diff --git a/docs/notebooks/QNN_tutorial.ipynb b/docs/notebooks/QNN_tutorial.ipynb new file mode 100644 index 0000000..c8ccc36 --- /dev/null +++ b/docs/notebooks/QNN_tutorial.ipynb @@ -0,0 +1,1629 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Qiskit quantum neural network (QNN) tutorial\n", + "In this notebook, we implement a quantum neural network (QNN) for a data classification task. Our dataset consists of images containing horizontal and vertical stripes, and our goal is to label unseen images into one of the two categories depending on the orientation of their line. As the ansatz of our QNN, we specifically construct a [quantum convolutional neural network](https://www.nature.com/articles/s41567-019-0648-8) (QCNN). For data generation and ansatz construction, we follow the strategy found [here](https://qiskit-community.github.io/qiskit-machine-learning/tutorials/11_quantum_convolutional_neural_networks.html)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Data generation\n", + "We start by randomly generating a dataset consisting of 2x4 images with horizontal and vertical lines. Images with horizontal lines are labeled -1 and vertical with +1." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np \n", + "\n", + "\n", + "def generate_dataset(num_images):\n", + " images = []\n", + " labels = []\n", + " hor_array = np.zeros((6, 8))\n", + " ver_array = np.zeros((4, 8))\n", + "\n", + " j = 0\n", + " for i in range(0, 7):\n", + " if i != 3:\n", + " hor_array[j][i] = np.pi / 2\n", + " hor_array[j][i + 1] = np.pi / 2\n", + " j += 1\n", + "\n", + " j = 0\n", + " for i in range(0, 4):\n", + " ver_array[j][i] = np.pi / 2\n", + " ver_array[j][i + 4] = np.pi / 2\n", + " j += 1\n", + "\n", + " for n in range(num_images):\n", + " rng = np.random.randint(0, 2)\n", + " if rng == 0:\n", + " labels.append(-1)\n", + " random_image = np.random.randint(0, 6)\n", + " images.append(np.array(hor_array[random_image]))\n", + " elif rng == 1:\n", + " labels.append(1)\n", + " random_image = np.random.randint(0, 4)\n", + " images.append(np.array(ver_array[random_image]))\n", + "\n", + " # Create noise\n", + " for i in range(8):\n", + " if images[-1][i] == 0:\n", + " images[-1][i] = np.random.rand() * np.pi / 4\n", + " return images, labels" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We split the generated data into training and test sets." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.model_selection import train_test_split\n", + "\n", + "np.random.seed(42)\n", + "images, labels = generate_dataset(50)\n", + "\n", + "train_images, test_images, train_labels, test_labels = train_test_split(\n", + " images, labels, test_size=0.3, random_state=246\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can now display some images from the dataset with horizontal or vertical lines." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "fig, ax = plt.subplots(2, 2, figsize=(10, 6), subplot_kw={\"xticks\": [], \"yticks\": []})\n", + "for i in range(4):\n", + " ax[i // 2, i % 2].imshow(\n", + " train_images[i].reshape(2, 4), # Change back to 2 by 4\n", + " aspect=\"equal\",\n", + " )\n", + "plt.subplots_adjust(wspace=0.1, hspace=0.025)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Defining the neural network ansatz\n", + "As mentioned previously, our ansatz is a quantum convolutional neural network (QCNN), consisting of alternating convolutional and pooling layers. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We start by constructing the parametric two-qubit unitary which will be the building block of the convolutional layer. As a design choice, we implement these convolutional circuits as the 3-parameter gate set found in between the CNOT gate blocks of the [KAK decomposition](https://journals.aps.org/pra/abstract/10.1103/PhysRevA.69.032315)." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import numpy as np\n", + "from qiskit import QuantumCircuit\n", + "from qiskit.circuit import ParameterVector\n", + "\n", + "\n", + "def conv_circuit(params):\n", + " target = QuantumCircuit(2)\n", + " target.rz(-np.pi / 2, 1)\n", + " target.cx(1, 0)\n", + " target.rz(params[0], 0)\n", + " target.ry(params[1], 1)\n", + " target.cx(0, 1)\n", + " target.ry(params[2], 1)\n", + " target.cx(1, 0)\n", + " target.rz(np.pi / 2, 0)\n", + " return target\n", + "\n", + "\n", + "# Display the convolutional circuit\n", + "params = ParameterVector(\"θ\", length=3)\n", + "circuit = conv_circuit(params)\n", + "circuit.draw(\"mpl\", style=\"clifford\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The convolutional layer consists of these two-qubit unitaries laid out in non-overlapping nearest-neighbor topology in 2 layers as defined below." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def conv_layer(num_qubits, param_prefix):\n", + " qc = QuantumCircuit(num_qubits, name=\"Convolutional Layer\")\n", + " qubits = list(range(num_qubits))\n", + " param_index = 0\n", + " params = ParameterVector(param_prefix, length=num_qubits * 3)\n", + " for q1, q2 in zip(qubits[0::2], qubits[1::2]):\n", + " qc = qc.compose(conv_circuit(params[param_index : (param_index + 3)]), [q1, q2])\n", + " param_index += 3\n", + " qc.barrier()\n", + " for q1, q2 in zip(qubits[1::2], qubits[2::2]):\n", + " qc = qc.compose(conv_circuit(params[param_index : (param_index + 3)]), [q1, q2])\n", + " param_index += 3\n", + "\n", + " qc_inst = qc.to_instruction()\n", + "\n", + " qc = QuantumCircuit(num_qubits)\n", + " qc.append(qc_inst, qubits)\n", + " return qc\n", + "\n", + "\n", + "# Display the convolutional layer for an example of 8 qubits\n", + "circuit = conv_layer(8, \"θ\")\n", + "circuit.decompose().draw(\"mpl\", style=\"clifford\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The pooling layer also consists of two-qubit unitaries, which we choose to design with 3 parameters as below." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def pool_circuit(params):\n", + " target = QuantumCircuit(2)\n", + " target.rz(-np.pi / 2, 1)\n", + " target.cx(1, 0)\n", + " target.rz(params[0], 0)\n", + " target.ry(params[1], 1)\n", + " target.cx(0, 1)\n", + " target.ry(params[2], 1)\n", + "\n", + " return target\n", + "\n", + "\n", + "# Display the pooling circuit\n", + "params = ParameterVector(\"θ\", length=3)\n", + "circuit = pool_circuit(params)\n", + "circuit.draw(\"mpl\", style=\"clifford\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "When putting the pooling layer together, we connect a pair of qubits with the two-qubit unitaries defined above. Then we trace out one of the qubits per pooling circuit block, i.e. we discard half of the qubits in the entire pooling layer and effectively reduce the system size by half." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def pool_layer(sources, sinks, param_prefix):\n", + " num_qubits = len(sources) + len(sinks)\n", + " qc = QuantumCircuit(num_qubits, name=\"Pooling Layer\")\n", + " param_index = 0\n", + " params = ParameterVector(param_prefix, length=num_qubits // 2 * 3)\n", + " for source, sink in zip(sources, sinks):\n", + " qc = qc.compose(pool_circuit(params[param_index : (param_index + 3)]), [source, sink])\n", + " qc.barrier()\n", + " param_index += 3\n", + "\n", + " qc_inst = qc.to_instruction()\n", + "\n", + " qc = QuantumCircuit(num_qubits)\n", + " qc.append(qc_inst, range(num_qubits))\n", + " return qc\n", + "\n", + "\n", + "# Display the pooling layer with an example of 8 qubits, where source qubits are discarded afterwards\n", + "sources = [0, 1, 2, 3]\n", + "sinks = [4, 5, 6, 7]\n", + "circuit = pool_layer(sources, sinks, \"θ\")\n", + "circuit.decompose().draw(\"mpl\", style=\"clifford\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Qiskit Patterns Step 1: Mapping the problem to quantum circuits\n", + "We are now ready to build the whole quantum circuit, which consists of a feature map to encode the data onto the quantum computer followed by the quantum convolutional neural network ansatz." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "def get_qcnn_ansatz(num_qubits: int) -> QuantumCircuit:\n", + " \"\"\"\n", + " Creates the quantum convolutional neural network (QCNN) ansatz.\n", + "\n", + " Args:\n", + " num_qubits: number of qubits in the circuit.\n", + "\n", + " Returns:\n", + " ansatz: QCNN ansatz as a QuantumCircuit object.\n", + " \"\"\"\n", + " ansatz = QuantumCircuit(num_qubits, name=\"Ansatz\")\n", + "\n", + " # Convolutional layer acts on \"full_qubits\" number of qubits\n", + " # Pooling layer reduces \"full_qubits\" number of qubits to \"half_qubits\" number of qubits\n", + " # If \"full_qubits\" is odd, take the larger half to ensure last pooling layer has 2->1 qubits\n", + " full_qubits = num_qubits\n", + " half_qubits = (num_qubits + 1) // 2\n", + "\n", + " # Add convolutional and pooling layers until there is only one qubit left for binary classification\n", + " layer = 1\n", + " pool_until = 1\n", + " while full_qubits > pool_until:\n", + " # Convolutional Layer\n", + " ansatz.compose(conv_layer(num_qubits=full_qubits, \n", + " param_prefix=f\"c{layer}\"),\n", + " list(range(num_qubits - full_qubits, num_qubits)),\n", + " inplace=True)\n", + " \n", + " # Pooling Layer\n", + " ansatz.compose(pool_layer(sources=list(range(0, half_qubits)),\n", + " sinks=list(range(half_qubits, full_qubits)),\n", + " param_prefix=f\"p{layer}\"),\n", + " list(range(num_qubits - full_qubits, num_qubits)),\n", + " inplace=True)\n", + "\n", + " full_qubits = half_qubits\n", + " half_qubits = (half_qubits + 1) // 2\n", + " layer += 1\n", + " \n", + " return ansatz" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We encode the data onto the quantum computer using the Z Feature Map. This method uses one qubit per data feature, i.e. per pixel in this case. Then we add convolutional and pooling layers in an alternating fashion until we are left with a single qubit in the circuit. This is because we have a binary classification task and measuring a single qubit is sufficient to classify data into one of the two labels. " + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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QPc5M86abp27tein+5N1cOurElCz3ucb+u56Se5/+fR569sYcvvfg3P/MtalbZ4P063Fc6T7LPlf66k7HZp8eJ1R43Xp1G5TbpjeCteOVd8alVlGtbPPf1XmXqVe3ftpt3j2vvjOuiioDYE2Z4wEAYMWqZfjzlltuyZIlS3LMMcekcePGFe7ToMHSD19XFP78y1/+kgkTJqytEsuZPvPN3PjgBTmm/0/SauM2mTFzyjq7diG57fHLMvr5m/OV7Q/O0V89b43OtUWLjkmS+vUafenNkzem/ztvTH8hx/b7SU7Yt+zjBO975g/l9v+i1byaNNg4r819ttz2VV31bIsWHTPulfuzSbOtvjC4uSL77XJSRj06LJPe/le+f9g1X7jvw8/9KV3a7ZXzji376Ix3P3x9hce8/f6kfCUHl9n21vsvJ6l4FRBWjTkDaqY9u3wjE994Ivc+84ecPOCiL91/2UpDb733Unbq+NUyY5UxJ/fvcUJuH3NFHnr2xuy/68D8e/Jj2X/XU1KvzgaVWkOThhvn07kzy22fPrP8z85VXVFzs43bZfwr92f2vI/SuEGzcvU1rN80GzZssUrnTJLnX3skH34yLYOPuC77Lbe69h8f+PEqn29dXH//XU/J05PuyT9fuiOvv/t8kqUrgn7emvYfAJVFPwzltdq4XZaULMnb701Ku827lhl7672X/7tP21Xed23YsHHL1K/XKFM/eKXc2DsVbFtTDz/3p7TaqE0uOvm+1KpVq3T7uP/cX+H+22y5czpssWPuf+baDOh5csZM/Ft23/6QMiutbtG8Q4qKirJo8YKCCOVCdffhJ9PStFGLMv89ukyLDbfIy2/9MwsXLUjdOvWqoDoA1oQ5HgAAVqxahj9Hj166klGfPn1WuM/UqVOTVBz+/OSTT/K9730vw4cPz7HHHrt2ilzOr/92Wlo1b5fD9jp7nVyvEE14/dH8/t6h2bLlNhl61I1r/LjUnbfZN80ab5JbH/1l9u5+ZJkP8JPks4XzsnjxojSs3yS1ai1dtWLZqg7LvDnjxYx98e/lzt1gg6Wh40/mzix33tYtO+XJF2/Pf95+Jtv+97cUlyxZktvHXL5K9ffrcVzuGHtl/njfj3L+8beldq2yK2vM+vS9bNRk0xUe37plp5xx8K/z6dyZ2bvbkV94rVpFtZPlVueYt2DOF9Z811NX58Bep6dRgw2TJHPmfZy7n/pdGjdolq7t9v6yt1djdOzUMQsWzSt9Xa9Og1wz6LUvPc6cATXTgJ7fzl3//G1ue3x4ttty13xlh4PL7fPq1Gfzn7efzkFfOSM9OvVP/XqNcsfYK7Pvzt9Kw/pNkiRz53+aO8ZemQYbNE6Pjv1Xu54OW3RPu8265pHnbkq9OvWzpGRJ+u9cdtWjyqhhi5ad8uTEv6X443fTYsMtkiQLFn2WO//5m3L7Nqj3v5/BK2P3HQ7JM/+5N7c++st8e/9flm5/5j/35fV3n89Xdzq2TEBgZS3rHZb/+Tn+lQfzn7efXuXzrYvr77rd19K86ea5+18j8/b7k7J9m92z1SZlH+++pv0HUDMs3+OuCv0wrL7ddzgkdz3129zy6MX50dE3l35u8uaMF/PUy3dmh7Z7lD7GfVX2XRtq16qdXbYdkDH/vi0vvjk2O7TdvXTstid+VenXq1WrdlJUVOZzncWLF+XWR3+5wmP27zkwI/5+Rq6647tZsGh+BvT8dpnxpo2ap+e2++fJibfn5bf+lc5b71ZmvKSkJB/PKV6rX0dYX61JL7AiX9YjfLZgbupWEApaemz9pfssnCsYBFCJKnO+/6J53hwPhWtt9IUAUB21atUq48ePX61jq2X486233kqSbL311hWOL1q0KGPHjk1ScfjzvPPOS6dOnXLMMcesk/Dnw8/elOdeeyiXnf5E2cdqUurDT6bn5zcdkSVLFmePLt/IUy/ducJ9223WtdyqFRVpUK9Rhh51Yy68/pCcdMk22XeXk7JFiw6ZPe+jvPP+f/Lki7fnwhP+nm7te2erTbZLm023z18euySfLZibLTfZJlM/eDX3/Gtk2mzWJa9NLbuS53Zb7ZZ/jL0qV95+Rnpu97XUqV032261azbbuG323+2U3PbEr3LhDV/P1/c4K3Xr1MsT/77tCx/7XpFtttwlx/e/MDc+dGFOu7x79up6eJo33TwzP5me1959Ns/8597c98sFX3iOr+9x5kpda8+uh+Wef43Mz286Mjt17JdZn76X+8ddl6YNm6/wmA0btch3r9w1+/x3tbEHx/0x73/0ds4+/A+pX88jzpaZPm1a5i+cW/q6/ko8/s2cATVX/XoN87OT7s6Pr/taLrjhkPTotE96dOyfJo2a5+PZH+SFyY9m/KsP5IjeQ5MkjRs0y8CvXZIr//6dpXPyzicmSR4cf32mFb+e731jZGlIf3X173FCRt49OKMeG5bWLTuVu+ldGTUc/JVBeWzCrRl6Tb8csNtpWbR4QR5+9k/ZoIKfJ9tsucvSR5s/8ovMnjcr9es1SquN22a7rXat8Nz77HxiHhx/Q0Y9OizvzZySLu32yrTi13PnU7/NRo03zUkrscJqRXZou0c2btIqI+8enBmzpqTlhq0zedqEPPzcn9K2VZe8OWPiap13mcWLF+bPD/+8wrE9uhy6WtevXat29tvlpPz5kaXnPWm/8u+9MvoPoPpbvsddFfphWH09OvXP3t2OyGMTbs3subOya+cDMuvTGbnzn79JvTr1852DR6zWvmvLt/b9eZ595YH86Nr9cvBXBqXFhq3z9H/uycezP0iyaiu6P//6I1mwaH657U0btciBvU7LXl0Oy7X3nZsf/WFA9uhyaObO/ySjn7/5C+eQvjsdk2vuGZJHnrsprTZumx2XW8U+Sc489Op8/zd7ZPDVe6Vfj+PTYfMdU1KyJNNnvpF/vvSP9O9xfI7f58KVfh9QXaxJL7AiX9YjbFCvYebNfr/CsWXzwwYr0WcAsPIqc77/onneHA+Fa230hQBAWdUy/Dlnzpwkybx5Ff8WyahRo1JcXJwmTZqkbduyj7AaP358fv/73+fZZ8s/lnttWLDos4y86+z03Hb/bNSkVd4tXvoY7eKP302SzJn/cd4tfj0bNmpR7lGkNcnUD17Jx3OKkyS3jP7iEMZx/S9YqfBnkuyyzb656qxxGTX6l3nkuZvy8ZwP0rjBRtm8eft8Y8+z03azpeepXat2fn7yPRl59w/y0LM3ZP6COWnTaocMOfKGvDH9hXLhzz7dv5nX330+j71wa57491+zpGRJfnDEH7PZxm2z2cZtc+EJd+S6+36UGx44P00aNU+/nY7LfruclJMu3baiMlf8Xve5IJ223Dl/f3JE/j7misxfMCfNGm+SNq12yBmVeKPmtAMvS8MNmuTxF/6Sf770j7RstmW+tusp6bTlLjnnmoofbfbt/Ydl4ptjcuc/f5OPPn0vW7TslHOP/nP67nh0pdVVHWy2+eblVv78IuYMYIsWHXL1957PPf8amTET/5abR/8i8z6bnSYNN06n1jtnyJE3lJlrD/rKGdm4yWb56+OX5qaHfpokabd5t1x4wt+z+w6HrHE9X93pmPzh3nMyd/4npaHT5a1pDTu03T1Djrw+t4y+KL+/Z0haNN0iB/Q6PZ223DlDR5a9Cb/JRltl8BHXZdSjwzLi9tOzaPHC9O9xwgrDn3Vq183FAx/IzQ//PI+9MCpPvnh7Gtdvlr26Hp5v7ffzbNJsy1X7gvxX4wbNcvG3H8jv7xmaf4y9MouXLErHLXrkFyfdm/ueuXaNw58LFy/I9Q+cX+HY5i06ZOtNO6/W9Qfs+u3cMvqi1K/XKHt1O7zCfdZV/wEUruV73FWhH4Y1c+43/5wOW+yUB8dfn2vuGpz69Rqla7u9c+K+P0vbzbqs9r5rw5abbJNfnf5Errn7B/n7k79OvTr1s+t2B+S7X/9Njr+4XTao+8XzweeNe+X+jHul/CPct2y5TQ7sdVoO7z0kJSnJ/c9cm6v/cVY2atIqvbsdmX12+Va+PbxzhedsVL9penc7MvePuy777vytCsOomzTbMr/93rMZ9eiw/POlf5SuiN+y2ZbZrfOB2bvbESv/BYFqZE16gRX5sh6hedPN8/Z7L2fBos/KPRa4+ON3s2GjFlaEA6hklTnff9E8b46HwrU2+kIAqI5atWq12scWlZQs9yzIaqBz586ZNGlSrrrqqnznO98pMzZ9+vT06NEj06dPz+67754nn3yydGzx4sXp2bNn+vTpk+HDhydZusrABRdckAsvvHC1alm8IHn0C+6Bz573Ub7+k42+9DynfO3SHN77B6tVA6xLD4y7PsP/8q0MP+3RdGvfu6rLWe/1OTOp/bnPJMwZAFSlDz+ZnqN/sWUG7HJyvnfYyKouByhQy/e4q0I/DLw69dl859c75+QBF+eovj+s0lpG3H5G7nn6mtx07pS0bNa6SmuBQrImvcCKfFmP8Mf7f5ybH/lFLjv9iXRpt2fp9gUL5+cbFzRPl3Z75aJv31e5RQHUcJU533/RPG+Oh8K1NvpCAKCsarnyZ79+/TJp0qQMGzYs/fv3T6dOnZIk48aNy3HHHZfi4qUrSHbv3r3McVdddVXee++91Q56ro769Rrl/OP+Wm77x7M/yIi/n5Fdttkv+/U8Oe02W7mVLIHqzZwBwNp011NXZ8mSxdl/t1OquhSACumHoXr5bOG8Mit8lpSU5C+PXZIk2alT/6oqK0kyZ97HeeS5m9JzmwGCn1AAenc7MreMvii3j7miTDDo3qd/n/kL56bvjsdUYXUArAlzPAAArFi1DH8OHTo0N998c955551sv/322XbbbTN//vy8/vrrGTBgQNq0aZMHHngg3bp1Kz2muLg4559/foYPH55Fixblo48+Kh2bP39+PvroozRt2jS1atWq1Frr1K6bvboeVm77jJlTkiSbNW9f4ThQM5kzAFgbHp1wa96f9Xb++til2bnTvunUukdVlwRQIf0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+ "text/plain": [ + "
" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from qiskit.circuit.library import ZFeatureMap\n", + "from qiskit.quantum_info import SparsePauliOp\n", + "\n", + "# One qubit per data feature\n", + "num_qubits = len(train_images[0])\n", + "\n", + "# Data encoding\n", + "# Note qiskit orders parameters alphabetically\n", + "feature_map = ZFeatureMap(num_qubits, parameter_prefix='a')\n", + "\n", + "# QCNN ansatz\n", + "ansatz = get_qcnn_ansatz(num_qubits)\n", + "\n", + "# Combine the feature map with the ansatz\n", + "circuit = QuantumCircuit(num_qubits)\n", + "circuit.compose(feature_map, range(num_qubits), inplace=True)\n", + "circuit.compose(ansatz, range(num_qubits), inplace=True)\n", + "\n", + "# Display the circuit\n", + "circuit.draw(\"mpl\", style=\"clifford\", fold=-1)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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z/GOtzJX+9KF08XHS0C7hH89KXr3+AAAAAAAD/UJv8/L1LyiVXpll/BmueRukNXnST8ZL3duEfzwrebkMAAAAb1uTL70611hANBz1funDZT/MGRkvZTnsoTDag/Cq2nrjQbBFm8M/Vk6R9Ngn0hlDpYn9mT8KZ6EMwMtlYPY6Y8G4WpMvnD7c3mpjLvKy7cZi0skOe8GQl8sAuP7wdhnYUij9Y7axYFQ4AgFj0aiVucazJO0zIhOfVbxcBgAAgLd9u8VYYyCUBQMPVl1nHOf7bdLUcVJ6SkTCswztQXhVRY306hxpVV74x1q/Q3r4Q+MFQ8f1DP94VqIOgJd9v136z3yjPghHbb2xCPH326WfjpdaNItMfFahHgC8h99oeMbEYZfo2H6nK6CAdu/J02eLX9Gz7/1KW3es1C0/fsHu8GAByoA71dRJL34trSuI3DH9AemDpcbbBc4f4awJoNKhZb24rECff/tPPff+rdq2czVlHa60Klf6+6zwJ/0dLL9Eeuoz6bpJUqeWkTuuVagHGtepUyc9/PDD6tSpk92hRBVlAF4qA8V7pb9+Ke0qi9wxK2uMBScuOk4a3Styx7WKl64/AABmeKU/AABonFdyAf1Cb/Pa9d9eJD33lfFgf6QUVxjjTVeeIPXrELnjWsVrZcAMr+QBAEDDyAPutHSrscCTPxC5Y27bLT35uXT9JKmtw146K9EehLdU10kvzjQe5oyUer/03hJj/ui5w5k/6iZeaQtQBuC1MvDx99KnyyN7zGXbpJIK6ZqJUmpSZI9tBa+VARyK6w+vlYG1+dJLX0s1EXyWpKBUevIz6bqTpM6tIndcq3itDJjhlT4BAKBh5AF3mrVWentxZI+5tkB6+gvj2eIMhy0aJtEehLeUV0l//UrKK47cMWvqpNfnG/MRTxoQueNahTqgcbQF3GnBRuk/C4yXwkTKxp3GnJEbJkkt0yJ3XKtQDwANc2MeiLM7AMAqvTsN1+QRl+vkEVN18cS79NRNC9SqRUd9vPBFFZdFcNYYYhZlwH3q/dLL30R24dCDzVorvb80OseOpoPL+oUn3qEnb5qvNhnZ+njhiyop32V3eEBEbdwR+YVD9ymvlp79Utq5J/LHjjbqgcZlZmbq7LPPVmZmpt2hRBVlAF4pA+VV0rNfRXbh0H0Ckt5YYLyB0mm8cv0BADDLK/0BAEDjvJIL6Bd6m5eu/47SyC8cuk9tvfGw6aadkT92tHmpDJjllTwAAGgYecB9VuVGfuHQfUp+WFC+eG/kjx1ttAfhFXX10stfR3bh0IN9vUb6aFl0jh1N1AGN80pbgDIAL5WBL1dGfuHQfbYWSn+baSwW4DReKgM4EtcfXioDm3dFfuHQfSpqjPnJBaWRP3a0eakMmOWVPgEAoGHkAfdZsDHyC4fuU1AqPfelVBGFOUnRRnsQXlFVKz03I7ILhx7svSXS7HXROXY0UQc0jraA+3y3RfrP/MguHLpPYZkxZ6SsKvLHjjbqAaBhbswDLB4Kz0pLaaEBXccoEAgof/cmu8OBDSgDzvfFSmlNfnTP8dUqaWVudM8Rbc2S0tSv62gFAgHl7d5odzhAxOytlv4xOzoLh+5TXm08aFLvj945rEA9cEBRUZH+/e9/q6ioyO5QLEUZgBvLQCBgvBEqmos8B2QMHkdjcVIrufH6AwAQCq/2BwAAB3g1F9Av9Da3Xv+6eukfc6KzcOg+tfXGfYjKmuidwwpuLQOh8GoeAAAYyAPuUlop/WtudBYO3aekQno1yuewAu1BuNXnK6W1UXrx/MHnWJ0X3XNEG3XAAV5tC1AG4NYysGmn9MHS6J5j8y7pQwcuJH04t5YBBIfrD7eWgcoa4x5ONBYO3afih3PURfEcVnBrGQiFV/sEAAADecBdCkqlNxdG9xz5pdJbi6J7DivQHoRbvfutlBPlKv2dxdE/R7RRBxxAW8BdCst+WDg0iufYVRa9xUmtRD0AGNyYB1g8FJ4VCASUW7hBktQirbXN0cAOlAFnyyuWPlthbp9bp0j3n2f8acYbC5z5dqCD5f/QiG+RmmVzJEDkTP9W2mPybR2h1AM5RdKXq8ydJxZRDxjy8/P14IMPKj8/yqtPxyDKANxWBr7bIq3IMbdPKHmgtl56fZ7zHwp02/UHACAUXu4PAAAMXs4F9Au9zY3X//OVxv1CM0IZGyqtlN79ztx5YpEby0AovJwHAADkATcJBKQ3F5hf5D2U9uDGndKcdebOE4toD8Jtcoqkzy2cP+r0l0pQBxi83BagDMBtZaCmTnothIeCQ8kF36wx2oRO57YyAHO4/nBjGfjfd8ZLP8wIJQ/kl5h/di0WubEMhMLLfQIAAHnATer90mvzjD/NCKU9+N1W6fvt5s4Ti2gPwm1W50nzTa5/F0od4A8Y9Y3TXypBHWCgLeAe/oD0+nzzL5UJpR5YmSt9u8XceWIR9QDgzjyQYHcA0VZYWKhp06bpnXfeUU5Ojtq0aaPzzz9fDz30kG6++Wa9/PLLeuqpp3TjjTfaHSqirKq2QqV7CxUIBFS0J1/vznlKm/KXqX+X0cpu09vu8GAByoB7BALGhEyzA3stmkmZqebPt6fSeGvwBcea39cOB5f14rICvT/vOW3IXaJ+nY9Vdps+dodnm117jLc7+ANSeorUJUuKYxl1x1pXIC3ebH6/UOuBT5dLw7tKrZub39cO1AOgDBxpb7WxCKRkDNZX1UopifbGFE1uLwNVtdLbi83vF2oe2LRLWrBRGtPL/L52cPv1D0UgIOUWH6gH6v3G93w+e+MCAAAAgGigX3ikkopDx4bq6qWEeHtjihYvXP9de8wvEiOFPja0YKN0bHepZzvz+9rBC2UAAAB42/fbjQc0zAq1Pfj+EmloF2N/J6A9eKSaOmlroVRZKyUlSJ0ypeYOuZ440r75o2ZfABpqHVBSIX38vXT+SPP72oE6AJSBIwUC0vaiQ+eMuJkXysDnK6TCMvP7hZILAjLyzq/PlOIcMs/IC2XArPKqQ+8RVNdKyS6dP8r1b1hh2aFlwO9377MkXigDm3aaXyRGCr1P8MVKaUR3qV0L8/vawQtlAAAAeNuc9dK23eb3C7U9+N+FUr8Oxv0FJ6A9eKTKGqPMVNdJyQlS5ywpNdnuqBCqunpjvM6sUOuAvBLp6zXSpIHm97UDdQC8YOGm0F76FWo98M5iaWAnqVmS+X3tQD1wpOpaaetuY22C5AQpO0tKoy0AF3BIFyU0S5cu1WmnnaaCggKlpaVpwIABysvL05NPPqmNGzeqqKhIkjRs2DB7A42inFUz9fZDEzX+kkc04ozbG/zME5f71G3YGTrn9g8sjs5a//zsPv3zs/v2/zvOF6cxA87WLT9+wcaooovrfygvlgG32rrb+LLSwk3SGcOkVAc06A8v65I0ftD5uum8Z2yKyD6BgLRsuzRnnbR+x6HbstKksb2lsb0Y5HOib9Zae756vzR3vXT2cGvPGyrqAVAGDtheZNQZS7ZIdT9M/N5bI933jjSquzShn3Mmc5nh9jKweLNUUWPtOb9ZI43u6YzFJt1+/c2oqTPa8nPWSfmlB75fXi09/ok0rrc0srt7F4wBAAAA4E30Cw9YV2CMDa3MNe4ZSMbY0P3vSmN6SuP7SBkhTASLZV64/rPXm18kJlzfrHXO4qFeKAMAAMDbrJ4zUlNvLEpyyiBrzxsq2oMH7C6XZq017hcefH85zmcsCHt8X6l7G/viQ2g27zLmglhpwUbp9KHOeEktdQAoAwdU10rzNhpzP3fuOfD98mrpL58aY4PDu7pv8Ti3l4HaemnuBmvPuXOPtCZPGtDJ2vOGyu1lwIxtu43+w9Kth84fvfcd6dge0oS+UluXzR/l+h8QCEjLc6TZ64z7RfvsrZF+/78fniXp7b6HxL1QBqweF/AHjDmoTnmhgBfKAAAA8C5/wPr2YFmVtGSrdFxPa88bKtqDB+SXGOXl2y3G82X7JMZLw7sZ94k6tbQpOIRseY7x4jcrzVonndhfinfAWDJ1ANwuEDCe9bZSRY3xTPuEvtaeN1TUAwfs2GPMGVm0yVhEfJ+EOOmYrkZboHMr++IDwuXaxUMLCwt11llnqaCgQLfddpvuu+8+NW/eXJI0bdo03XXXXUpISJDP59OQIUNsjhZWOOO4q3X8kAskn08pSWnKbtNHLVKz7A4LFqIMuMfsddafs7beaBCe0M/6c5u1r6zX+Wu1OX+53pj5sApLc5SUmLL/M8s3zdJvXjrtiH3r6mvk99fr02n1VoYcFfV+6fV50uItDW8v2it9sNSY2HvNRKl1cyujQziKyo0Hva02f6M0ZYgz3hBGPQDKgGHeBuPtfg0tJFBdZywysGCT9NPx0qBs6+OLpmDKwB9evVj+gF+/m/rm/u/tqSjSVY8O1NVnPqpJwy+zI/SjCvwwCc9q+aXSpl1Sz7bWn9ssN19/M/ZWSy/MlLYWNrx9e5H0nwXGTeArT3DGg14AAAAAEAzGhozxg4+WSZ+vbHh7eZWxbd5G6eoTpS4umvjj9nGB6jrj3o7V9k06DuWt41ZzexkAAADellcsbdxp/XnnrpcmDXDGA2H0CQ3rC6SXvpGqao/c5g8YD/ou2SqdM1ya2N/6+BA6O+aPVtcZ80ed8EAYdQAoA4bSCun5mUbboSFbCo2vpdukn4xzxrzQYLl9bGjpVmNOkNXmrHfO4qFuLwPBmrNeemuh1NB7qKrrjEUf5m+UrpggDXTItQ0GecBQ75feXNj4/YTiCunDZUYZuHai1MZFi8i6vQ4orZC+3279eRduks4YKiU7YJ6p28sAAADwtrX5UmGZ9eeds945i4fSLzQs3Sa9OufAy0QOVltv9BcXb5YuGS2N7G59fAidHfeJSiqMdQ2GdLb+3GZRB8DtNu+S8kqsP+/sdcZL6Xw+689tFvWAYWWO9MpsI+8frs4vLdpstAUuONZ4yRTgRC66xX2om2++WTk5Obrxxhv16KOPHrLtzjvv1GuvvaZly5ape/fuatHCRXd40KhOrXtreJ/JdocBG1EG3KG23pj0Y4dFm52xeOjBZf3YfqdpUPfxuuWv4/XE29fqt5f/R5I0uMcEvf+H8kP2KyzN0w1PjtQ5Y2+0POZICwSMyR6NLRx6sF1l0nNfSb86VUpPOfrnYb9vtxjX2GoVNdLqPGloF+vPbRb1QOPS0tI0btw4paWl2R1KVFEGpO+2SG8sOPrnauulv88yJv/1bh/1sCwTTBm46fy/6urHBuurJa/rpGMukSQ9Nf0GDew+PqYnfeUWGwt52mHRJmcsHurm6x+smrqmFw492Pod0svfGAvKO+GBTwBA6LzSHwAANM4ruYCxIWNh0MYWDj1YeZVxj+CXp0rtXDJtwO3jAitzGl78J9r8AWPM8aQB1p/bLLeXgXB4JQ8AABpGHnCHYOYCRUNJhbFoaR8H3FOmTyht323cK2zoIZDD/e87Y8G4cTwM4gg1ddIyGxYKkoz5o05YPJQ6oHFeaQtQBoyxo+dmSPklR//sihzp1bnG4oFxDnjgMxhuHxtavNme867KNRYtTUu25/xmuL0MBOPbzcaL54+mtt6YN3b9JGfMCwwGecDwzuLgXkS2u1z665fSr6ZIGc2iH5cV3F4HfLfVuGdjtapao90wwgGLCrm9DITDK30CAEDDyAPuYNe4wLbd0o5SqV2GPec3g36h8Rz4P2cfve9Q75f+PVdKTpAGO2BRSEhF5fa8aFIy6h8nLB5KHdA42gLuYFdbYMceaXuR1KWVPec3g3pA2rhDenmWkeubEpCxLlFyojSimxWRwU5uzAOuXBJh9erVeuONN9S6dWv98Y9/bPAzI0aMkCQNHTr0iG3Tp0/X2LFjlZaWpoyMDI0bN04rVwbxZBEAIOrySxp+y4sV8oqDm1AcawZ2G6vJw6dq5rI3tHLL3AY/U1NXrQf+eb4GdRuvSyf9xuIII29LYXCTPfYpLJe+INU7xrbd3jx3OLxYDzSmW7duevHFF9WtWze7Q7GU18pAbb309uLgP1/vl95aZM/CxFZpqAy0SM3SbRe8pKffvVGFpXn65vu39P3GmfrV+c/ZHG3TbM0DRfadOxxuuv7BWrAxuIVD91lXIC2x6SUFAADreLU/AAA4wKu5wGtjQyUV0sffB//5ihrp/SXRi8dubhsX4B6BeW4rA+Hwah4AABjIA+5Ae9A8r/UJJemdb83N8/vft1JlTfTiQeTkFh/9AZ9onruO+aOO5tW2gBfLwDdrgls4dJ/vt0urc6MWju3cNDYUCEhbbWqTBWQ8GOxEbioDwaipY/7owbyYB7btluasD/7zxRXS58ujF4/d3FYHMIfYPLeVgXB4tU8AADCQB9yB+0Tmea1f6A8Y/fxgXzoQkPF5u+49wBw7+2XUAc5HW8AdaAuY57V6IBCQ/msyt7+9yLi3AHdzYx5w5eKhr7/+uvx+vy677DKlp6c3+JlmzYzXwR2+eOiTTz6pCy+8UOPHj9d7772n119/XZMnT1ZlZWXU446mupoKVZYVNvgF9+P6w02229ig9geMBUSd6LLJv1NcXLz+8em9DW5/4u1rVVNbpTsuesXawKJk9jrz+yzcRIPeKeycfOfUiX+S9+qBxtTX16u8vFz19Q6czR8mL5WBZdukvdXm9tmxR9pg01vHrNJQGRjVb4pOGHKhHn79cj31zvW69YIX1SIttl99ZGddXFDi3PaCW65/MAKB0NqDoewDAHAWL/cHAAAGL+cCL40NzV1v/iHflblS8d7oxBML3DQuwD2C0LipDITDy3kAAEAecAN/QMqxsz3o0AdBJG/1CXOLpc27zO1TUy8t2hydeBBZdvbL6v1Sfql95w+Hl+qApni5LeClMlDvl+ZuML/fbBOLzDmRW8aGCsulqlr7zu+29qATy0Awlmw1XhpmRn6J+Takk3gpD0jmFg7dZ9Fme+uXaHNTHWBnXUwecD4v9wkAAOQBN6iskXaV2Xd+t80bOpib+oVr8qTd5eb2Ka2UVuREJx5Elp39spIKqazKvvOHw0t1QFNoCzhfbb2UV2Lf+e2crxIuL9UDm3ZKBSbv61fUSEu3RScexA435gFXLh761VdfSZImTpzY6GdycozW+8GLh27cuFF33HGH/vznP2vatGmaNGmSTj/9dD3wwAMaOXJkdIOOsvlv36cXrmvT4Bfcj+sPNzHbSHPb+UPVqXUvTRx6sZZs+FLLN806ZNv02U9qweoP9MAV7yolKdWmCCOnui60hnlFDYN7TlBVawyw2cWpdYDkrXqgKWvWrNGoUaO0Zs0au0OxnJfKwMJN1u7nFI2VgavPelS5uzdoVL/TdFz/M2yMMDgFJfad2x+w90ZzONxy/YOxbbexILBZWwqlnSHsBwBwDi/3BwAABi/nAsaGmhYISItdvFCMm8YF7Byn313u3BfLuKkMhMPLeQAAQB5wg9IKexdzceqigRJ9wmjuB2vZOV8gFs4fKi/VAU3xclvAS2Vgw47Q5piuyZP2VEY+nljhlrEhu+thN84hdloZCAbtwSN5KQ/U1UvfbTG/X3Wd9P32iIcTM9xSB9TWGwtJ24U84Hxe7hMAAMgDbhDKc0KR5Mb2oOS+fiHjAu5m9++h3eOTofJSHdAU2gLOt2uP8Yy3XZgz4gwLQ3weYBFtAddzYx5IsDuAaNi6daskqWvXrg1ur6ur05w5cyQdunjoyy+/rMTERF111VURjWfkyJEqKCgwtU98YjOd92DkXl86aOLV6n3cBQ1um/6nk8M+fp/evVVfG/psiaSEZnrhRne9rrV3n96qqQvt/8Rp11+iDBwunOuPpo284DF1G3lRg9tunSK1aNb4vi1SDvx5/3lNn2dPpfT4J0d+/867f6eNc/8eZLShi8bvxCWTfqsZS1/XPz67V49eO0OStHTDDL344V166Bcfq31Wt7COHyvlPrVlZ53+63kh7Xv7b36vdd88H+GIEEkpzdvqzHu+a3R7pOqBxuqA3cVlys7uH2S0oYtWXoxmPWBXHXDFFVeY+nx+fr4k6aOPPtKSJUuC2ueVV14xGVX4KAPhOeW2mWrRtpfp/T78bLbuuuDiyAcUAivLQLOkNHXI6qHu7QeHdWyrysDkX36mzI4DGtxmRXvw9DPP1e6ti4OMNnRWtQcjdf2l2KkHOg06XWOmvhDSvqeedaF2bZwb4YgAANFkpk8QSn9AsqdPAAAIDmND5nhibMjn048e2iJfXLzpXZ987h/62bu/jUJQ5jEu0LjzHtyg+MSUBrdF+x6BJPUbOEQ1e6P/+nCnlQHuEcApTv/NIqVmdFB+Qb6ys0fZHQ4sxvV3J8aGvKV521469baZDW47WltQCr89uGVbnrKzjw0q1nDQJwzP6