10\\rangle)$. This takes us to the bonus question of the task. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Bonus Question\n",
    "\n",
    "The general state capable of giving half probability of $\\vert01\\rangle$ and $\\vert10\\rangle$ each is,\n",
    "\n",
    "$$\\vert\\Psi\\rangle = \\frac{1}{\\sqrt2}\\vert01\\rangle + \\frac{1}{\\sqrt2}e^{i\\alpha}\\vert10\\rangle$$\n",
    "\n",
    "Since the solution provided above did not used any Rx rotation it was seen in the final state that none of the coefficients had imaginary part. Thus the above equation reduced to,\n",
    "\n",
    "$$\\vert\\Psi\\rangle = \\frac{1}{\\sqrt2}\\vert01\\rangle \\pm \\frac{1}{\\sqrt2}\\vert10\\rangle$$\n",
    "\n",
    "These two states can be distinguished easily by doing a bell basis measurement which is applying the CNOT gate that takes the second qubit to $\\vert1\\rangle$, followed by a hadamard that takes superposition of first qubit to computational basis and then final measurement. Since the task restricts to use only Rx and Ry gates, an appropiate angle for such rotation might also do the job.\n",
    "\n",
    "The hadamard gate maps $\\frac{1}{\\sqrt2}(\\vert0\\rangle+\\vert1\\rangle) \\rightarrow \\vert0\\rangle$ and $\\frac{1}{\\sqrt2}(\\vert0\\rangle-\\vert1\\rangle) \\rightarrow \\vert1\\rangle$ if instead of hadamard, Ry rotation by $\\pi/2$ is used it would map $\\frac{1}{\\sqrt2}(\\vert0\\rangle+\\vert1\\rangle) \\rightarrow \\vert1\\rangle$ and $\\frac{1}{\\sqrt2}(\\vert0\\rangle-\\vert1\\rangle) \\rightarrow \\vert0\\rangle$. Though they are different, they can be used interchangeably to distinguish the two states. \n",
    "\n",
    "To conclude, if after the variational circuit a CNOT gate with second qubit as target and Ry gate on first qubit with $\\pi/2$ rotation is added the states prepared in $\\frac{1}{\\sqrt2}\\vert01\\rangle + \\frac{1}{\\sqrt2}\\vert10\\rangle$ would give $\\vert11\\rangle$ and the one prepared in $\\frac{1}{\\sqrt2}\\vert01\\rangle - \\frac{1}{\\sqrt2}\\vert10\\rangle$ would give $\\vert01\\rangle$."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Redifining the circuit\n",
    "\n",
    "As proposed above, the variational part of the circuit will now be followed by the CNOT gate and a $\\pi/2$ Ry rotation. This part will kind of mimic the bell state measurement and will ensure that the optimized parameters in variational parts produce the target state. Note that this circuit itself does not produce the state, and its only role is to give us the parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"word-wrap: normal;white-space: pre;background: #fff0;line-height: 1.1;font-family: &quot;Courier New&quot;,Courier,monospace\">     ┌───────────┐      ░      ┌──────────┐\n",
       "q_0: ┤ RY(theta) ├──■───░───■──┤ RY(pi/2) ├\n",
       "     └┬─────────┬┘┌─┴─┐ ░ ┌─┴─┐└──────────┘\n",
       "q_1: ─┤ RY(phi) ├─┤ X ├─░─┤ X ├────────────\n",
       "      └─────────┘ └───┘ ░ └───┘            </pre>"
      ],
      "text/plain": [
       "     ┌───────────┐      ░      ┌──────────┐\n",
       "q_0: ┤ RY(theta) ├──■───░───■──┤ RY(pi/2) ├\n",
       "     └┬─────────┬┘┌─┴─┐ ░ ┌─┴─┐└──────────┘\n",
       "q_1: ─┤ RY(phi) ├─┤ X ├─░─┤ X ├────────────\n",
       "      └─────────┘ └───┘ ░ └───┘            "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param0 = Parameter('theta')\n",
    "param1 = Parameter('phi')\n",
    "\n",
    "qcb = QuantumCircuit(2)\n",
    "qcb.ry(param0, 0)\n",
    "qcb.ry(param1, 1)\n",
    "qcb.cx(0,1)\n",
    "qcb.barrier()\n",
    "qcb.cx(0,1)\n",
    "qcb.ry(pi/2, 0)\n",
    "qcb.draw()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above circuit will now be imported to pennylane with noise model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#device with noise model from ibmq_16_melbourne\n",
    "dev1 = qml.device(\"qiskit.aer\", wires=2, noise_model = noise_model, shots=1024, analytic=False)\n",
    "\n",
    "qcb = qml.from_qiskit(qcb)\n",
    "\n",
    "@qml.qnode(dev1)\n",
    "def circuit_bonus(x):\n",
    "    qcb({param0 : x[0], param1: x[1]})\n",
    "    return qml.probs(wires = [0,1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Redifining the cost function\n",
    "\n",
    "The above circuit would give $\\vert11\\rangle$ with full probability when the parameters are optimized to give the variational part $\\frac{1}{\\sqrt2}(\\vert01\\rangle + \\vert10\\rangle)$. And so the cost function needs to be redifined to give low output for the same. The cost function would now be defined as,\n",
    "\n",
    "$$J = (P_{00})^2 + (P_{01})^2 + (P_{10})^2 + (1 - P_{11})^2$$\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cost_fun_bonus(params):\n",
    "    prob = circuit_bonus(params)\n",
    "    p00, p01, p10, p11 = prob\n",
    "    cost = (p00)**2 + (p01)**2 + (p10)**2 + (1 - p11)**2\n",
    "    return cost"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Putting it all together\n",
    "The above variational circuit and cost function is now put together with the pennylane gradient descent optimizer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def circuit_opt_bonus(shots, iteration, in_param, opt):\n",
    "    #randomly initialized initial parameters\n",
    "    params = in_param\n",
    "    print(shot, 'measurements per iteration:')\n",
    "    dev1.shots = shot\n",
    "    optimizer = opt\n",