MueU/aQM03vt2FbobKzh0U+IETUqIueUNfhP2pwmxXzBW67825tmv+vIKMNHfNHg+fWtoDTykAs5YFuIy/SyAseM71fQNLYE09TSe7yyAdlEvPGGtd52Hk67pKnGtxmRXvw/Q8/1d0XXRlktKGjDIRnyp2zld6qm+n93v14pm7/8eWRDygETssDUuyUgZTm7XTmPd+GtO9v7ntYa2Y0XMdYjXsEDUtslqlz7l/R6PZo3ycqr6hRdnaPIKMNHXkgeNwnghncIwBlwH2syAMSuSCWtO01Xsdf9Z8Gt1kxLjBv4Xd66KdnBxlt6LhPFJ6J17+rVl1Hmt5v9sIVeuDyKVGICJF0/FVvqG2vcQ1us+I+0aVTr1D+6i+CjDZ0jA0Fh7aA92R1Ga6TbnivwW1WtAW+X75G2VdODjLa0NEWCM+EK/+tdn1OML3fdys3K/uKCVGICNHkhvkC7du31+LFoa1b4crFQ/fu3StJqqxsuFJ54403VFhYqObNm6t79+77vz937lz17dtXr776qh588EFt375dvXv31r333qtLLrkk5HgKCgqUm5trap+E5MiuxJzZvre6DIpeAsrLz1NddQivaP1BSmL0Vp4e2vNEff6I9UuH5+flqao2tP8Tp11/iTJwuHCuP5rW/4c6viEtmkmZQRSluLjgPteQkpJi03V6KEL5nThaWe/arr8+nVa//98FRVv04KsX6qozH9HQnieGEuYhYqXcN68OvXlTUlJiyfVF6FIz6prcHu16wO/3x2wdINlbD9hVB1RUmDtnVVXV/j+D3deOeoEyEJ66uqbrisZUVVXFTB6wqgxEklVloLa2ptFtVrQHd+3aqfwYzQV2Xn8pduqBlA6FIe9buGtXzNQDAIDgmOkThNIfkOzpEwAAgsPY0KEYG5Lk8ykgyRfCruXl5TGT9xkXaJzf71djS8NaMTaUn5erqvLoLx7qtDLAPQI4RX19/f4/ubbew/V3J8aGvKWlP73RbcG2BaXQ24P1dXXMGWlEzPQJJVVUhhaHn/zgCANtnj9aXFwUs/WAV+ePurUt4LQyEEt5IKNnccj77ty5Q7tiIBcwb6xx6V12N7rNivZgZWVlzOYByRtlIBhenT/q1bbA4VIzQy/je/aUeqoMRJJV1z85tfH+gMSzJF4oA4fjPhHM4B4BKAPuY0UekMgFsSQuc1ej26wYF6iprnZVe9Ct44PV1dUh7VdbW8PvuwNUV1c1us2K+0S7Cwtjth7w4tgQbQHvqU/JbnSbFW0Bq3IFbYHwVIXYFqirreX33YHcOl8gWK5cPLR9+/YqLi7Wd999pzFjxhyyLT8/X3fccYckaciQIfL5fIdsy83N1d13362HH35YnTt31ksvvaRLL71Ubdq00eTJoS2+2L59e9P7xCceZTnrGNOxQ0fV14a+AnRSgrN+3mB06Ngx5FWxnXb9JcrA4cK5/mhaUhOZa89R/stbpBgNeb9f2tP42ECTx0pPTVKnTp2a3jkCov07UVVTofteOVdjBpytc8fdGJFjxkq5j09MVH1dteITkk3vm6S9llxfhC4hKa3J7ZGqBxo7jr+u0hV1gBT5esCuOiA11dwASEpKyv4/g93XjnqBMhCe2vIdkvqZ3s9ftTtm8oAT28dWlQGfv/HFQ61oD7bMSFOcS3JBpMVKPdAs3hjcDQQCh4z7NGXfZ9MS62KmHgAABMdMnyCU/oBkT58AABAcxoZC5+axocrSfKW1bHyCWGPi6vbETN5nXKBx/tpKqZGXcUb7HoEktWnVUv6M6F8fp5UB7hHAKeLj4/f/ybX1Hq6/OzE25C3NMpo3uu1obUEp/PZgwF/NnJFGxFKfUNWhLRpXVVbA77sD2D1/tHlqiivqATfNH3VrW8BpZSCW8kCyL7QHEwN+v5onB5QUA7nAaeNCknVloHla43PDrWgPJsYHXJEHoiGW6oGa8p1Su16m9/NXFcVMe9BpeUCKnTLgi09UXU2lEpLM/x8m+GPnWRKn1QNWXf+4+KQmt0f/WZIq8kAjnNAfkLhP5HXcIwBlwH2syAMSuSCWZDZv/LpZMS4QJ2ueN+I+UXjqKwtD2q9u7y5+3x0g3tf4CxGsuE+U0TzVFfWAW8aGaAt4T4uMxtcZsaIt4AvUuqIOkNzdFvBXNv4iuqbU7t3J77sDuWG+QChrU+7jysVDJ0+erNWrV+vhhx/WySefrD59+kiSFi1apKlTp6qw0GjwDxs27JD9/H6/ysvL9a9//UvnnnuuJGnSpElatWqVfv/734e8eOjixYtN71NdJ931Rkins8W69euVHEZpqq+RZjwZuXhiwfp163WUe1KNctr1lygDhwvn+qNpc9ZL/13Y8LbHP2l63/vPM94AsKdKun96aOd//aXH1aXV46HtbEK0fydmLX9bm/KXKbdwnWYuO7LCeen2VWrbsoupY8ZSuX91rrR4s7l9UpOkb957rskJxogN90+XShqZ3xntemBYn/Z6PifH/I4mWZEXI10P2FUHrFmzxtTnV65cqZdfflmnn366Bg4cGNQ+Dz74YCihhYUyEJ5vN0v/mmt+v8d+fZF6/vmiyAcUAie2j60qA28skOZtaHhbtPNAnE9aOv9zJcab39csykDoAgHp4Q+lgtLgFg6VJJ/Pp+5tpDXLZkUxMgBANJjpE4TSH5Ds6RMAAILD2FDo3Dw29PH30qfLze0T55PefuFuZabeHZ2gTGJcoHFPfS5t3NnwtmiPDbVKl7ZtaWRgKsKcVga4RwCnuO8dqbRS6tC+g3IsuOeH2ML1dyfGhrwlEJDu/q9UVXvktqO1BaXw24MTRvbWy8wZaVAs9QnziqVpH5nf76pzh+qfd5EfYt2stdLbjUzLt2L+6Jv/eELZWU+EtrMJzB8NnlvbAk4rA7GUB/x+6ff/k4pNriE6MDtOT6xZEp2gTHLauJBkXRkoLJMefK/hbVa0B6+74jy98+h55nc0iTIQnoWbpNfmmd/viXsuVfcnLo18QCFwWh6QYqsM/Ge+NH+juX2SE6QZ7zyh5MTot/WC4bR6wMrr/+B7Rj5oSLT7BP26ZuqvLhkXiDQn9Ack7hN5HfcIQBlwHyvygEQuiCVVtdKv32x4mxXjAhecOUGv3++O9qCbxwdX50nPzzC/3z3XnaohD5EfYt37S6QvVzW8zYr7RLM+e1PpKaHtawZjQ8GhLeA9dfXSXW9K9f4jt1nRFjjjpBH6129oCzQkltoCm3ZKT35ufr9bfzJer91HW8Bp3DpfIFiuXB7rzjvv1Guvvabt27dr4MCB6tevn6qqqrRhwwaddtpp6tatmz799FMNHTr0kP2ysrIk6ZBFQn0+nyZPnqxXXnnFyh8BANCIzln2nTvOJ3XItO/8kXTyiKk6ecRUu8OImvF9zC8eelxPsXCoQ3TOanzx0GjLtrEOijS31wON6dOnj+bMmaPmzZvbHYrt3FwGhnaRpn8rlVcHv0/7DKlH2+jFFOseu26m3SEErXOWFMLc3ohonylLFg61mpOufzB8PqM9+NYic/uN7x2deAAAsYP+AACAXHCAm8eGxvSSPl8h+QPB7zMo25gQ5kVOGxfonNX44qFWnNuNnFYGwkEeAABvIw84n89ntMnW77Dn/G5qD7q5T9ixpdSjjbRpV/D7JCVIo3pELyZEjp1zt+LjjLklbuDmOqAptAUOcHMZiIuTxvaWPlxmbr/xfaITjxM4aWyoVbrULEmqrLHn/J1b2XPeaHNSGQjGMV2ld7+VKkyUk44tpW6toxdTrHFzHpCkcX3MLx56bA8pOTE68cQ6p9UBnbMaXzw06ucmDzgefQIA8DbygPOlJEptmku77GoPcp/IEfp2kFqnS4Xlwe+TmSoN7BS9mBA5dvbLMlNlycKhVnBzHdAU2gLOlxAvdciQcortOT9tAWfo3sZYFyq/JPh90pKNtSngbm7MA3F2BxAN2dnZmjVrls444wylpKRoy5Ytyvr/9u47Poo6/+P4eze9kgRCDb33Lv0AqaKAemJDFE/lrFiw3J2e5aenp556dlHsBblD8exYAAVEBaUJKr0TEAIhhISU3d8fI6Gl7G52Z3ZnXs/HYx+Q7M7MZ5kP8/nOfL/znYwMTZ06VR999JHWrFkjSSdNHlrZjLCFhYUhjRkA4Jv6adZN2NQg3Z6TRdlR45rGzcG+ykyRhvo+MTws1tjCwVlWbhvBERMTo4yMDMXEOHSEl0NER0njTpFcPn4+yv37531dAJaytA7YdOCfHfVqbtwU6Ks29aQujUMXDwAgPHA+AACgFjhDWqI0qnPVnzsiKU4a3TV08SC4rLw21IhrQxGPOgAAzkYdsAcr22S0ByPH2T2kOD8eIn12d+OmY4S/BunGGA8rZKUb41EQuWgLOMeA1sbxwlddGklt6ocuHgSPy2Vdm+zIRPYIfzFR0jk9ff98tFsa15Pxo3bSMMOoBb6qmSwN7xC6eBBcVo7j5bpA5OOcAACcjTpgD5a2B7m3OCK4XdK5vYw/feGScV+pVX0P8I+VxwDuK418tAXswcp6TFsgMrhcxjX/aD9q+7iezCXlBHasA7ZtwrZt21Yffvih8vLylJeXp++++06TJk1Sfn6+Nm3aJLfbrQ4dju/ZGTt2rCTps88+K/udx+PR559/rp49/eg5BACETHSUdZP69GxmzXbhP5fLGPRzig/7rHaqdNWpxs3BiAzdm1gzQCspTmrLANGIt2XLFl199dXasmWL1aEgxDo3ki7oU3VHT2y0dNkfpOa1zYkL1Vc/zXhZoWdTa7YL/8VESZcP9G0C0Tb1pEsH0NELAE7A+QAAgFrgHEPaSSM6Vv251HijjyDTPg+Qtb32DaSEWPO363YZ/ROIbNQBAHA26oA99LCovy49UWpBn3LEyMqQJg2WEqoY7++SdFZ3qbcfD6mGtWKjjUn+rMD40chHW8A54mOkKwcb9aAqnRtK4/v6PpkArGfV+K32DRhrHkm6NZHO7131/+24aOmygVJTPx5SjchwVjeprw/t/MwUo58oJSH0MSE4ujaxpm7Hx0gdsszfLoKLcwIAcDbqgD1YdZ22cU2pTqo124b/WtWVJg6o+oFgUW5pQj/jug8iQ3qS1KKONdumnyjy0RawB6v6COrWMB42icjQrLZx7T+2iofOul3SBb2tm8MK5rJjHXDctAirVq2S1+tVy5YtlZiYeNx7o0eP1oABAzRp0iS98MILmj17ts477zytWrVKd955p0URV09Wu0G6/g2vup9+c4Wfuf4Nr8be/KGJUcEs7H/YVf+W5m8zNorJoiJNlNtoqF/2B6l1vZPfr5Uije0m3ThCykg2Pz4ELj3JmouxvZrzxAg7yMvL09y5c5WXl2d1KDDBKc2km0+Tepfz/zc+RvpDa+mWUVI7Ongiissl9Wtl/nbrpzFIONIkxklXD5HO71X+hfnGtaTxfaQrBklx9nlQEACgEpwPAACoBc7hckmndZKuHWo8ZObEmwhTE4zJRW8e5dskAggfsdG+PTwu2Do2lGokVv05hDfqAAA4G3XAHuqlWfNgyL4tJbfjRlxHtua1pVtPNx4uUd5EXz2aSjeMkAa2MT82VE9/C8YLxEVbN3kxgoe2gLOkJEiTh0nn9DRu6DxR89rSxf2kSwYwLjTSdGlkzSSe/Sy4fwHV07u5dNNIY/z3iZOFJMQa7cBbRklt61sTH0LL7ZbGnWKMDWxb33hwwLFqJktjuko3jjTuK0HkqJEgdWpo/nZPaWacFyCycU4AAM5GHbCHlnWteUi0FfexoXo6NTTO+we0Kr8t37elcd9ptyamh4ZqsmKekfREqR3XkCIebQF7aFJLamDBJJ79Wxlj0xE52taXbh1l9AUkxJ78fu/m0pTTjD4EOIMd64DjLlmvXLlSktS5c+eT3nO5XHr//fd122236W9/+5sOHDigzp076+OPP9app55qdqgAgAo0riU1rSVt3GPeNns1L79BiPDmchk3c3ZsKO09KD3yiXSoyBg09rfRPCk8kg1qI/20zbztRbkZ+AdEqvrpxhPkx3SVtu+XioqNiUOzajKIK5L1aCJ9vFzKP2zeNge24eJuJIqOknq3MNrzO/dLuQXG79OTyr85BAAAAABgLy3qGK/cAmlXrlRcKiXGSo1qGtd9EZkGtJIWrJFKPeZtc2Br87YFAACAyg1qI63fbd724qKN/iZEnvQkaXRXaWQnaeteadpXxtixlHjpor5WR4dANallnNdv2WveNnu3MMaaAIgssdHGzZz9Wkrb90kHCozJ5GomSZmpVkeHQEVHGft19krztlknVWpdz7ztIXiyMqQLfh8/umP/0fGjDWsaxwjYm8sltW9gvHIOSr/lGf0KyfFGbnAvSeQa2EZatsW87bldRt8UAAAArOd2Ge3BmYvN22ZqgvEwE0SeOqnSH3tKp3eRtuVIL31t9BOlxkvnnmJ1dAhUx4ZGH+C+fPO2OaA1D5oEwoXr97bAW4vM22ZiLA+ajFS1UqSzukundzbGjLx4TFvg/N5WRwdUn+O6uiqbPFSS0tLSNHXqVE2dOtXMsAAAfjq3l/SvT8y5KTAtURpVftlABKmZfPTp4NFuBntEuhZ1jKe3fr/BnO2d1snIIQCRKzFOalnH6igQLHEx0h97SK8tNGd7LepIPZuZsy2EhstlTCZc34KnigEAAAAArFcjwXg5xdPvTdai1e9r177NevaGpWrRoEvZe8NucalJ3Q66fNSD6tV2lN788h/6evl/y97Pztmg0065XFeOeVQrN8zXU+9dpw07l2vW/+1TckKa+V+mHLVSpOEdpE9WmLO9Pi2kZrXN2VYw+LP/DxcX6N8z/6x123+UJNWt2UxTxr2otOTMsN3/AAAAHbKkjlnSSpMeOjumqzHZJCJXTJTRpj8ydoxxY5HN5ZLO62U8SNzjDf320hONsWMAIpfLZUwSB/sY0k5aulnafSD023K5jBtIaT9EtiTGjzpeRrLxcoKi4kL9483ztXnXasXFJCgtubYmn/2sGtQynopx0f1NFBMdp3EDb9GoXpcrO2eTHp4xUet2LFXd9KaaetOysnWFaz9B00ypbwvpm3XmbG94h8iZeNzf/b903Ry9+PFfVHD4oFwul3q1OV2Xjfqn3G63duxZr3te+6O27F6tJ6/7/rj+JgAAACv1bSEt3iht3mPO9s49hQdQRLr4GOPewCP9RC6u80S0KLd0fi/p2TnmbC8r3ZioEED46NFUWrxBWrvLnO39sWdkPWjytueHa19etlwutxLjU3TN2CfUokFXSSePIZakN7+4T7MXvyxJGtTlfP3ptH9IkuYtm6HXP79Hew/s0Hv37rfkuwRLbLTUnLYAbMhxpylVTR6K8FFVZ8Wxvl39oZ7/8GaVekvVtG5H3XLeK0qKT5XX65XL5dJDb0/Uree/UvZzZaY8O0i7923W8J6XasKwOyVJu/dt0ZOzrtG2PWvkdkVpdJ+rdGb/6+gECSH2P6pSL00a2VH6aLnvyxwoOP5PX53XS0qI9W8ZK7315f36/IdXtX3PWt118bvq1+HMsvdO7Oj1eDx64aNbteTXT1XqKVH7Jv00+exnFRMdS44j7J3ZXfp1p5Trx//pQI4DjWpKg9v6F1s4+3rFTC1d+6WuGvOYz7UW9vf1ipn67uePdLBgHzkRgfyp/ZUN8ou0dkHXxtLyLdLyrb4vE0gdiI2WLgjzAeD+5EBlA/2kis+vwjEHAAAAAADBwbUh+xnQ6RydO+hW3fhM/3Lff+zq+WU3eI4fcrvGD7ldklRUcljn31tfp3YbL0nq2GyApt60TMNuCb8LI0PbG5NFbcvxfZlArg2lJ0pju/kXm9X82f8ffTtVh4sP6fkpK+VyufTof6/Qf+c9rCvOeCis9z8AAHA2l0sad4q0frd0qMj35QJpD7asI/Vt6V98Vnp4xqVatWmh4mISFB+brKvH/lutG/aUdPLYyJ05G3Xva+eo1FMqj6dEDeu01Y1/fF4pien0DSLsNUiXhneUPvXjoRKBjh89v3dk3QxWFcaOoTxcH7QPf9oCi3+drWkf3Va27P783cpIqatnb/hRh4sLNPnJPtqxd53+csEbx41FCgex0dKFfaTHP5O8fkwkHUgtGNTGmKQuUviTA5L09twH9fmSVxUdFavYmHhdM/YJtWl0StjnAIKLOmA/o3pN0iltTpPL5dJ7C5/So/+9XI9cNa/s/dvHzyg7z0uMT9WlI+9TfmGuXvrk9uPWE879BGO6ST/vlPbl+75MIHWgQbo0rIN/sVnNn/2fkpCu28e/rXo1m6mouFC3Pj9Un//wmkb0nKj6tZpr6k3LdNH9TSz5HgAAABVxu6ULe0sPfyyVeHxfLpD2YI+mxkPtIsXDMy7V2m0/yOVyKzoqRpeN+qe6tRwi6eT7yyTpk+9f1Ntz/ymvx6MuLU7V5LOfUXRUTNg+SAA4onU944Hgi/x4qEQgx4Aot3EdMsrtX3zhjH4i2IH794d+PfiRVFTi+3KBHAc6NZS6NfYvPqv9fcJ/ymr3gpWz9PCMiZp609GJmY4dQ7xiw9eau2y6pk5ZoSh3tG54up/aN+mrXm1P16Au56lNo1668rEu5n8JAD5x3OShc+aYNH08gqKqzgpJKjh8UI/89zI9ctVXalS7jZ6cda3e/OJeTTrjYb3x+f8pNamWPJ4SzVn6llZt+kbXnfVUldu9csxjZZ3bXq9Xd796ls4b/BcN7DxOkrQvz5h+nE6Q0GL/oyqntpM27ZFWbfft849+6v82hrWX2tb3fzkrdWs5VIO7nK9//edP5b5/bEfvp4tf1LrtP+qZG35UdFSMHps5SbMWPK5zB91CjiPsJcZKl/Q3ng5UXOrbMv4eB1LipYv72evC3sKfZmlo94sl+VZr7aZOnTq67bbbVKcOjxA/1sKfZmlgp3MVFRXjuJywA39qf2WD/CKtXeBySef2krJzpV0HfFvG3zrgktGhXDPMnzjvTw5UNtCvsvOrcMwBAAD8xfkAAIBaUD6uDUWmYbe4dOGQ2/Xdzx+psChfE4bdpSG/T/rZqdkfAlrnNz+9p8waDdUqq3swQw2JKLdx/f6Jz6SDh31bxt9rQ7FR0iUDwnOSmODtf5cKiw+ppLRYbpdbBUUH1bRex9AEHQaoAwDgbNQBe0lNkCb0k6Z9JZX6eGOov+3B9CRpfF+jXzJS9Otwlm465wVFRUXr29Uf6t7Xx+mNv20qe//YsZE1U+vrsWsWKC4mQZL09P+u12uf361rxj5O3yAiwrD20uY90s87fPt8IONHR3Q0bkC1EyePHaMtUDGuD9qHP22Bnq1HqGfrEWXv3fHSGercfLAkKS4mQVNvWqYpzw4yMXr/NKkljekq/e9H35fxtxY0ry2N6uzfMlbzJwfWbV+mD755RtNuXqWEuGR98cMbeuq9a/XU5O8jIgcQPNSByFRRP0FsTLx6tR1V9rm2jXpr5lf/qnA9qYkZ6tC0v5avn2dC1METHyNN7C89/aXvk0T4WweS4qSL+4fnvSTB2v8tGnQt+3tsTLya1++iXfs2hTJ0S3FOAADORh2wlzo1pPN6SW8u8n0Zf9uD9dOls3v4t4zVrhrzWNlkYOu2L9WtU4do5t175HYbjdpj7y/bmbNRr8z+u569/kelp9TRna+M1UffPq+x/a4J6wcJAEec2U3avk/aste3zwfST/THHsaxwE7oJ6ItYBc1k6XxfaRXFvj+kDF/jwO1U6VzTwnPMSOVjSE+dtLv/MJcGXfKl2/eshka2m2CEmKTJEkje/5Jc5dOV6+2p4cyfMASdqwDjps8FJHD186K73/5RC3qd1Wj2m0kSWP6Xq2/vDBck854WBOG36UPvnlWXy59U7XSssomjpyz9C3Nmv+4ikuL5PV6NHHkferTbnS5cSxd+6ViouPKJo6UpPQU+xwEwhX7H76IchsTB770tfTLzuCvf2Cb8B/ws3rTIj3/0S0qOJwnr9eriSPuVd8OY31efv2O5eracqhiomMlST3bnKbXP7tb5w66JVQhA0HVrLZ0+UDjZhBfJxD1VUq8dNWpUq2U4K431A4W7NcVj3TQ4eICZdZoqOLSw8reu0FDuk/Q9Wc/q1WbFuqW815RdFSMXwND7KJWrVqaOHGi1WGYqrKcmDJumkpKi4/LiyOckhORprq1v7JBfpHYLkiKk64aIj3zpbTbxwlEfeVySef3krqE2VOhqpsDlQ30q+z8CgAAO3Di+QAA4HhOrAVcG7I3l1x67sal2rl3g655vIfaN+mnuhlNAl7fJ9+/qNNOuSx4AYZY7VTj2tCzX/o+gaivYqON/ocmtYK73mAKxv4/o/eftXrzNxp3T2253VFq06iXxva9NjQBhwEn1gEAwFHUAftpW98YO/bqAt8nEPVVeqJ09RApLTG46w2mqvoN2zburT2521VaWqKoqJOHi8dGx5X9vdRTqsKifCXEhvlTFYFjRLmlSwcY48bWZAd//ae2lUZG4LMVGDtWMae2Bbg+aF/VbQsca0/uDi1d+6WmnPtSqMMOqsFtpeIS6eMVwV9300zpikFSTFTw1x0s1c0Bl8ulEk+x0Q6MS9bBwv2qVSPLzK8AE1AH7M2XfoJZCx5Xn/a+jzGNJI1rGcfqF+b5PoGor5LijHtJ6qQGd73BFOz9n3MgW/NXzNS9f/owBNGGB6eeEwAADNQB++nZzGgHzlws+ThnmM/q1ZCuGiwlxgZ5xUFU1XUBY7Kwis1fMVN92o1RRmpdSdIZva/U9Dn3a2y/a0IaNxAscTHSnwdLz86RtuUEf/1ndpf6tgz+ekONfqKK0Rawn86NpAt6S9O/9X0CUV9lphhjRpLjg7veYKrs2tCD0y/W8vVzJUn/uOzjCtfx2/4t6tC0f9nPddKbaN6yt0MaN2AVO9YBJg9FxKios2L3/i2qk350Npc66U2Uc2CnSktLNH3OA0pOTNeQruPVtG5HPf2/63XN2MfVo9UIDe5ygVwul7JzNmnyk73V/fbNxw2GPGLz7tWqkZSpf7xxvrb+9qvqpjfRn0c/ono1m4X0++J47H9U5MiNe+8skRatC846o9zS6Z2NAUXh+BSAIw4cytFdr56pOyfMVMdmA+TxeHSwcL9f62iZ1V0ffTtVY/tdq7iYBH29/D+2fkok7Kl1PePk+7WF0r784KwzK126ZIBxYh9pkhPSdGqXC5UQl6KLhv1di3+drelz7teUcdO05NfP1K5x3+MGdx1h54FBx8rNzdWiRYvUp08f1ahRw+pwTFFZTkjSsnVzy80Lp+REJAlG7a9MpLYL0hKlycOMOhCsG4ISY42Lxh0bBmd9wRLsHDhxoF9l51dV3UQAAEAkcOL5AADgeE6sBVwbsrfTel0uSapXs5k6NvuDVm74OuDJQ3ft26xVmxbo9osia9BXg3Rp8nDplfnSjv3BWWfNZOnifsZNp+EsGPt/yZrP5PF69J87s+VyufXwjIl69bM7denI+0IQsfWcWAcAAEdRB+ypU0PjprA3vpEOFARnnU1qGZOSpicFZ32h4Eu/4az5j+uUNqMq7ecrLinStU+cot37N6tpvU66d+L7IY4cCK7YaGnSIOPm8G/XB2edUW5pdFdpYOvwHj9aEcaOVcypbQGuD9pTsNoCR3y25BWd0maU0pNrhyji0Bne0bh5d9YPUnFpcNbZtbF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+ "text/plain": [ + "
" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display the circuit\n", + "circuit.decompose().draw(\"mpl\", style=\"clifford\", fold=-1)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ParameterView([ParameterVectorElement(a[0]), ParameterVectorElement(a[1]), ParameterVectorElement(a[2]), ParameterVectorElement(a[3]), ParameterVectorElement(a[4]), ParameterVectorElement(a[5]), ParameterVectorElement(a[6]), ParameterVectorElement(a[7])])\n", + "ParameterView([ParameterVectorElement(c1[0]), ParameterVectorElement(c1[1]), ParameterVectorElement(c1[2]), ParameterVectorElement(c1[3]), ParameterVectorElement(c1[4]), ParameterVectorElement(c1[5]), ParameterVectorElement(c1[6]), ParameterVectorElement(c1[7]), ParameterVectorElement(c1[8]), ParameterVectorElement(c1[9]), ParameterVectorElement(c1[10]), ParameterVectorElement(c1[11]), ParameterVectorElement(c1[12]), ParameterVectorElement(c1[13]), ParameterVectorElement(c1[14]), ParameterVectorElement(c1[15]), ParameterVectorElement(c1[16]), ParameterVectorElement(c1[17]), ParameterVectorElement(c1[18]), ParameterVectorElement(c1[19]), ParameterVectorElement(c1[20]), ParameterVectorElement(c2[0]), ParameterVectorElement(c2[1]), ParameterVectorElement(c2[2]), ParameterVectorElement(c2[3]), ParameterVectorElement(c2[4]), ParameterVectorElement(c2[5]), ParameterVectorElement(c2[6]), ParameterVectorElement(c2[7]), ParameterVectorElement(c2[8]), ParameterVectorElement(c3[0]), ParameterVectorElement(c3[1]), ParameterVectorElement(c3[2]), ParameterVectorElement(p1[0]), ParameterVectorElement(p1[1]), ParameterVectorElement(p1[2]), ParameterVectorElement(p1[3]), ParameterVectorElement(p1[4]), ParameterVectorElement(p1[5]), ParameterVectorElement(p1[6]), ParameterVectorElement(p1[7]), ParameterVectorElement(p1[8]), ParameterVectorElement(p1[9]), ParameterVectorElement(p1[10]), ParameterVectorElement(p1[11]), ParameterVectorElement(p2[0]), ParameterVectorElement(p2[1]), ParameterVectorElement(p2[2]), ParameterVectorElement(p2[3]), ParameterVectorElement(p2[4]), ParameterVectorElement(p2[5]), ParameterVectorElement(p3[0]), ParameterVectorElement(p3[1]), ParameterVectorElement(p3[2])])\n", + "ParameterView([ParameterVectorElement(a[0]), ParameterVectorElement(a[1]), ParameterVectorElement(a[2]), ParameterVectorElement(a[3]), ParameterVectorElement(a[4]), ParameterVectorElement(a[5]), ParameterVectorElement(a[6]), ParameterVectorElement(a[7]), ParameterVectorElement(c1[0]), ParameterVectorElement(c1[1]), ParameterVectorElement(c1[2]), ParameterVectorElement(c1[3]), ParameterVectorElement(c1[4]), ParameterVectorElement(c1[5]), ParameterVectorElement(c1[6]), ParameterVectorElement(c1[7]), ParameterVectorElement(c1[8]), ParameterVectorElement(c1[9]), ParameterVectorElement(c1[10]), ParameterVectorElement(c1[11]), ParameterVectorElement(c1[12]), ParameterVectorElement(c1[13]), ParameterVectorElement(c1[14]), ParameterVectorElement(c1[15]), ParameterVectorElement(c1[16]), ParameterVectorElement(c1[17]), ParameterVectorElement(c1[18]), ParameterVectorElement(c1[19]), ParameterVectorElement(c1[20]), ParameterVectorElement(c2[0]), ParameterVectorElement(c2[1]), ParameterVectorElement(c2[2]), ParameterVectorElement(c2[3]), ParameterVectorElement(c2[4]), ParameterVectorElement(c2[5]), ParameterVectorElement(c2[6]), ParameterVectorElement(c2[7]), ParameterVectorElement(c2[8]), ParameterVectorElement(c3[0]), ParameterVectorElement(c3[1]), ParameterVectorElement(c3[2]), ParameterVectorElement(p1[0]), ParameterVectorElement(p1[1]), ParameterVectorElement(p1[2]), ParameterVectorElement(p1[3]), ParameterVectorElement(p1[4]), ParameterVectorElement(p1[5]), ParameterVectorElement(p1[6]), ParameterVectorElement(p1[7]), ParameterVectorElement(p1[8]), ParameterVectorElement(p1[9]), ParameterVectorElement(p1[10]), ParameterVectorElement(p1[11]), ParameterVectorElement(p2[0]), ParameterVectorElement(p2[1]), ParameterVectorElement(p2[2]), ParameterVectorElement(p2[3]), ParameterVectorElement(p2[4]), ParameterVectorElement(p2[5]), ParameterVectorElement(p3[0]), ParameterVectorElement(p3[1]), ParameterVectorElement(p3[2])])\n" + ] + } + ], + "source": [ + "print(feature_map.parameters)\n", + "print(ansatz.parameters)\n", + "# Note that parameters are ordered alphabetically\n", + "print(circuit.parameters)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Our observable is the Pauli-Z operator on the last qubit. " + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "observable = SparsePauliOp.from_list([(\"Z\" + \"I\" * (num_qubits - 1), 1)])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Before we optimize the circuit for a run on quantum hardware, we test the code on the simulator for small problem sizes. We define functions to run and train the circuit below." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load the data and do a forward pass\n", + "We write a function to do one forward pass of the quantum neural network. Then we test it with a small batch from the dataset and randomly initialized ansatz parameters." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "from qiskit.primitives import BaseEstimatorV2\n", + "from qiskit.quantum_info.operators.base_operator import BaseOperator\n", + "\n", + "\n", + "def forward(circuit: QuantumCircuit,\n", + " input_params: np.ndarray, \n", + " weight_params: np.ndarray, \n", + " estimator: BaseEstimatorV2,\n", + " observable: BaseOperator\n", + ") -> np.ndarray:\n", + " \"\"\"\n", + " Forward pass of the neural network.\n", + " \n", + " Args:\n", + " circuit: circuit consisting of data loader gates and the neural network ansatz.\n", + " input_params: data encoding parameters.\n", + " weight_params: neural network ansatz parameters.\n", + " estimator: EstimatorV2 primitive. \n", + " observable: a single oberservable to compute the expectation over.\n", + "\n", + " Returns:\n", + " expectation_values: an array (for one observable) or a matrix (for a sequence of observables) of expectation values.\n", + " Rows correspond to observables and columns to data samples.\n", + " \"\"\"\n", + " num_samples = input_params.shape[0]\n", + " weights = np.broadcast_to(weight_params, (num_samples, len(weight_params)))\n", + " params = np.concatenate((input_params, weights), axis=1)\n", + " pub = (circuit, observable, params)\n", + " job = estimator.run([pub])\n", + " result = job.result()[0]\n", + " expectation_values = result.data.evs\n", + "\n", + " return expectation_values" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Below is an example forward pass with two images from the dataset and randomly initialized ansatz parameters." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.03532339 0.13862284]\n" + ] + } + ], + "source": [ + "from qiskit.primitives import StatevectorEstimator as Estimator\n", + "\n", + "\n", + "np.random.seed(42)\n", + "\n", + "result = forward(circuit=circuit, \n", + " input_params=np.array(train_images[:2]), \n", + " weight_params=np.random.rand(len(ansatz.parameters)) * 2 * np.pi, \n", + " estimator=Estimator(), \n", + " observable=observable)\n", + "print(result)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Computing gradients with the parameter shift rule\n", + "We implement one example gradient evaluation of the expectation values using the [parameter shift rule](https://doi.org/10.1103/PhysRevA.99.032331), to be used with gradient-based optimizers. \n", + "When using a gradient-free optimizer such as COBYLA, we can omit this function." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "def param_shift_estimator_gradient(circuit: QuantumCircuit,\n", + " input_params: np.ndarray, \n", + " weight_params: np.ndarray, \n", + " estimator: BaseEstimatorV2,\n", + " observable: BaseOperator\n", + ") -> np.ndarray:\n", + " \"\"\"\n", + " Compute the gradients of the expectation values using the parameter shift rule.\n", + "\n", + " Args:\n", + " circuit: circuit consisting of data loader gates and the neural network ansatz.\n", + " input_params: data encoding parameters.\n", + " weight_params: neural network ansatz parameters.\n", + " estimator: EstimatorV2 primitive. \n", + " observable: a single oberservable to compute the expectation over.\n", + "\n", + " Returns:\n", + " weight_gradients: list of expectation value gradients with respect to weight parameters.\n", + " Columns correspond to weight parameters, and rows to different inputs.\n", + " \"\"\"\n", + " num_samples = input_params.shape[0]\n", + " num_weight_params = len(weight_params)\n", + "\n", + " weights = np.broadcast_to(weight_params, (num_samples * num_weight_params, num_weight_params))\n", + " inputs = np.tile(input_params, (num_weight_params, 1))\n", + "\n", + " weights_plus = weights.copy()\n", + " for j in range(num_weight_params):\n", + " for i in range(num_samples):\n", + " weights_plus[j * num_samples + i][j] = weight_params[j] + np.pi / 2 \n", + "\n", + " weights_minus = weights.copy()\n", + " for j in range(num_weight_params):\n", + " for i in range(num_samples):\n", + " weights_minus[j * num_samples + i][j] = weight_params[j] - np.pi / 2 \n", + "\n", + " params_plus = np.concatenate((inputs, weights_plus), axis=1)\n", + " params_minus = np.concatenate((inputs, weights_minus), axis=1)\n", + "\n", + " pub_plus = (circuit, observable, params_plus)\n", + " pub_minus = (circuit, observable, params_minus)\n", + "\n", + " job = estimator.run([pub_plus, pub_minus])\n", + "\n", + " result_plus = job.result()[0]\n", + " result_minus = job.result()[1]\n", + "\n", + " expectation_values_plus = result_plus.data.evs\n", + " expectation_values_minus = result_minus.data.evs\n", + "\n", + " weight_gradients = (expectation_values_plus - expectation_values_minus) / 2\n", + " weight_gradients = np.array(weight_gradients).reshape((num_weight_params, num_samples)).T\n", + " \n", + " return weight_gradients" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We compute the gradients for all ansatz parameters for two images from the dataset." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(2, 54)\n" + ] + } + ], + "source": [ + "from qiskit.primitives import StatevectorEstimator as Estimator\n", + "\n", + "np.random.seed(12345)\n", + "\n", + "input_params = np.array([train_images[0], train_images[1]])\n", + "weight_params = np.random.rand(len(ansatz.parameters)) * 2 * np.pi\n", + "estimator = Estimator()\n", + "\n", + "weight_grads = param_shift_estimator_gradient(circuit, input_params, weight_params, estimator, observable)\n", + "print(weight_grads.shape)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Testing our gradients\n", + "We can test our gradient function implementation with the help of [qiskit-algorithms](https://qiskit-community.github.io/qiskit-algorithms/index.html) package. Note that qiskit-algorithms is an external package in [Qiskit Ecosystem](https://qiskit.github.io/ecosystem/)." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "True\n" + ] + } + ], + "source": [ + "from qiskit.primitives import Estimator # EstimatorV1 due to Qiskit Algorithms\n", + "from qiskit_algorithms.gradients import ParamShiftEstimatorGradient\n", + "\n", + "# Define the estimator\n", + "# Qiskit Algorthms currently supports EstimatorV1\n", + "estimator = Estimator()\n", + "# Define the gradient\n", + "gradient = ParamShiftEstimatorGradient(estimator)\n", + "\n", + "num_samples = input_params.shape[0]\n", + "num_features = input_params.shape[1]\n", + "\n", + "weights = np.broadcast_to(weight_params, (num_samples, len(weight_params)))\n", + "params = np.concatenate((input_params, weights), axis=1)\n", + "\n", + "# Test for an arbitrary sample\n", + "sample_idx = 1\n", + "\n", + "# Evaluate the gradient of the circuits using parameter shift gradients\n", + "pse_grads = gradient.run(circuit, observable, [params[sample_idx]]).result().gradients\n", + "pse_weight_grads = pse_grads[0][num_features:]\n", + "\n", + "# This prints true if our implementation is the same\n", + "print(np.allclose(pse_weight_grads, weight_grads[sample_idx]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Loss function\n", + "Now we define the loss function that will be minimized during training. For this example, we implement the mean squared error (MSE) loss function." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "def mse_loss(predict: np.ndarray, target: np.ndarray) -> np.ndarray:\n", + " \"\"\"\n", + " Mean squared error (MSE).\n", + "\n", + " prediction: predictions from the forward pass of neural network.\n", + " target: true labels.\n", + "\n", + " output: MSE loss.\n", + " \"\"\"\n", + " if len(predict.shape) <= 1:\n", + " return ((predict - target) ** 2).mean()\n", + " else:\n", + " raise AssertionError (\"input should be 1d-array\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Below is an example run of the loss function." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "True labels: [-1 -1]\n", + "Predictions: [0.03532339 0.13862284]\n", + "Loss: 1.184178242451296\n" + ] + } + ], + "source": [ + "from qiskit.primitives import StatevectorEstimator as Estimator # back to EstimatorV2\n", + "\n", + "batch_size = 2\n", + "train_images_batch = np.array(train_images[:batch_size])\n", + "train_labels_batch = np.array(train_labels[:batch_size])\n", + "print(f\"True labels: {train_labels_batch}\")\n", + "\n", + "np.random.seed(42)\n", + "\n", + "pred_batch = forward(circuit=circuit, \n", + " input_params=train_images_batch, \n", + " weight_params=np.random.rand(len(ansatz.parameters)) * 2 * np.pi,\n", + " estimator=Estimator(),\n", + " observable=observable)\n", + "print(f\"Predictions: {pred_batch}\")\n", + "\n", + "loss = mse_loss(predict=pred_batch, target=train_labels_batch)\n", + "print(f\"Loss: {loss}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Cost function\n", + "We now define the cost function that will be provided to the optimizer. This function only takes the ansatz parameters as input; other variables for the forward pass and the loss are set as global parameters." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "def cost_function(weight_params: np.ndarray) -> np.ndarray:\n", + " \"\"\"\n", + " Cost function for the optimizer to update the ansatz parameters.\n", + "\n", + " weight_params: ansatz parameters to be updated by the optimizer.\n", + "\n", + " output: MSE loss.\n", + " \"\"\"\n", + " predictions = forward(circuit=circuit, \n", + " input_params=input_params, \n", + " weight_params=weight_params, \n", + " estimator=estimator, \n", + " observable=observable)\n", + " \n", + " cost = mse_loss(predict=predictions, target=target)\n", + " objective_func_vals.append(cost)\n", + " \n", + " global iter\n", + " if iter % 50 == 0:\n", + " print(f\"Iter: {iter}, loss: {cost}\")\n", + " iter += 1\n", + "\n", + " return cost" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We set the initial variables for the cost function." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "# Globals\n", + "circuit = circuit\n", + "input_params = train_images_batch\n", + "estimator = Estimator()\n", + "observables = observable\n", + "target = train_labels_batch\n", + "objective_func_vals = []\n", + "iter = 0" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Below is an example run of the cost function." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Iter: 0, loss: 1.184178242451296\n" + ] + }, + { + "data": { + "text/plain": [ + "1.184178242451296" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.random.seed(42)\n", + "weight_params = np.random.rand(len(ansatz.parameters)) * 2 * np.pi\n", + "cost_function(weight_params)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We also write a function that computes both the MSE cost and its gradient with respect to weight parameters. This function will only be called by gradient-based optimizers." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "def cost_and_grad_function(weight_params):\n", + " \"\"\"\n", + " Cost and the gradient function for the optimizer to update the ansatz parameters.\n", + "\n", + " Args:\n", + " weight_params: ansatz parameters to be updated by the optimizer.\n", + "\n", + " Output: \n", + " cost: MSE loss.\n", + " gradients: gradient of the MSE loss with respect to weight parameters.\n", + " \"\"\"\n", + " predictions = forward(circuit=circuit, \n", + " input_params=input_params, \n", + " weight_params=weight_params, \n", + " estimator=estimator, \n", + " observable=observable)\n", + " \n", + " cost = mse_loss(predict=predictions, target=target)\n", + " objective_func_vals.append(cost)\n", + "\n", + " gradients = param_shift_estimator_gradient(circuit=circuit, \n", + " input_params=input_params, \n", + " weight_params=weight_params, \n", + " estimator=estimator, \n", + " observable=observable)\n", + " # Gradients of the MSE loss\n", + " diff = np.reshape((predictions - target), (1, -1))\n", + " gradient_vector = diff @ gradients * 2\n", + " gradient_vector = np.reshape(gradient_vector, (-1))\n", + "\n", + " global iter\n", + " print(f\"Iter: {iter}, loss: {cost}\")\n", + " iter += 1\n", + "\n", + " return cost, gradient_vector" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Below is an example run." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Iter: 1, loss: 1.184178242451296\n" + ] + }, + { + "data": { + "text/plain": [ + "(1.184178242451296,\n", + " array([-9.73635503e-02, 2.90607919e-02, 2.92371447e-02, -1.83220260e-01,\n", + " -8.16173174e-02, 1.08135151e-01, 2.25847817e-02, -1.10414480e-01,\n", + " -9.70392311e-02, -2.14267714e-01, 4.48859608e-02, 1.54265814e-01,\n", + " -5.44025296e-02, 1.18097785e-01, 2.17290967e-02, -1.50924377e-02,\n", + " 3.02143412e-02, 2.50517768e-02, 3.09498351e-02, 1.70949383e-01,\n", + " -2.14613488e-01, -4.57851032e-02, -9.43863061e-02, 1.15150766e-02,\n", + " 1.55499039e-01, -3.00765083e-01, -7.89320359e-01, -1.25303057e-01,\n", + " -1.57091473e-01, 2.47432338e-01, -9.91262603e-17, -5.11632142e-01,\n", + " 1.93187528e-01, 0.00000000e+00, 2.87367666e-02, -4.74870843e-03,\n", + " 3.16031323e-17, -1.66767353e-01, -3.86115833e-02, -1.70966252e-16,\n", + " -7.90389908e-02, -3.59969443e-01, -1.43679983e-17, -4.53234582e-01,\n", + " 7.55213659e-02, -1.46547119e-16, 7.31008081e-02, 3.77530915e-02,\n", + " 0.00000000e+00, 2.09343686e-01, -9.61306142e-01, 1.44972763e-18,\n", + " -5.11632142e-01, 1.93187528e-01]))" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.random.seed(42)\n", + "weight_params = np.random.rand(len(ansatz.parameters)) * 2 * np.pi\n", + "cost_and_grad_function(weight_params)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Optimizer\n", + "Now we run the optimizer once for a small batch from the training data. We use the COBYLA method for minimization in this example." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Iter: 50, loss: 0.8452108010169597\n", + "Iter: 100, loss: 0.4951055010179454\n", + "Iter: 150, loss: 0.1043639483375415\n", + "Iter: 200, loss: 0.1164047704175796\n", + "Iter: 250, loss: 0.007016066683612002\n", + "Iter: 300, loss: 0.008054336419343503\n", + " message: Maximum number of function evaluations has been exceeded.\n", + " success: False\n", + " status: 2\n", + " fun: 0.005406553515892247\n", + " x: [ 3.041e+00 7.237e+00 ... 5.673e+00 4.445e+00]\n", + " nfev: 300\n", + " maxcv: 0.0\n" + ] + } + ], + "source": [ + "from scipy.optimize import minimize\n", + "\n", + "res = minimize(cost_function, weight_params, method='COBYLA', options={'maxiter': 300})\n", + "print(res)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We run a forward pass with the learned parameters to see accuracy over the small training batch." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Learned weights: [ 3.04136266 7.23721475 4.90796555 4.74858607 -0.06221705 1.95919411\n", + " 0.14294464 5.19378045 4.71302214 5.18968837 1.34092247 8.16126145\n", + " 6.06594677 1.19130735 2.7756313 0.51918586 1.43804901 2.86731259\n", + " 3.54133744 2.29606527 4.1389571 2.46240586 1.69303441 3.71459939\n", + " 4.12221838 4.3448042 1.52458544 2.91071961 3.36304352 0.04466029\n", + " 4.56731689 2.46274125 0.09725305 8.46202367 7.18495553 5.2099957\n", + " 1.87984973 1.67575894 4.69249178 2.34388806 0.71997451 2.18789395\n", + " 0.2558983 4.83185251 1.60313854 4.43355953 1.61751653 2.61613918\n", + " 2.91839883 2.259583 4.79309013 5.115291 5.67287039 4.44528954]\n", + "Forward pass expectations: [-0.93910267 -0.91571108]\n" + ] + } + ], + "source": [ + "print(f\"Learned weights: {res['x']}\")\n", + "pred_batch = forward(circuit, train_images_batch, res['x'], estimator, observable)\n", + "print(f\"Forward pass expectations: {pred_batch}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Since this is a classification task, we use the mean value 0 of the class labels as the cutoff value." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Predicted labels: [-1. -1.]\n", + "True labels: [-1 -1]\n" + ] + } + ], + "source": [ + "import copy\n", + "\n", + "pred_labels_batch = copy.deepcopy(pred_batch)\n", + "pred_labels_batch[pred_labels_batch >= 0] = 1\n", + "pred_labels_batch[pred_labels_batch < 0] = -1\n", + "\n", + "print(f\"Predicted labels: {pred_labels_batch}\")\n", + "print(f\"True labels: {train_labels_batch}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Qiskit Patterns Step 2: Optimize problem for quantum execution" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We start by selecting a backend for execution." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ibm_sherbrooke\n" + ] + } + ], + "source": [ + "from qiskit_ibm_runtime import QiskitRuntimeService\n", + "\n", + "service = QiskitRuntimeService(channel=\"ibm_quantum\", instance=\"ibm-q/open/main\")\n", + "backend = service.least_busy(operational=True, simulator=False)\n", + "print(backend.name)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here we optimize the circuit for running on a real backend by specifying the optimization_level and adding dynamical decoupling. The code below generates a mass manager using preset pass managers from qiskit.transpiler." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "from qiskit.circuit.library import XGate\n", + "from qiskit.transpiler import PassManager\n", + "from qiskit.transpiler.passes import (\n", + " ALAPScheduleAnalysis,\n", + " ConstrainedReschedule,\n", + " PadDynamicalDecoupling,\n", + ")\n", + "from qiskit.transpiler.preset_passmanagers import generate_preset_pass_manager\n", + "\n", + "target = backend.target\n", + "pm = generate_preset_pass_manager(target=target, optimization_level=3)\n", + "pm.scheduling = PassManager(\n", + " [\n", + " ALAPScheduleAnalysis(target=target),\n", + " ConstrainedReschedule(target.acquire_alignment, target.pulse_alignment),\n", + " PadDynamicalDecoupling(\n", + " target=target, dd_sequence=[XGate(), XGate()], pulse_alignment=target.pulse_alignment\n", + " ),\n", + " ]\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we use the pass manager on the initial state. We can similarly apply device layout characteristics to the observable to get a more physical representation." + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "circuit_ibm = pm.run(circuit)\n", + "observable_ibm = observable.apply_layout(circuit_ibm.layout)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Qiskit Patterns Step 3: Execute using Qiskit Primitives" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Loop over the dataset in batches and epochs\n", + "We first implement the full algorithm using a simulator for cursory debugging and for estimates of error. We can now go over the entire dataset in batches in desired number of epochs to train our quantum neural network." + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 0, batch: 0\n", + "Iter: 0, loss: 1.0013021384464493\n", + "Iter: 50, loss: 0.8988555074915614\n", + "Iter: 100, loss: 0.7628055487183264\n", + "Iter: 150, loss: 0.7788079857327457\n", + "Iter: 200, loss: 0.6844030491371074\n", + "Iter: 250, loss: 0.6478703592143327\n", + "Iter: 300, loss: 0.5781510133556838\n", + "Iter: 350, loss: 0.5461730508570907\n", + "Iter: 400, loss: 0.4978965313286361\n", + "Iter: 450, loss: 0.473482067187513\n", + "Iter: 500, loss: 0.46388261715784945\n", + "Iter: 550, loss: 0.45806806678195117\n", + "Iter: 600, loss: 0.45029400561948807\n", + "Iter: 650, loss: 0.44627644433782687\n", + "Iter: 700, loss: 0.44233162108083485\n", + "Iter: 750, loss: 0.44160739439248076\n", + "Iter: 800, loss: 0.440143753656243\n", + "Iter: 850, loss: 0.4391586729786056\n", + "Iter: 900, loss: 0.43818804964766345\n", + "Iter: 950, loss: 0.43774765215024525\n" + ] + } + ], + "source": [ + "batch_size = 35\n", + "num_epochs = 1\n", + "num_samples = len(train_images)\n", + "\n", + "# Globals\n", + "circuit = circuit\n", + "estimator = Estimator() # simulator for debugging\n", + "observables = observable\n", + "objective_func_vals = []\n", + "iter = 0\n", + "\n", + "# Random initial weights for the ansatz\n", + "np.random.seed(42)\n", + "weight_params = np.random.rand(len(ansatz.parameters)) * 2 * np.pi\n", + "\n", + "for epoch in range(num_epochs):\n", + " for i in range((num_samples - 1) // batch_size + 1):\n", + " print(f\"Epoch: {epoch}, batch: {i}\")\n", + " start_i = i * batch_size\n", + " end_i = start_i + batch_size\n", + " train_images_batch = np.array(train_images[start_i:end_i])\n", + " train_labels_batch = np.array(train_labels[start_i:end_i])\n", + " input_params = train_images_batch\n", + " target = train_labels_batch\n", + " iter = 0\n", + " res = minimize(cost_function, weight_params, method='COBYLA', options={'maxiter': 1000})\n", + " weight_params = res['x']\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If we would like to use a gradient-based optimize instead, we can run the cell below." + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 0, batch: 0\n", + "Iter: 0, loss: 1.0013021384464493\n", + "Iter: 1, loss: 0.9616124405029491\n", + "Iter: 2, loss: 0.9593197164689236\n", + "Iter: 3, loss: 0.9501330262492601\n", + "Iter: 4, loss: 0.9149300798176002\n", + "Iter: 5, loss: 0.91224248050783\n", + "Iter: 6, loss: 0.9016808132406134\n", + "Iter: 7, loss: 0.8659308844245013\n", + "Iter: 8, loss: 0.8631305766909787\n", + "Iter: 9, loss: 0.8525051032897298\n", + "Iter: 10, loss: 0.8517304097454138\n", + "Iter: 11, loss: 0.8486353834691429\n", + "Iter: 12, loss: 0.8363458890924125\n", + "Iter: 13, loss: 0.790934721388661\n", + "Iter: 14, loss: 0.7873961033727147\n", + "Iter: 15, loss: 0.7736238039330865\n", + "Iter: 16, loss: 0.7305036930895389\n", + "Iter: 17, loss: 0.7270640524718088\n", + "Iter: 18, loss: 0.714349811564467\n", + "Iter: 19, loss: 0.7134181784203941\n", + "Iter: 20, loss: 0.7097074498869945\n", + "Iter: 21, loss: 0.6952907929670569\n", + "Iter: 22, loss: 0.6590593304379527\n", + "Iter: 23, loss: 0.6559651153296127\n", + "Iter: 24, loss: 0.6455977553480631\n", + "Iter: 25, loss: 0.6448232303438566\n", + "Iter: 26, loss: 0.6417742556723521\n", + "Iter: 27, loss: 0.6304503623058628\n", + " Current function value: 0.630450\n", + " Iterations: 10\n", + " Function evaluations: 28\n", + " Gradient evaluations: 28\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/anaconda3/envs/quantum1/lib/python3.10/site-packages/scipy/optimize/_minimize.py:708: OptimizeWarning: Maximum number of iterations has been exceeded.\n", + " res = _minimize_bfgs(fun, x0, args, jac, callback, **options)\n" + ] + } + ], + "source": [ + "batch_size = 35\n", + "num_epochs = 1\n", + "num_samples = len(train_images)\n", + "\n", + "# Globals\n", + "circuit = circuit\n", + "estimator = Estimator() # simulator for debugging\n", + "observables = observable\n", + "objective_func_vals = []\n", + "iter = 0\n", + "\n", + "# Random initial weights for the ansatz\n", + "np.random.seed(42)\n", + "weight_params = np.random.rand(len(ansatz.parameters)) * 2 * np.pi\n", + "\n", + "for epoch in range(num_epochs):\n", + " for i in range((num_samples - 1) // batch_size + 1):\n", + " print(f\"Epoch: {epoch}, batch: {i}\")\n", + " start_i = i * batch_size\n", + " end_i = start_i + batch_size\n", + " train_images_batch = np.array(train_images[start_i:end_i])\n", + " train_labels_batch = np.array(train_labels[start_i:end_i])\n", + " input_params = train_images_batch\n", + " target = train_labels_batch\n", + " iter = 0\n", + " res = minimize(cost_and_grad_function, weight_params, method='BFGS', jac=True, options={'disp': True, 'maxiter': 10})\n", + " weight_params = res['x']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Alternatively, we can use the SPSA optimizer." + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 0, batch: 0\n", + "Iter: 0, loss: 0.9923538645069631\n", + "Iter: 50, loss: 1.0491942798654916\n", + "Iter: 100, loss: 0.9799959968320546\n", + "Iter: 150, loss: 0.8857701149698306\n", + "Iter: 200, loss: 0.8178654359214842\n", + "Iter: 250, loss: 0.7574209229582517\n" + ] + } + ], + "source": [ + "from qiskit_algorithms.optimizers import SPSA\n", + "\n", + "\n", + "spsa = SPSA(maxiter=100)\n", + "\n", + "batch_size = 35\n", + "num_epochs = 1\n", + "num_samples = len(train_images)\n", + "\n", + "# Globals\n", + "circuit = circuit\n", + "estimator = Estimator() # simulator for debugging\n", + "observables = observable\n", + "objective_func_vals = []\n", + "iter = 0\n", + "\n", + "# Random initial weights for the ansatz\n", + "np.random.seed(42)\n", + "weight_params = np.random.rand(len(ansatz.parameters)) * 2 * np.pi\n", + "\n", + "for epoch in range(num_epochs):\n", + " for i in range((num_samples - 1) // batch_size + 1):\n", + " print(f\"Epoch: {epoch}, batch: {i}\")\n", + " start_i = i * batch_size\n", + " end_i = start_i + batch_size\n", + " train_images_batch = np.array(train_images[start_i:end_i])\n", + " train_labels_batch = np.array(train_labels[start_i:end_i])\n", + " input_params = train_images_batch\n", + " target = train_labels_batch\n", + " iter = 0\n", + " res = spsa.minimize(cost_function, x0=weight_params)\n", + " weight_params = res.x\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We are now ready to run the training on real hardware." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# To continue running on real hardware use\n", + "from qiskit_ibm_runtime import EstimatorV2 as Estimator, EstimatorOptions, Session\n", + "\n", + "batch_size = 35\n", + "num_epochs = 1\n", + "num_samples = len(train_images)\n", + "\n", + "# Globals\n", + "circuit = circuit_ibm\n", + "observables = observable_ibm\n", + "objective_func_vals = []\n", + "iter = 0\n", + "\n", + "# Random initial weights for the ansatz\n", + "np.random.seed(42)\n", + "weight_params = np.random.rand(len(ansatz.parameters)) * 2 * np.pi\n", + "\n", + "with Session(backend=backend):\n", + " session_options = EstimatorOptions()\n", + " session_options.default_shots = 10000\n", + " session_options.resilience_level = 1\n", + "\n", + " estimator = Estimator(session=Session(service, backend=backend), options=session_options) # hardware\n", + " \n", + " for epoch in range(num_epochs):\n", + " for i in range((num_samples - 1) // batch_size + 1):\n", + " print(f\"Epoch: {epoch}, batch: {i}\")\n", + " start_i = i * batch_size\n", + " end_i = start_i + batch_size\n", + " train_images_batch = np.array(train_images[start_i:end_i])\n", + " train_labels_batch = np.array(train_labels[start_i:end_i])\n", + " input_params = train_images_batch\n", + " target = train_labels_batch\n", + " iter = 0\n", + " # We can replace this with other optimizers\n", + " res = minimize(cost_function, weight_params, method='COBYLA', options={'maxiter': 1500})\n", + " weight_params = res['x']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Qiskit Patterns Step 4: Post-process, return result in classical format" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Testing and accuracy\n", + "We now interpret the results from training. We first test the training accuracy over the training set." + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[-0.09390355 0.45874848 -0.57343827 0.60371608 0.68416663 -0.30337543\n", + " 0.50806944 0.6433382 0.23476469 0.59818076 0.72405324 0.60110709\n", + " -0.03611846 0.39301714 0.36442518 0.51957222 -0.7280917 0.39874926\n", + " -0.6619111 -0.75414771 0.66798464 0.32584446 -0.10038303 0.43637671\n", + " 0.39385072 0.29094047 -0.24409827 -0.07560782 0.20853491 -0.23922216\n", + " 0.58716185 0.07707207 0.19127664 0.43264673 0.78270414]\n", + "[-1. 1. -1. 1. 1. -1. 1. 1. 1. 1. 1. 1. -1. 1. 1. 1. -1. 1.\n", + " -1. -1. 1. 1. -1. 1. 1. 1. -1. -1. 1. -1. 1. 1. 1. 1. 1.]\n", + "[-1, -1, -1, 1, 1, -1, 1, 1, 1, 1, 1, 1, -1, 1, 1, 1, -1, 1, -1, -1, 1, 1, -1, 1, 1, 1, -1, -1, 1, -1, 1, -1, 1, 1, 1]\n", + "Train accuracy: 94.28571428571428%\n" + ] + } + ], + "source": [ + "from sklearn.metrics import accuracy_score\n", + "from qiskit.primitives import StatevectorEstimator as Estimator # simulator\n", + "# from qiskit_ibm_runtime import EstimatorV2 as Estimator # hardware\n", + "\n", + "estimator = Estimator()\n", + "# estimator = Estimator(backend=backend)\n", + "\n", + "pred_train = forward(circuit, np.array(train_images), res['x'], estimator, observable)\n", + "# pred_train = forward(circuit_ibm, np.array(train_images), res['x'], estimator, observable_ibm)\n", + "\n", + "print(pred_train)\n", + "\n", + "pred_train_labels = copy.deepcopy(pred_train)\n", + "pred_train_labels[pred_train_labels >= 0] = 1\n", + "pred_train_labels[pred_train_labels < 0] = -1\n", + "print(pred_train_labels)\n", + "print(train_labels)\n", + "\n", + "accuracy = accuracy_score(train_labels, pred_train_labels)\n", + "print(f\"Train accuracy: {accuracy * 100}%\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We now test the model accuracy over the test set." + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[-0.16285746 0.71801972 0.4283502 -0.24037397 0.55176844 -0.38751922\n", + " 0.03649525 0.42260929 0.44751901 0.19029177 0.47835348 0.43318434\n", + " 0.49879189 0.4579182 0.24163785]\n", + "[-1. 1. 1. -1. 1. -1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]\n", + "[-1, 1, 1, -1, 1, -1, 1, 1, 1, 1, 1, -1, 1, 1, 1]\n", + "Test accuracy: 93.33333333333333%\n" + ] + } + ], + "source": [ + "pred_test = forward(circuit, np.array(test_images), res['x'], estimator, observable)\n", + "# pred_test = forward(circuit_ibm, np.array(test_images), res['x'], estimator, observable_ibm)\n", + "\n", + "print(pred_test)\n", + "\n", + "pred_test_labels = copy.deepcopy(pred_test)\n", + "pred_test_labels[pred_test_labels >= 0] = 1\n", + "pred_test_labels[pred_test_labels < 0] = -1\n", + "print(pred_test_labels)\n", + "print(test_labels)\n", + "\n", + "accuracy = accuracy_score(test_labels, pred_test_labels)\n", + "print(f\"Test accuracy: {accuracy * 100}%\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, we plot how the loss decreases over iterations during training." + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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jfg8Ejajqe7JKuvnesdPdDSN62jxbsAEAAABA5qikdzM3nDFOknTa+P5Rx91tbRwXimkclyS9mgN87HT32MuyqVJtNZbsGR0AAAAAtKKS3s3cOHecvnT8MJXXNOvVjZWR425nS408VTaODeH+oJH2WnLzU7H7qcdelWy/9c5Gd3cAAAAA3QWV9G7GZrNpSEmucl2OqONuR8vvqSrY/pjwHIxZk57senOA98VU0mOvy6ZKuuV0984fBgAAAACkREjvpjyu6H904TXpSSvhISPueX9Md/dkFXBz8I49LzaUZ3vjOCrpAAAAALIRIb2bynVHV9JdjtTT3a32Ng+EjKiAnawCHtU4LsWa9GxqHMcWbAAAAAC6C0J6N+Vxxkx3T6O7u1WVPBiK7u6erAJuzuXdqXGc5Zr0Th8FAAAAAKRGSO+mYivpke7uScJxbLAOHzOiursnfs3oSnry6e5Z1TiOSjoAAACAboKQ3k15YhrH2e2t090TrbeODdZSS5g2oirp8UE+zLwFW6A7NY6zqJtn0/gAAAAAIIyQ3o0NKvZEHjtstsjjRPnTqpIeCLVtC7b47u7x901HKGTo//70nr7774/SOr8trIZCRAcAAACQjQjp3diw0rzIY7sppCea8m41BT0QCkU3jktcSI+6b1xVPubXZBV5sw3ldXptY6X++cEnHdZx3fK+VNIBAAAAZCFCejc23BTSbaZ/kommcsdWv6WW4B6KWpOeOLyaw26q6e7prkmPeu0O6AhfVe/Vgsfjq/REdAAAAADZqMtD+v3336+RI0fK4/Fo1qxZeu+99xKe6/f7dfvtt2vMmDHyeDyaMmWKXnjhhU4cbXYZYQrp6Ux3D1huwRaKWZOerLt74i3Y4kJ6moHbNOwO2bbt1v+u1Xs7DsYdD2XTFnEAAAAAcFiXhvTHH39cCxYs0K233qqVK1dqypQpmjdvnvbv3295/o9+9CM99NBD+u1vf6t169bpmmuu0ec+9zl9+OGHnTzy7HDOsQMlSSV5rujp7gkCqD+QTuO49Nakx3d3j3kti6q9FZtax53uNZnYeaDB8jgRHQAAAEA26tKQfvfdd+uqq67SFVdcoYkTJ+rBBx9UXl6eHnnkEcvz//KXv+gHP/iBzj33XI0ePVrXXnutzj33XN11112dPPLsMHZAoZbcdLKWLjglqiKdaLq737KSbqQ95Tx6C7bWexmGobe3VkXfN83p7nbTJ7Azt21jSToAAACAbOTsqhf2+XxasWKFFi5cGDlmt9s1d+5cLV++3PIar9crj8cTdSw3N1fLli1L+Dper1derzfye21t7RGOPLuMKyuUFB2aE+Vsf8C6Um2eZp5sa7KoLdhMjxc+sVqPvb87+rXSrIqbX87qS4SOQkYHAAAAkI26rJJeVVWlYDCosrKyqONlZWUqLy+3vGbevHm6++67tXnzZoVCIS1ZskRPPPGE9u3bl/B1Fi1apOLi4sjPsGHD2vV9ZAvzdPdE663TWfOddE26KVH7Dgf+8prmuIAuSf4013wHo/Ze78xKOjEdAAAAQPbp8sZxmfjNb36jcePG6eijj5bb7db8+fN1xRVXyG5P/DYWLlyompqayM/u3fGBsiewpzHd3aq7e6zk3d1bH4eb0FXVey3PDQRDemHNPm2vsl4THnm9JM3oOhIZHQAAAEA26rKQ3q9fPzkcDlVUVEQdr6io0MCBAy2v6d+/v5566ik1NDRo586d2rBhgwoKCjR69OiEr5OTk6OioqKon57IZrNF1qWnmu4+bXiJ1tw2z/Kc9Lu7G0nPX7p+v67560qd9qvXko476d7rHchgwjsAAACALNRlId3tdmv69OlaunRp5FgoFNLSpUs1e/bspNd6PB4NGTJEgUBA//nPf3TBBRd09HC7hfCU90SV9PB0d5fdrny3w/KcTBvHJZpCb7XtWarXs9oirqNQSQcAAACQjbqscZwkLViwQJdffrlmzJihmTNn6p577lFDQ4OuuOIKSdJll12mIUOGaNGiRZKkd999V3v27NHUqVO1Z88e/fjHP1YoFNJ3v/vdrnwbWcNuk4JKHNLrmv2SpPwch2w2m1wOW/xWahmG9EShPpDm1HXzOvROXZPeaa8EAAAAAOnr0pB+0UUXqbKyUrfccovKy8s1depUvfDCC5Fmcrt27Ypab97c3Kwf/ehH2rZtmwoKCnTuuefqL3/5i0pKSrroHWSXlkq6kXC6+6HGlpDeJ88tSXLY40N6sjXp5kJ3eM/1RNXvNPvGJdzWraMl62IPAAAAAF2lS0O6JM