    "    costs = []\n",
    "    for i in range(iteration):\n",
    "        cost = cost_fun_bonus(params)\n",
    "        params = optimizer.step(cost_fun_bonus, params)\n",
    "        costs.append(cost)\n",
    "    print(\"The optimal parameter is \", params)\n",
    "    print(\"The final cost is \", round(costs[-1],4))\n",
    "    print()\n",
    "\n",
    "    return params, costs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The optimization is done once again for different number of shots per iteration and final parameters are noted."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 measurements per iteration:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/nikhil/.local/lib/python3.8/site-packages/pennylane_qiskit/converter.py:223: UserWarning: pennylane_qiskit.converter: The Barrier instruction is not supported by PennyLane, and has not been added to the template.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The optimal parameter is  [0.61969055 2.79095474]\n",
      "The final cost is  2.0\n",
      "\n",
      "10 measurements per iteration:\n",
      "The optimal parameter is  [1.50569055 3.20295474]\n",
      "The final cost is  0.12\n",
      "\n",
      "100 measurements per iteration:\n",
      "The optimal parameter is  [1.51389055 3.17479474]\n",
      "The final cost is  0.0098\n",
      "\n",
      "1000 measurements per iteration:\n",
      "The optimal parameter is  [1.51140935 3.17656654]\n",
      "The final cost is  0.0254\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#Lets call the above function with a list of measurements to be taken per iteration and iterations per optimization\n",
    "shots = [1,10,100,1000]\n",
    "iteration = 75\n",
    "in_param = np.random.randn(2)\n",
    "#Using pennylane gradient descent optimizer\n",
    "opt = qml.GradientDescentOptimizer(stepsize=0.2)\n",
    "#Store the lists of cost convergence for each number of shots\n",
    "cost_shot = []\n",
    "#Store the final parameters value after the optimization\n",
    "final_params = []\n",
    "\n",
    "for shot in shots:\n",
    "    final_param, costs = circuit_opt_bonus(shot, iteration, in_param, opt)\n",
    "    final_params.append(final_param)\n",
    "    cost_shot.append(costs)    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here notice that the final parameters are always either $[\\pi/2, pi]$ or $[\\pi/2, -pi]$ as predicted by the theory in previous section. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Final measurements\n",
    "After the optimization being done the final measurement corresponding to the $\\frac{1}{\\sqrt2}(\\vert01\\rangle + \\vert10\\rangle)$ can be done for the sake of completeness. The optimal parameters when fed into the original circuit (without measurement sub-part) it should produce the $\\vert01\\rangle$ and $\\vert01\\rangle$ with equal probability."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 measurements per iteration used for circuit optimization:\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10 measurements per iteration used for circuit optimization:\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100 measurements per iteration used for circuit optimization:\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1000 measurements per iteration used for circuit optimization:\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "#device with noise model from ibmq_16_melbourne\n",
    "dev1 = qml.device(\"qiskit.aer\", wires=2, noise_model = noise_model, shots=1024, analytic=False)\n",
    "\n",
    "param0 = Parameter('theta')\n",
    "param1 = Parameter('phi')\n",
    "\n",
    "qc = QuantumCircuit(2)\n",
    "qc.ry(param0, 0)\n",
    "qc.ry(param1, 1)\n",
    "qc.cx(0,1)\n",
    "qc = qml.from_qiskit(qc)\n",
    "\n",
    "@qml.qnode(dev1)\n",
    "def circuit(x):\n",
    "    qc({param0 : x[0], param1: x[1]})\n",
    "    return qml.probs(wires = [0,1])\n",
    "def plot_graph(params):\n",
    "    prob = circuit(params)\n",
    "    result = {'00': prob[0], '01': prob[1], '10': prob[2], '11': prob[3] }\n",
    "    return plot_histogram(result, figsize=(6, 4))\n",
    "\n",
    "images = []\n",
    "for i in range(len(shots)):\n",
    "    images.append(plot_graph(final_params[i]))\n",
    "    print(shots[i], \"measurements per iteration used for circuit optimization:\")\n",
    "    display(images[i])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion\n",
    "\n",
    "In the task the variational circuit was successfully implemented to the produce a specific measurement outcome. It was shown with the calculation how the problem can be solved by only two variational parameter and thus reducing the dimension of problem. Since there were only two parameters it could be easily visualized by a surface plot. It  was seen from the plot that the solution was not unique. Different optimization algorithms were also compared and in which the momentum gradient descent optimizer outperformed the others.\n",
    "\n",
    "By the end of task, a modified bell state mesaurement was used to confirm the final state of the circuit. Though commonly, such entangled pair and measurements are done using hadamard gate, Ry gate was shown to be sufficient for both. And finally a specific maximally entangled state was prepared using variational quantum algorithm."
   ]
  }
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