2fP1/z58+3fO61116L+v2UU07RunXrOmFU3VNkunuChHyo0SdJKjkc0t0Ou5r90cHYnJMr67z601vbdfHxwzW8b170+vFQ8kp6ugIJtnXraGR0AAAAANmoW3V3R3IOe/I16dUN4Uq6S5KU44pflx40VcYX/HOVfvfaVl30++Vx903VOC5d5tfrzEo6AAAAAGQjQnoPkqq7e6SSnt9SSc9xxv/jN4fuZVuqJEn7appb7mvu7h5on0q6OZezTzoAAACA3o6Q3oOEp7snCs7VjTGVdIuQbr40NseanwtPTT/SKermSnqi9e3VjT49v3qfvIFgxvdPlMU7cWY9AAAAAKSNkN6DhKe7J6oShyvp4cZxOU6r6e5J9kk3zOvH239NeqJ90r/yx3f1zb+t1N0vbcro3t5AMOH4KKQDAAAAyEaE9B7kcEZP2KE93N29JLImPfl091jm8B/MoJKeNPibG8clCOlr9tRKkp5etTfla4V5A0HN+OnL2lBeZ/m8wSZsAAAAALIQIb0HsUW6u8c/ZxiGquMq6RYhPUmJORhV9Q5X0lM3e0vWEC4qpKe4V/hLiHRsrqhXXXMg4fNU0gEAAABkI0J6D+KwJe7uXu8NRKrebZ3ubn4qUklPo9lbsmp7OtPdw8JfQrQHGscBAAAAyEaE9B7EHunu3hJA399xUJ//3Vv6+JPqSNO4HKddue6WcO62qKRLifdZD1kE6nTWpIc7wae6ZyDFFmz2dvy0EtEBAAAAZCNCeg8Sme5+OIFe/Pt3tHJXtb788LuqbW4J6UW5rsj5VtPdpcRT3kNtXJPuTzKNPaqSnuJeNrVnJb3dbgUAAAAA7YaQ3oOEu7uHw3Q4SNd7A/IermZ7TM3irKa7m6+LO2463LomPY3p7kmmsUetc09ScZcyW5OeCo3jAAAAAGQjQnoPEpnubhGcvf6WAGwO5lbd3aXEwduI2oIt/Up6uiE9ZeO4dkzpVNIBAAAAZCNCeg9ij5nubuYNBCVFT3HPdLq7OVCHH6fV3T3JOcFMGselfKX0HeH27gAAAADQIQjpPYg9Zrq7uTFceLp7dEhvraqbi9QJG8cZ5kDdcr+01qQnaQgXiGocl/xe9vbs7s50dwAAAABZiJDeg8ROd891tYbw1pBumu5uCuxOU+v0YMiI6rTutMfvvx6ugCcK9GbJwnfISH+6eztmdNq7AwAAAMhKhPQeJFxpfm/HQUnRIf36f3woKbq6bl6Tbre3hvxgyFCTPxh5LrYhndQ6Nf2IK+nB9Ke7t28lHQAAAACyDyG9B9lYUSdJuuflzTpQ743sh26WaLq7TbZIGA8a0SE9HI6DFlXvtLq7Jzkn6p4p9km3ZRDSQyk6w6UzAwAAAAAAOhshvQc5fmRp5HF5bbNlo7Ucl/V0d7vNFMZDhpp9rYE5XAk359pgO1XSzY3nUt0rk+buqb48IKIDAAAAyEaE9B7kx+dPijyubWrdG90sUXd3m621kh4KKaqSHggZCh3+CfNnUklPMo09ENXdPdU+6e1XSWcLNgAAAADZiJDeg0wcXKTjhpdIkmqa/Go2Be2wqJBuqqrbbJLDNK290ReIus4fCkUF33A4T9WRXUoevkMZdHfPZEl6qu8O6O4OAAAAIBsR0nuY4lyXJKm22Z+gkt4azN0O83R3W2QLt2AoFFVJl1qausXuaW4YRnr7pAcN+YMhVTf64p4LWFTnE8lkTXrK6e5kdAAAAABZiJDewxQdDunf/ffHqvcG4p5P1N3dZmvdai0YUlwV3h8IxQXbkJHemvRAKKTP/+5tTb19icprmqOeC2a0T3rKlzKNLVVIJ6UDAAAAyD6E9B6myOOKOzZ3woDI40Rr0qMr6YYafbGV9FBUJ/bIsTTXpK/eUyNJWrqhIuq5qJBuUUk3h+mM1qSnKPAT0QEAAABkI0J6DxOe7m5WkueOPDZXz6O3YGtdkx4yDNU1R1fhfcFQXHU6EDLSqqR7A62BP9/tjHoudgp9LPP9M+ruTuM4AAAAAN0QIb2HKcp1xh0rMQV3czBP1N09EDJU1+yPuoc/aMTtLR6MWaeeSGWdN/LY44reuz0QNd09vvxtngLfrvukk9IBAAAAZCFCeg9jVUnPz2kN7uZg7nHF7JN++NdgyFB9TCXdHwzFdUz3h0JpVdJ/9dKmyGNzqDcMQzVNrV8GWFXSzc3kMiikx32hEIuIDgAAACAbEdJ7GKs16XluU0d3Z4Lp7qYt2DZX1OmDnYei7uELxK8/D4bS6+5uZp76Pv8fH2rJutY16lZbtZkr6Zntk578eQrpAAAAALJR/NxodGtWlfRct/UUd/PxkKHIdPfvP7E67h6+YCiuI7o/GEprn3Qz87Zwz328L+o5q6q8eQp8JlPUU0/DJ6UDAAAAyD5U0nuYQotKunkduLl63sfUUK6m0R8J6Vbe335Qe2O2TwsEjYzXdsdu7RZ9v/hKus90LJ3172Gptlijkg4AAAAgGxHSexjzdPYw83R3c3d3cyj3BUNJp5Mv+t+GuGOJurs7k4R9cyU9lmV3d9OxVB3bzVKdS+M4AAAAANmIkN7DjBtQoLMmlkUdy3VZT3ePlaySbiUQst4n3elIEtL9iUO61T7p5mOpmsGZpaq6k9EBAAAAZCNCeg9jt9v0+8tmqNDU0T03wXT3lt+tK+vpCAQNyzXpuTHbrJk1BxJPd2/0xT9nrq6n00k+LFUIz8aMfuf/NuiB17Z29TAAAAAAdCFCeg+VYwrKiRrHSVLf/NZ16VYhvU9e/Br3sEDIep90q+Z1Yckq6fXeQNyxqOnuPbiSvvNAgx58fat+/sKGlOvpAQAAAPRchPQeyrwHerKQ3scc0i3WpJeano8VCIYsp6gX58VfM7JvnqToLdhi1TcH4jvIh9rW3T3VudkWhM2zCFJ9F1HX7Fd5TBM/AAAAAD0DIb2HmjK0JPI42XR3cwivqvfG3advQU7cMbej5WOTSSX9ouOHS2ptHGfVyT0QMuIay/lNv2cy3T1lSE/7Tp3DPN5UswCm3r5En1q0VPvrCOoAAABAT8M+6T3U7RdMUlGuUxcdPzyqkh7b/d0c0nccaIy7T4lF4HY77fId3iPdKjhbhfRwZT+8BZvPIqRLUl1zIGrLOPP9M2scl/z5bKukm4eT6guGcIj/eHeN5k70dOSwAAAAAHQyKuk9VN+CHC36/GRNHVYSqXxLUuyM9j4WU9PNciyawIWDfqLu7sW58d/9hCv44Up5orXpsevS/cGeW0k/1ODTPS9v0u6DjVF/HzNZew8AAACgZyGk9wJFHpdG9s3TkJJc9YuZvn7liaNkt0mfP26IrjttjBx2m/50xfEqyXPp9KMHKNcV/xGJTHfPoJIeXgsfDumJKun1zdEh3dw4LpNKeqqQng05+Lv/+Vj3vLxZJ/3iVV1w/1uR45nsBw8AAACgZ2G6ey9gt9v08oJTZCi+g/uw0jytu/3sSIief9o45bodemfhGXI77Lrj+fVx92utpFuvSS/0RIf0iYOKlHM47HsPT3dPVEmv8/qjfjc3psskvKYK9Nkw3X351gOWxzP5MgIAAABAz0JI7yWcjsSTJjwW27WFj1lVxc3T3a26u+eb9mi/78vTdNK4/np/+0FJUnOkkm7d5T22ku5v6xZs3WCf9ETVfqa7AwAAAL0XIR1JFVvsk26e7h60SMP5pkZ1px89QHluZ1wlvTmmkp7rcqjJH1S9t2Ubtqv/skLN/qA+O3VI5JxMwmvKanQW5OBExXwyOgAAANB7EdKRVPJKumE5Bd1cSXfaW84NN47zJViTXpzrioT0Rl9QS9ZVSJKOG94nco43EFKjL6A8d+qPberGcV2fhBONIZP94AEAAAD0LDSOQ1JFSUL6P9/fHTUdPSzfbQ7pLWvg4xrHxeyHXnK4Yl/XHFCDqcN7c6B1WnyjL6iJt7youubodetWUq1ft5il3+kSDZHp7gAAAEDvRUhHUkWexJ3a39txUAcbfHHP9y9s7SBvPxzSw2vcm/1Brdh5SBf//p3o1zn8ZUC9NxC1DVttU/QadUn6YOehlONOVYzOjkq6tWQhPRsa3gEAAADoOEx3R1KW092TNKGTpPEDC3XdaWNUmt8a1s2V9Gv+uiLumrzD69jrmwNq8LZWz2utquZp5NRU1eisyLoJ16QnHpz5bWXDWwAAAADQvgjpSMoqpMdu42blO/OOjvo90jguEIyb6i61Bv/4Snp8SE+nCp4ypKe8Q8dL9D6SjZ316gAAAEDPxnR3JGUV0g81xk9xTyXcOM4fNCzDqetwpb2uOTqkV9Z5485NZz15qmnh2TBtPHF3d0I6AAAA0FsR0pFUuEmcWUVtfHBOJcd0H6tmc+Eu7s3+YFTjuA3ldXHnBtJorJaqcVw2ZN1EgTvZ28uGcQMAAADoOIR0ZKy8pjnja3JdDtksZsmfNK6f3v3BGRpSkitJkW3YkvGaOr4n0h7bpAdDhq77+0o98NrWNM7OXFsax5mDfepFBwAAAAC6G0I6MtaWKdd2u0198txxx0vz3Sor8ij3cOO4Rl90Jd1Kky+NkJ6ycVz88+9sOxD1BcRrG/fruY/36ecvbEj5em3Rli3Y2J0NAAAA6NkI6UjpO/PGa0TfPI3pn6/PTBmshy+foSElufrrlbN02vj+mjO2r/5z7WwNKcnVQ1+dnvA+ffLi17f7gy0LzMPd3ZvTqKQ3+VOH9FSN42KffnfbAV38+3f0qUVLI8ca0/gyoCOwJh0AAADovejujpSuO22srjttbNSxt75/uiTpxHH94o4lUprv1tbKhqhj4ap4ritcSQ/EhfR+BW5V1bc2q2v2p+4cl+l09+XbDqS8Z2dJuk96Gk3zAAAAAHRfVNLRaUrz46e7h6vVyaa7j+qXH/V7OpX0VBXn2Onu2VSgppIOAAAA9F6EdHQaq5AeDtzJprsP65MX9fueQ0369j8/0gc7DiZ8rfYIs+ZGd525ZVswSbXc/L6I6wAAAEDPw3R3dJqklXRX6z7q1Y3+qHOG9MmN+v0/Kz+J/HXHnedZvlaqNemxmduq87xZIGTI5eicfurpNo7Lhr3eAQAAALQvKunoNFbd3cNBMzzdXZKq6qP3YR9cEh3S05Gqkp5ppT1gsbd7R0kWvs3P0ekdAAAA6HkI6eg0sZX0vvlu/fKLUyRJboddDntLpbqyrh1CeooGa+YcvKe6Sev31cadYzPtRO5PdcN2FEy6Jr31MZV0AAAAoOdhujs6jTmkf+/so3XNKaNlOzzP3GazKdflUL03oEOx093bENKTBV1JMg6v6A6FDM2585Wk50hSsBMr6ebp7k+v2qMmX1AXzxwuKXoGQEdU0pt8QX1t8fs6/egBuurk0e3/AgAAAACSIqSj05SYprtfeNyQSEAPy3U7LPdIH1ziyfi1Und3b/nrOosKumEYstlsUSG4Myvp4bEHQ4ZueGyVJOn0CQM0oNATE9LbP6U/9v4uLd92QMu3HSCkAwAAAF2gy6e733///Ro5cqQ8Ho9mzZql9957L+n599xzj8aPH6/c3FwNGzZMN910k5qbmztptDgSk4cU65KZw3XLpydqQFF88A43j4uV5878u6RQmo3j3txcFfdcuJIdNAXzzlyTHu7uHjC9foO3pcGeOZd3REgPN/IDAAAA0DW6tJL++OOPa8GCBXrwwQc1a9Ys3XPPPZo3b542btyoAQMGxJ3/97//Xd///vf1yCOP6IQTTtCmTZv0f//3f7LZbLr77ru74B0gE3a7TYs+f2zC5/NMzePsNum2C47RmJg90tOVKlMbMtToC+jJDz+Je84fNOR0tPw1LFlIb/AGlJ/Tfv8qhb8kMBfvw3MOorZg64DvDVJ1uQcAAADQsbq0kn733Xfrqquu0hVXXKGJEyfqwQcfVF5enh555BHL899++23NmTNHX/7ylzVy5EidddZZuuSSS1JW39E9mDu8l+S59dVPjdAJY/ulfb0/GNLbW6rU5AumNd39gde2alNFffx9DqdjczBPNN39tmfWatKtL+r9JHu2Zyo89oDFa4Y6uJJubpbXGWh+BwAAAETrspDu8/m0YsUKzZ07t3Uwdrvmzp2r5cuXW15zwgknaMWKFZFQvm3bNj3//PM699xzE76O1+tVbW1t1A+yk3m6e0meK+Prf/PyZn35D+/qu//5OPV0d0nbKhssnwuH83Smu//prR2SpF++uDHj8SYSDt9RlfTD2dncVC7VXvDZrtkf1Bl3v65v/ePDrh4KAAAAkDW6LKRXVVUpGAyqrKws6nhZWZnKy8str/nyl7+s22+/XSeeeKJcLpfGjBmjU089VT/4wQ8Svs6iRYtUXFwc+Rk2bFi7vg+0H/N091KLPdVTue/VLZKkZz7am9Y+6Q2++CZ1khQ4vCjcPN3dH0zeOK4968/h8G1VSTd60HT3NzZValtlg575aG/nvSgAAACQ5bq8cVwmXnvtNd1xxx363e9+p5UrV+qJJ57Qc889p5/85CcJr1m4cKFqamoiP7t37+7EESMTHlf0dPcjkSJTyzCkRq91kzS/RUgOpKha29sx3Ua6u1sE8o6e7t6ZuvfoAQAAgI7RZY3j+vXrJ4fDoYqKiqjjFRUVGjhwoOU1N998s7761a/q61//uiTp2GOPVUNDg66++mr98Ic/lN0e/51DTk6OcnJy2v8NoN2ZK+l92jDd3SzlmnQpZSU9EDW1PEUlvR0r0OEvGKKmtoenwHfwPun0jQMAAAC6VpdV0t1ut6ZPn66lS5dGjoVCIS1dulSzZ8+2vKaxsTEuiDscLcGOBlTdn3mrtT75R1ZJT1llNozIdmMeV/RnKjzNPapxXIp28e1aSY9sAWfEHevofdLp7g4AAAB0rS6d7r5gwQI9/PDDevTRR7V+/Xpde+21amho0BVXXCFJuuyyy7Rw4cLI+eeff74eeOABPfbYY9q+fbuWLFmim2++Weeff34krKP7mja8JPI4P8290RN9OZOqqVrIaNk6TZJKcqO/EAhEurunv096u1bSjfiQHj5mfrvd/Yupbj58AAAAoEN06T7pF110kSorK3XLLbeovLxcU6dO1QsvvBBpJrdr166oyvmPfvQj2Ww2/ehHP9KePXvUv39/nX/++frZz37WVW8B7eicYwbpBq2SJLmd6X1/5A8acjttUd3c3U57ygDYsk96SyW9JM+l8trm1nsGwmvSU2/B1hGCFpX0oGUlvf1fu7O3YAMAAAAQrUtDuiTNnz9f8+fPt3zutddei/rd6XTq1ltv1a233toJI0NnczvtevRrM/X4+7v05VnDo5778fkT9cSHe2QY0uo9NZHjvmBIbqdd1U3+yLFclyN1JT3Uuia9ODd6/Xtkn3TTPa740/t64NLjdM6xgyzv157T3Q2LSnpnNY5jujsAAADQtbpVd3f0fKcc1V+/u3R6XHD+vzmj9N/5J2pUv/yo4/5AS6A+UO+NHPMFQlGd0a00+4OR4Bu7J3t4anvstmvX/m1lwvu1b+O4+O7unVVJj9XsD2pvdVPHvxAAAAAASYR0dDOlMQ3lwkG60hTSm/zBqPXkVuq9rZ3dY78QCF+bqhpv1q77pBvhcVitSTdX1zuikt76TkIhQ2f++nWdcOcr2rK/rt1fCwAAAEA8Qjq6lfMmR0839wVDavIF9faWA1HHw+vNEwmH9Dy3QznO6KaD4X3SU3V0N+uI7u5RVfPIMdN5Hdx5LWgY2n2wpYr+4tqKFGe3Rcd+4QAAAAB0R4R0dCvHjyzVE988IfK7NxDSOb95Q/e9uiXqvA3lySu/4RCf53bK5Yj+1yCyT3qKary50t4R3d0DVo3jQh073d38Nqy2gOsonTF1HwAAAOgOCOnodo4b3kd9D097P1Dv044DjZKkfgU5cjsy+0jn5zjkckYn7Mg+6SmSoy/QGuJt7ZjSrQJ5OLh3ZuM4qy3gOkpHzwoAAAAAugtCOrql8BZtlXWta9H/O3+OimOawKWS53bGBfuARXd3K1EhPaNXTS4czgMW3d2j16S344tasGpcZ9bsD0b9PTgShHQAAACgRZdvwQa0RXiKemVdy/7mg4s9GlySm3FwzXc74qa7+5NMdzcMQzabTRvL6+RytEbzTCNmsjXYIaMlqIesprubK+kdMEfc/GVD9NT66NfyBUKaevtLKshx6f0fntGmmQTmW5LRAQAAgBaEdHRL4YD842fWSZKK81qmv1c3+jK6T16OM1KVD/NHtmCLT47eQEj7app19m/eiAqWqdavx0rWOf7pj/bo/te26JLjh7WeH5nu3nlbsEVNd495e7sONqjZH1Kz36tgyJDTcWRzCaikAwAAAC2Y7o5uKbb6XXJ4G7VUU9RjHWrwWTSOO7xPeSg+eNc1B7S9qj6u8pvodZv9QV25+H395Z2dkqQX1uzT7c+sS9o5fltlg3yBkB5dvjNyzLLjeyd0d0/8WjbL89qKxnEAAABACyrp6JZyYqrfJRmuRQ+rbfbLHVMFTrYmva7ZrwZv/PZuidZm/+O9XVq6Yb+Wbtivj3dX618rPpEkjR1QkHJsRR6naptbtooLV7Wjp4i3f7INRk2nNx1PkqK/9OByXXvqWJ19zMA2vy6VdAAAAKAFlXR0S3GV9DaG9Ns+M8liTXp4unt88K73BtRweI91s0SV9Lrm1nPDAV2S9lQ3phybx9W6f3s4xHZ0x3Vz8E/WOM68BP2jT2p0zV9XHNnrtk//OQAAAKDbI6SjW4pdA12c27Im/VOjS9O+x1UnjdKp4wck3Cfdqnq8+K0duu3wOnira8weWbZddy/ZZPnayaa7h3lN1fnwUDp6TXqivdFjK93t0c3efEcq6QAAAEALQjq6pUBMyA1X0h+4dLrOO3ZQWvco9LRcE9s4LlwVtwrST3y4R01+i+nuFufe/mx8mI+cn8bWZd5A6+tYdnfvgGBrvn/Qort8W/zrg91a9Pz6FB3tCekAAACAREhHN1UfM+W8+HDjuD75bl0yc3ha9yj0tLRkSLgFm0XjuEQy7e5uNZU+VnQlPbwmvWP3SQ8lmO4eG6Izeenv/PtjPfTGNr2z7WD0PaK+cMhomAAAAECPRUhHtxQb0s3Tr8PhO5XWSnr05O3WfdLTT46b99frxbXlkd+bLartVq+RjGFR1e7ofdJDCaa7x1bSE1XF//TWdt27dLPlczVN0dvjhaK+cCClAwAAABIhHd1UXEg35ex0Q3pRgkp6OJxnup3bN/6yQgfqvfrjsu06+uYXkp6bznR3s6DlFmwZ3SItUdPdoxrHJT4vzDAM3fbMOt29ZJM+OZS6MV5n7vkOAAAAdBeEdHRL9aau6VOGFuvTkwdHfi/IsJKeqLt77BT2X31xSsp7Hmr06SdJ1qLHvka6vvPvj7V0fYXlPuntWYU2B3PzTILY6e5Wa9TNx6xnEkTPWOjMPd8BAACA7oKQjm7JXOV+ev6Jys9pDeZFnvS2Y0u0Jj28Fj02SH9h+lBdPntE0ns2+dKrkPsyXMMuSVc++kHUFPjFb+/QvF+/oeN+skSvbKjI+H5WzIHfPMbYUG4Z0jMM2uYl/4R0AAAAoAUhHT1OjjO9j3VRpJIeuyY9fk/ysOI8d9J71jT503rtdNakW4kNsxsr6nSo0a+vLf6gTfdLdn9zJT02gFtl6tR99qIvCnVwEzwAAACgOyKko1u695Jpcjlsuu/L0+Kes9ls+tTo0qh16lbClXRbzDTsQJLu7iW5yav0tc0dHdLbdFnazMMyjzF2Sr1V1TzT0B3d3Z2UDgAAAEhSeot3gSzzmSmDdfakgXF7nIf946pPKRAyZBgtlXJ/0JDLYdMbm6t0+SPvSWpdux4b5pPtkx7ejz2R2nQr6YHoe+c47fraiaP0wGtbk17X0WG2vae7J1svH6RxHAAAABCHkI5uK1FAl1qq6eZp7OFt1rymhmaxa9HDwhVkqyDaJ8V093Qr6bFr0vPcDpUV5qS8rqO3KjN/CeAPJA7pVuOI3RLOMAxV1fvizrN6LSrpAAAAQAtCOnqV40eWSpJG9s2LHPO4osO6NxBuHBc/Jb0oN/m/MumuSffGbMFms9nksKeYn6/Onu6eeJ/0VN3dJelXL23U/a8mnhlgPp190gEAAIAWhHT0Kn3y3fr4x2cp1+WIHBvTv0BfnD5U68trtWZPrVbuPKRgyLDcJz1R9T0s3ZDe5Ive590wDNnTCumdV0k3r8mPnflv9WWB+VjIUNKALkUHc6a7AwAAAC1oHIdep8jjigrbNptNv/ziFD35zTkqznXpQINPtz2zNlIZ/sYpo/W/G06SJI3omx+57tbzJ8bdu7LOm9YYmiz2EXek6nSnjg+z5pDuM1X7Y/eMt/qywHzMqtIee4n5HKa7AwAAAC3aFNIfffRRPffcc5Hfv/vd76qkpEQnnHCCdu7c2W6DAzqTy2HXvEllkqQ/L2/5HOe5Hbpp7lGaMKhIklSc69Lr3zlVyxeerivmjNKqW86Muse+mua0XquiNjrM22y2tCrpnbom3VQ+f3vrAT34emtlPNV093RCd1TlvW3N7gEAAIAep00h/Y477lBubq4kafny5br//vv1i1/8Qv369dNNN93UrgMEOtO3zxqvb5w8WpfMHK5vnDxaT183Rx7T1HippZo+qLjl818S00gu3ZBuJa1KegeX0s23j12Tf+f/NpjOSx7SrUJ8LIPGcQAAAECcNq1J3717t8aOHStJeuqpp3ThhRfq6quv1pw5c3Tqqae25/iATlVW5NHCcye0+fp0p7tbsafxlZnFrnDtyvwlQLK93K1CtfmQ5XZsSe5BRgcAAABatKmSXlBQoAMHDkiSXnrpJZ15ZsuUX4/Ho6ampvYbHdCL2NOopB/JdPfYdeVWEk13DwuvU7e6VTAqdCdfs97ye+LnAAAAgN6qTZX0M888U1//+tc1bdo0bdq0Seeee64kae3atRo5cmR7jg/oNdLbgi2zMGsYhr7774+1p7pJq3ZX64Yzxukbp4xJeH70FmzxSbzJF5TbaU9jurvVvRNv40ZIBwAAAFq0qZJ+//33a/bs2aqsrNR//vMf9e3bV5K0YsUKXXLJJe06QKC3SCenplEMj7LzQKP+teITvb31gBp9QS0yrSu3HkPy6e7hrvRWa+NTdXePDeJswQYAAADEa1MlvaSkRPfdd1/c8dtuu+2IBwT0FpfMHKZ/vLc78nsgjRbnydaJW8m0Qp1qunvj4f3drfdJT14Zjx26+R4d3bUeAAAA6C7aVEl/4YUXtGzZssjv999/v6ZOnaovf/nLOnToULsNDugOxg4oaNN1RbmuqN+tQnEs897l6UhnCr2ZeQhWXwg0+oKHz8u8u3ts9T1EJR0AAACI06aQ/p3vfEe1tbWSpNWrV+vb3/62zj33XG3fvl0LFixo1wEC2e7Zb52od39whoaV5mZ0XXFMSA+kE9KTVNKtm7VlNKSYSnqG091Np1uG+NjGcaxJBwAAAOK0abr79u3bNXHiREnSf/7zH33605/WHXfcoZUrV0aayAG9hcflkMflUJHHJSn93Q1azm+VzlT2ZJV0f9CQ2xldOU+no7tZqi3Ymg5X0q1CtXm6vtUXBrHVdbq7AwAAAPHaVEl3u91qbGyUJL388ss666yzJEmlpaWRCjvQ28SG7lTiKulplL2TVdK9gWDcsXSm0JuZw7IvYLUm/fB0d4uxBlJ0d4/fgo190gEAAIBYbaqkn3jiiVqwYIHmzJmj9957T48//rgkadOmTRo6dGi7DhDoLmJDd6bnp1P19ieppFtV2a3CdDKhFGvSm/zhxnHx9zWPLb016a2PCekAAABAizZV0u+77z45nU79+9//1gMPPKAhQ4ZIkv73v//p7LPPbtcBAt1FUW5m33nl57SebxiGct2OlNckr6THP+dPo2O8Warp7o2R6e7Jx2bZ3T3mkJGiGzwAAADQG7Wpkj58+HA9++yzccd//etfH/GAgO4q0+nuua7oUP6lGcP08vr9Om18f/30ufWW1ehk69atQnrmlfT01qRbjy2z7u5BGscBAAAAcdoU0iUpGAzqqaee0vr16yVJkyZN0mc+8xk5HKmrgQCkAUU5kcc2m00el0N//tpMSdKi/22wDLrJGsed+5s39cGP5kZV6DPdV91c7fZZrGcPh3SrxnD+lJX01mO/XrJJf1i2PfJ7eU2zDMOQzZbZlnEAAABAT9Om6e5btmzRhAkTdNlll+mJJ57QE088oa985SuaNGmStm7d2t5jBLqF8PZkknTvJdOSnvv7r05Xv4KchM+7EuxvbhWcza///Op9UcfS2dbNzBy+rdbIN/qTVdKTr0k3H/vN0s1Rz33/idW67Zl1GY0VAAAA6InaFNKvv/56jRkzRrt379bKlSu1cuVK7dq1S6NGjdL111/f3mMEugVzSP3MlMG67TOTLM+bOapUZ00amPReTof1v5o+iw7uZnnu6Mkx7THdfdLgIs0/bawk03R3qzXpAXMl3eLeKcay+O0dGY0VAAAA6InaNN399ddf1zvvvKPS0tLIsb59++rOO+/UnDlz2m1wQHdyzSlj9MxH+3TprOGSJEeCarjSyM3OBNem2lLN44oO9xlPdzcF6XDV3m6zRZraNfpaurtbTXffU926R7xVIA+y7hwAAABIqU0hPScnR3V1dXHH6+vr5Xa7j3hQQHc0un+BVt16pnKcLYHW5Wj7+mpngmuTrUmX4pvHpbP3ulnUFmyH72W32yJN7pr8LcesKvT3vNw6hd0qkKeqpAMAAABo43T3T3/607r66qv17rvvyjAMGYahd955R9dcc40+85nPtPcYgW4jHNAlyWFv079ekiRngmtThfTY5zMO6abzA4e3b3PYpLzDlfSmw5X0VFVxyzXpVNIBAACAlNqUIu69916NGTNGs2fPlsfjkcfj0QknnKCxY8fqnnvuaechAt3TkVTSE12bbJ90ySKkZzjdPXpNestjh9083T3c3T39+4RlOBQAAACgV2rTdPeSkhI9/fTT2rJlS2QLtgkTJmjs2LHtOjigO0u4Jv0Irk093T26sZxVd/dkW52ZC+Dh17LZbJE94Ksb/ZJSN6Szel32QgcAAABSSzukL1iwIOnzr776auTx3Xff3fYRAT1Eoinr6XAl6u6eohydzpr0YMhIuObdqru7w2bTsNI8SdLOAw0yDCNl4LZqWJdpp/lU9lQ36WC9T8cOLW7X+wIAAABdKe2Q/uGHH6Z1XqIKHdDbJOrQnta1GTaOO2/yID338T75giEZhqHdB5s0tE9uZF25WSBkyLR0PoplSLfbNKw0V3ab1OALqrLem7IJnNU42zukz7nzFUnS6985VSP65rfrvQEAAICuknZIN1fKAaSWKGindW2CKnyiLdXyDndf9wVC+tu7u/Sjp9bostkjLMNrsmZy5kwfnrJut9uU43RocEmuPjnUpB1VjSmbwFlV/NOd7l7T6JfDYVNBTnp/PK3dW0tIBwAAQI/R9vm4AJJKFLSNNDZKTzQhJVElPf9woPUGQvr5/zZIkv68fKeCVpX0JFPmzUE6HLTDEwJG9WsJwjuqGpSqKN7WSnqzP6gpt7+kY2590XIvdissdQcAAEBPQkgHOsiRVNK3VTZYHk9UBfeYKulWHdrNrI6FJVqTLkkjD1erdxxoSDndPXZtvPneya7dU90UeZzp9nEAAABAT0BIBzrIkaxJr2nyZ3S+29nyr3JLSG89brlfeZLwa34u/NB++H0M6ZMrSSqvaU5ZFbcK6eFr0t0vPd017OnMTAAAAAC6C0I60EGcCTq0d4ScqJDeGlqtprYnWtcuWU8dD1fScw9X65sDwZTT3a27u4f/ml6oppIOAACA3oiQDnSQTCrpseuv7/vyNE0ZVqKffe6YtK4Ph3RvIBgVtP0WQTdp4ziLlB5eWu9xtTxo9odSNoGzWpMevibdkB5MNi3fdA/WpAMAAKAnSbu7O4DM2I9gO8JPTx6sT08erDc3V6Z1fmS6ezAUNf3berp74kq61VT08PsIr3tv9gfbFNLTme5ufspq+zircZLRAQAA0JMQ0oEsYDuCQC9Jbof1mnTzXufhkGzVOO5Qg09bKutllYsdh2cE5DhbQ3qqarjVFmzhYJ20Qm6eqp/m2nkAAACgJyGkAx0kUe6+ZObwtO+RbhZ1R6a7x65Jb3l83alj9K8Vn2hfTXPkmNnnfveWdhxotLy3I1JJT3+6uzcQjDsWSqOSbl7LTkgHAABAb5QVa9Lvv/9+jRw5Uh6PR7NmzdJ7772X8NxTTz1VNpst7ue8887rxBEDbfPst07U56YNSfv8dPcKD1e5vYFQzJTxll+cDnukIn7+fcu0tbI+6vpEAV1q7e4e1Tgu8Ux0SW1fk25+LlnFPRC1Jp3ADgAAgJ6jy0P6448/rgULFujWW2/VypUrNWXKFM2bN0/79++3PP+JJ57Qvn37Ij9r1qyRw+HQF7/4xU4eOZCcuZJ+9MBC/ebiqTpmSLHl1PbxZYWW90g3f5q3YDMLd3d3OmxymbrNf+UP76Z3Y0nh/nfhNelefyjlNmrWa9LDf00vfCddk04lHQAAAD1Ul4f0u+++W1dddZWuuOIKTZw4UQ8++KDy8vL0yCOPWJ5fWlqqgQMHRn6WLFmivLw8QjqyTmmeO/L4fzecpAumxlfQn7v+RF18/DD9+qKplvdINa08LGFID1fS7baobvP7apqjOqQnE67Ah0N6kz+Y8lqfRRU8nUr6zgMNkcfp7+dOYAcAAEDP0aVr0n0+n1asWKGFCxdGjtntds2dO1fLly9P6x5//OMfdfHFFys/P7+jhgm0yYAijx78ynHKczsTNoabNLhYd144OeE90i0Ym7dgMwuv8XbaW6e7h20or9PEwUUp722PW5OeTnf3+DXpke7uSd7UTY9/ZBp7eiE92XkAAABAd9OlIb2qqkrBYFBlZWVRx8vKyrRhw4aU17/33ntas2aN/vjHPyY8x+v1yuv1Rn6vra1t+4CBDJ19zKAjuj7jSnpMV/VGX0tYdsVMd5ekqnqv0hFbSW/2B5UqF3uTrElP1hDOLPm0+FBa5wEAAADdTZdPdz8Sf/zjH3Xsscdq5syZCc9ZtGiRiouLIz/Dhg3rxBECRybtNemHA3ijN7qCXe8NSJIcdrucDpvlc6lEKumHm9OFDMnrj6+UmyXbJz3dLx7SXZOebugHAAAAuoMuDen9+vWTw+FQRUVF1PGKigoNHDgw6bUNDQ167LHHdOWVVyY9b+HChaqpqYn87N69+4jHDXQWtzO9/dPD091rmvxRxxsOB3GnwyaXPfpf9/rmludSdUePhHR36/VNqUK61T7ph8O01RZwVtLvAp+i1TwAAADQjXRpSHe73Zo+fbqWLl0aORYKhbR06VLNnj076bX/+te/5PV69ZWvfCXpeTk5OSoqKor6AbqLk8f110nj+qlvvjvpeeEt2GKryuFqucthkyHr56wCtVl4lrzbYY90rG9IUYVPtgVbupX0dNekU0kHAABAT9Ll090XLFighx9+WI8++qjWr1+va6+9Vg0NDbriiiskSZdddllUY7mwP/7xj/rsZz+rvn37dvaQgU7jdNj1lytn6aGvTk96XnhNeqwG03T32OAcDulW68fNwvuk22y2yJT38Fr3RJJNd2+fNemEdAAAAPRMXdo4TpIuuugiVVZW6pZbblF5ebmmTp2qF154IdJMbteuXbLHTNPduHGjli1bppdeeqkrhgx0ujH9C5I+nyikRyrpdltcGA8/12wxdd3jsqvZ33K+3dSZ3uOyq8kfTBnSrYLzyl3V2lxRl3ajt3TXpNM4DgAAAD1Jl4d0SZo/f77mz59v+dxrr70Wd2z8+PEp19ECPUmffLdK89062OCzfD4nQUgPB22nwx4X0rdXNejMu1/XMUOK464rzXNrb02zJMkRFdIdkvxq9KXXdC7Wmb9+Q//8RvKlLGHprkn3W0zXDwRDWvz2Ds0e01eTBse/PwAAACBbZUVIB5DamP75CUN6okp6mNNui+vI/vL6ChmGtHl/fdz5JaaQbrfHhnSpwZu8kp5M+pX09Ka7W93v8Q9266fPrZck7bjzvAxHCAAAAHSdLl+TDiA9U4aWWB4/dkixXA67Lpg6OOG1Tkf8dPdkk1FKTY3qzJX0cMU+VXf3ZNIO6Ukax5mbz1mF+TV7ajMfGAAAAJAFqKQD3cSCs47SjJGlKs13q0+eS26nXRW1Xk0c3LJjwW8unqZvnT5WX3hwuaobo7dic9rjp7sn08cU0k2FdOW6HUf2JiQF22GfdHOAtwr9Dr5+BAAAQDdFSAe6iTy3U2cfMzDq2Ii++VG/jx1QqJU/OlOX/+k9vbm5KnLc6bBZdlxPxLzlW9R0d2c7hPQk4Tv6vLavSXfaSekAAADonvg/WaCHsdttkX3Tw5x2W8r90M2GlORGHjvs0d3dj1S6XxbETmM3DEOvbKhQeU1zVJXdKsybO9IDAAAA3QkhHeiBcmLCtCvD+d+DSjyRx/Hd3VudPyXxOvhE6tNsOhcbvv/70V59bfEH+vIf3km5Jp3p7gAAAOiu+F9ZoAcq8riifjdXw9Mx2FRJNxelY0P6BW0I6elu3xaIqfz/9Z2dkqRtlQ1Ra9Jjz5Oip+izjzoAAAC6E0I60ANdOmu4nPboruzfO/toSdI3Th6d8vrBxa0h3dznLS+mcVyy8H/daWMsj6e7fZu5Qr63uknv7zgU+d0cvK0q6eb33nwEnegBAACAzkZIB3qgY4YU647PH6tpw0t0ycxhGtUvX9ecMlpvf/90ffdwWI9l3mu9b0Fr47iqBm/kcWFMhd5ma/lCwEquy7rJXLqV9HAQr6zzat49b0Q99/GemqjzYqvl5jXphHQAAAB0J4R0oIf60oxhevKbc7To85Nls9lks9k0uCRXDrtNZUU5cecPLGpdh26uRFfWtob0otzoDSEcdptuPX+S7rloatz9YqfGh2VaSb/vlc2qa44O9g+8tjXyeM2eGk297SU99HrrMb9pOvyR7OkOAAAAdDZCOtALDe2TF3es0OPUn782U//8xmzZTJXoyvrElXSHzSa3064TxvaNu1+iPdUzXZP+xuGt5OZYvIYkba1sUJ03oEX/2xA5Zu4gTyUdAAAA3QkhHeiFhvbJjTvmDYR08lH9NXNUqSTpa3NGSZJuOvOoyDlFnuhKejjMW+1Lnni6e2aV9HCoX3jOBI3ql5/skgjz3unN/vS3ngMAAAC6mjP1KQB6GnP39rDY/ctvOX+iFpx1lApyWv+YSNQ13qqBXHutSW86HOpz3Q6V5LmSXRJhfi9MdwcAAEB3QiUd6IX6WIRdbyA+zJoDutQyJd4svB+5yxEf0j0JprtnuiY9XAnPczuU7m5q5kp6U5LKvWGwPRsAAACyCyEd6IVKct1xx0b2TT2VPL67e8dW0gPBkHyHA3euy6FGb3rXeoOp16T/5Nl1mnPnKzrU4EvrngAAAEBnIKQDvdDsMS1N2NwOu566bo4+PXmQ7vrSlJTXxVXS27AmvT7NoO0PhtRsmrbucTnSXs/uT2O6+x+Xbdfemmb97d2dad0TAAAA6AysSQd6oWGleVpy08kqznNpQKFH9335uLSuK8rNYE16gunuWysb0nqtYMiIVN1tNinHadep4/vrb+/uSnrd+n21en/Hwcjvqbq7pzuFHgAAAOgMVNKBXmpcWaEGFHpSn2iS73bInMdt8dk8IlElPV2BkKFmX+tUd5vNpu+fc7SOKitIeI1hGDrnN2/qUKM/cozu7gAAAOhOCOkA0maz2aKayVlV0MM8RxjSg0EjMlU9HPgLPS5989SxCa/xBuIDOd3dAQAA0J0Q0gFkJN8U0u1JSukeV+sfLxceN1SXzR6R0esEQq0h3Rz4c5yJ/9iy6uT+1paqyHZuYeau7kkmAwAAAACdjpAOICP7apojjwcWJ54ubw7WhoyMK+uBUChqj/Qwd5KQXtvsjzv25uYqPb1qT9QxX5Ap8AAAAMhOhHQAGblk5nC5HDbd9+VpKvLE77ce5nLY9bU5o+Ry2DT/tLFJp8ZbCYSMSNO3PLe5kp447Fc3xod0SXrs/d1Rv5unxd+1ZJPuXrIpo7EBAAAAHYWQDiAjd3zuGH3wwzP16cmDU557y/kTte72szW6f4HqYqrcnz9uiM45ZmDCa81r0s1V+GSV9Oom65A+bkB0s7nYju/3Lt2c8J4AAABAZ2ILNgAZsdlsKs5LXEG326RffKF1z3WXoyVUx1a5BxV7knZeD4SMyL7ouWmuSa9u9Fkej92b3WvxuoZhyJasXT0AAADQCaikA2hX/2/eeH1h+tC44zUxVW6HzaaQkXiT8kAoFNfdXZJyXMlCevRrXDFnpCSprjkmpAfiG8xZdYYHAAAAOhshHUC7KPS0TMw55aj+ls9ffPzw6AM2m5JkdAVDhpqtGsc50g/p/QpyJEm1TX79/o2tevTtHZKs90636gwPAAAAdDamuwNoF299/3Ttr23W2AGFls+fe+xAPXXdHH32/rckSV5/MGortFiBBGvSc5J0ia9uip7u3jffLUn6cHe1Pth5SJJ00fHD4takS1KjP6g+Ce8MAAAAdA5COoB2UeRxJe32brPZNHVYSeT3JougbBY07ZNunu6eSSW99HBIN++TfqDBZzm1vckXiDsGAAAAdDamuwPoEk2+YOo16T6LLdiSrkmPqaQfnu5udqDea1lJb/KxJh0AAABdj0o6gC7R5A8m3U5t5a5qrdxVLSn1mvS++W4daPDFbcHWr8Add+6Bep/lmvRGKukAAADIAlTSAXSJlkp6euea165bbcE2rDRPklSTYLq7WVW917K7e6rp9wAAAEBnIKQD6BItoTi9lF6c1xq2zXuZzxxZqme/daIKclomBR2Kme4ePm52oMG6kk53dwAAAGQDprsD6BKDinMVSmMZ+DdOGa0Ljxti+dzkocU6ZkhxpLp+KKaSbg70YQfqvZZT5qmkAwAAIBtQSQfQqR6/+lP6/LQh+sG5R+usSWVJz71izkgtPGeC8tzW3ycGD0+DT9ZMLtaBeuvu7o1U0gEAAJAFqKQD6FSzRvfVrNF9JUmnHz1A/7n2BA3tk6vtVQ0a2idXNU1+2WSTNxDUlKElSe8VCB4O6c74vdNPG98/6vccp13eQEiV9V457PEVdqa7AwAAIBsQ0gF0GZvNpukj+kiSyoo8kqShfdK/PnC481zslmq//MJkfWH6UEnSby6eqv+s3KPPTxuiGx9fpTc3V1nei+nuAAAAyAaEdADdVuhwSN9YURd1vNDjjKxHv2DqEF0wdYj2VDfJ7bTLZzHVXWK6OwAAALIDa9IBdFvhSnp42ntYbVP8nudDSnL18k2n6NxjB1reK7YaDwAAAHQFQjqAbqdfQY4k6cyJAyRJd154rEb2zYs8P2lIkeV1w/vm6a4vTrV8rtEXH+wBAACAzsZ0dwDdzpKbTtbm/fU6fmTLAvYTxvTTa985Tftrm7X7UKMmDS5OeG2uO77JnMR0dwAAAGQHKukAup0++W7NHFUatw/6gCKPpo8oTXn9d+aNl9sZ/ccf090BAACQDQjpAHqd604bq7W3zdOXZgyNHGv0BfXR7mrdu3SzfIGQ7ntls97cXNmFowQAAEBvZDMMw0h9Ws9RW1ur4uJi1dTUqKjIet0qgN5jyboKXfXnDzRlWIk+2l0tSRpfVhjpGP/Hy2foT2/t0M+/MFlDSnK7cKQAAADorjLJoVTSAfRqJXkuSVJlbXPkmHlLtysf/UDLtlTptv+u7fSxAQAAoPchpAPo1Urz3ZKkvTXNSc+rrPd2xnAAAADQyxHSAfRqpXnutM5z2m2pTwIAAACOECEdQK9WnOtSOvnbQUgHAABAJyCkA+jV7Hab+qRRTXc5+OMSAAAAHY//6wTQ64XXpUvS8SP7WJ5DJR0AAACdgZAOoNcrznVFHs8a1dfynN61WSUAAAC6CiEdQK/nC4Yij48bUWJ5TqMv0EmjAQAAQG9GSAfQ6zV4WwP4oOJcy3PqvcHOGg4AAAB6MUI6gF6vwRTAR/TNszyHSjoAAAA6AyEdQK/3w/MmSJKuPHGU8txOvbPwDA0vjQ7r5mo7AAAA0FGcXT0AAOhq508ZrGnDSzT48FT3gcUeDSvN1a6DjZFz6gnpAAAA6ARU0gFA0tA+ebKbtlnLdTminm/2hxQMHXmL97pmv25/Zp0+3HXoiO8FAACAnoeQDgAWct3xE40a2mFd+q9e3KhH3tquz/3u7SO+FwAAAHoeQjoAWMh1xf/x2B7r0jeU1x3xPQAAANBzEdIBwEKeRSX96j+vaNcu7xfc/5Z2HWhMfSIAAAB6jS4P6ffff79Gjhwpj8ejWbNm6b333kt6fnV1ta677joNGjRIOTk5Ouqoo/T888930mgB9BY5FpX01XtqdPNTa4/ovrbWZe/6aHe1fvDk6iO6HwAAAHqWLg3pjz/+uBYsWKBbb71VK1eu1JQpUzRv3jzt37/f8nyfz6czzzxTO3bs0L///W9t3LhRDz/8sIYMGdLJIwfQ0/kDrU3ipg4riTx+fvW+dn2dQ42+dr0fAAAAurcu3YLt7rvv1lVXXaUrrrhCkvTggw/queee0yOPPKLvf//7cec/8sgjOnjwoN5++225XC5J0siRIztzyAB6iWpTeP79ZdO1bm+t/u9P76vJH5QvEJLb2T7fcRpH3jAeAAAAPUiXVdJ9Pp9WrFihuXPntg7GbtfcuXO1fPlyy2v++9//avbs2bruuutUVlamY445RnfccYeCwWDC1/F6vaqtrY36AYBUzBXuAYUenTi2X+T3s3/zhl7fVKlDDT7tPsiacgAAALSfLgvpVVVVCgaDKisrizpeVlam8vJyy2u2bdumf//73woGg3r++ed1880366677tJPf/rThK+zaNEiFRcXR36GDRvWru8DQM/0pRktf1bMGlUqSXI67Mp3t+ydvq2yQZc/8p4uefgdnXHX61q7t6bLxgkAAICepcsbx2UiFAppwIAB+v3vf6/p06froosu0g9/+EM9+OCDCa9ZuHChampqIj+7d+/uxBED6K7OPmagnrv+RC2+YmbkWFGuK+qcDeV18gVD+vF/j6yZHAAAABDWZWvS+/XrJ4fDoYqKiqjjFRUVGjhwoOU1gwYNksvlksPhiBybMGGCysvL5fP55Ha7467JyclRTk5O+w4eQI9ns9k0aXBx1LFCj1P7LIrm7+84pEMNPvXJj/8zSJI2V9TprS1VuvRTIzpiqAAAAOhBuqyS7na7NX36dC1dujRyLBQKaenSpZo9e7blNXPmzNGWLVsUCoUixzZt2qRBgwZZBnQAaE9FHlfC5/75wW5dcN8yvb/jYNxzZ/76Df34mXX66zs7O3J4AAAA6AG6dLr7ggUL9PDDD+vRRx/V+vXrde2116qhoSHS7f2yyy7TwoULI+dfe+21OnjwoG644QZt2rRJzz33nO644w5dd911XfUWAPQisdPdJan48LFF/9ugjz6p0dcWv5/w+pW7qmWTLeoYzd0BAABg1qVbsF100UWqrKzULbfcovLyck2dOlUvvPBCpJncrl27ZLe3fo8wbNgwvfjii7rppps0efJkDRkyRDfccIO+973vddVbANCLFHri/8g855iBeuz91l4Xdc2BhNcb7LcGAACAFLo0pEvS/PnzNX/+fMvnXnvttbhjs2fP1jvvvNPBowKAeFbT3aeP6BMV0pMxJBnUzgEAAJBEt+ruDgBdyeOK/yNzwqCiqN8LcpJ892lIFNMBAACQDCEdANIUCMUn7IkxId0maX9ts/ZWN8Wda8iQPxiKPkZqBwAAgAkhHQDSFIwJ6T+/8FjZ7Tb1NW29VucNaOYdS3XWr99Qoy9+fbovJqQDAAAAZoR0AEiTuZL+5ndP05dmDJMk/fqiqXHn1nsDKq9pjqqcG4bkC0SH9GDIkGEYemFNufZYVN8BAADQuxDSASBNV544SpL0uWlDNKw0TzZby3ZqJx/VX29///Soirok1TYH1OwPRn43DMkfjK7GewMhvbv9oK756wrNufMV1TT6O/hdAAAAIJsR0gEgTWP6F2j1j8/S3V+aEvfc4JLcuL7ttU1+NZlCeiBkxFXSvYGg1u6tjfx+95KN7TpmAAAAdC+EdADIQKHHFamgx7LHHK9p8svrbw3lzf6gvDEh3RcIac+h1mnuT63aGxfkAQAA0HsQ0gGgnfzkgkk6bniJRvfLlyTVNkdX0pv8QfkCwahrvIGQdh9qjPxe0+TXaxv3d86AAQAAkHUI6QDQTs45dpCe+OYcHTeij6SWwG1ek75i5yHVNkd3fPcGQtp9sCWkDynJlSR9uLu6cwYMAACArENIB4B2VpzrkiTVNgXU5AsmPTcYMrShvE6SdMKYvpKkiprmI3r9ANu8AQAAdFuEdABoZ0WelpBe0+RXcwbry2eMbKnA70szpL+/46B++uy6qGr9w29s06RbX9QqqvEAAADdEiEdANpZca5T0uE16Skq6WFnTizTiL4ta9nLa9ML6V98cLn+sGy77ntlS+TYz55fL28gpIVPrM5w1AAAAMgGhHQAaGdFkenufnkDqUO6y2HTw5fN0ODiljXp+2qaZBixG7oltnpPTdsGCgAAgKxDSAeAdlZsCunpVNIHFHpa/lqUI0lq9odU0+TXe9sPak91U7JLJUkN3kDKcwAAANA9OLt6AADQ04Qr6R99UqNtVetTnt+/sCWce1wO9c1360CDT69u3K+bHv9IkrTjzvOSXt+Q5pR6AAAAZD8q6QDQzsKVdEmqa05d5R5wOKRL0sDilqr6KxsqI8f8Kbq1N/qopAMAAPQUhHQAaGdj+hfopHH90j4/PM1dkkrz3ZKit1GbcPMLemtLVcLrG6mkAwAA9BiEdABoZw67TX+5cpbGlxWmdf65xwyKPA5v32bu8B4IGfruvz9OeD1r0gEAAHoO1qQDQAc5dmixNlbUSWrZYq1fQY7+8d6uyPPfOHm0Th0/QLPH9I0cK/S0/LFcEbNXeihJt3erSnom3eEBAACQPQjpANBBThjTV/9e8Ykk6YyjB+ii44dp3IACTRpcpKMHFqk4zxV3TTikx+6VPqpffscP2IJhGPpwd7XGDShQoSd+vAAAAGhfhHQA6CCfmzZEQ/vkqd7r14lj+8tms+lrJ45Kek04CIdiCuE1TX6V1zRHGssFYprJ+QIhuZ2Zr2Bas6dGH39So9J8t/oXujV9RGnU8y+tq9A3/rJCxwwp0rPfOinj+wMAACAzhHQA6CA2m00zR5WmPtEkXEmPtXZvrT61aKleXnCKxg4okC8mpFc3+jSgyJPxGD/922VRv8du9/bCmnJJ0po9tRnfGwAAAJmjcRwAZJHYKeVnTxoY9fvTq/bIMAz5AtEh/WCjr0PGM6JvXuRxPQ3qAAAAOhwhHQCySGwlPTy9Pey3r2zRVX9eERfSK2q9cVPg2yK8J3uzv6UZXZ7bEXlu8+EmeAAAAOg4hHQAyCKxIX1wSfwU9pfXV8gbE9LLa5ripsC3hTcQ0k+eXacJt7ygjeV18vpb77mxvOND+po9Napp8nf46wAAAGQrQjoAZJGimOnu/QtzLM872BA9vX17VaN+8cLGjF7Lapu2Zn9Qf1y2XYYh3fPyJjUHWrd321pZn9H9M/X21ip9+rfLNPfu1zv0dQAAALIZjeMAIIvEVtILcqy3PYvdou3B17dG/R6MbQ9vwaryHp7mLkmBkBFVSbfaj709vbS2QpJUWeft0NcBAADIZlTSASCLxDaOy89xWJ63Pyakx0pn6nuzzyqktx4LhoyoafWx6+Dbm80W/fvHn1TrmY/2duhrAgAAZBsq6QCQRWIr6flu6z+mK2qTV5vTCdRN/vjK+CNvbY88DoSMqMq6vx3WvCdjj0npn7nvLUnS0D65mja8T4e+NgAAQLagkg4AWcTlsMvjav2jOVElvSJVJT0QUkVts2b89GX99Nl1ludYhfS/v7sr8viNTZX614pPWu95OKR7A0H9+L9r9camyqRjyJQtwfEt+zt2LTwAAEA2IaQDQJYpK2rt6J7ndur4kfFV5IrD67bN+5ib+QIhPb1qj6rqvfrDsu1RFfGwpgzXmIer839ZvlOL396hyx55L6PrU4md7g4AANAbEdIBIMucPWlg5HG+26k/XH68Fl9xfNQ54TXp/Qty1DffHXcPbzAkh731j/h3th3QG5sqtfit7QqFDL21pUrn3vtmRuMKr0/fdbAxo+vS8ZflO/Twm61T7c2d51O3wAMAAOg5WJMOAFnm4pnD9dAb2yS1THd3Ouw6dfwA/e3rs/Tj/67V5v31ke7ubqddYwYU6MD2g1H38AVCOlDfum799U2V+tNbOyRJ/qChX76U2XZtLde1hHSLnduOiGEYuvnptVHHYveBBwAA6C2opANAlhnVL1//uXa2nrpujpyO1j+m54ztp4uOHyZJqm70S2oJ6SNKrae8l9e0rlvfXNG6rvtnz69vU6f2jujuvmZPjX7w5Oq447VN/tZfKKUDAIBehEo6AGSh6SNKLY/3yYue2u522FVqMd1dkvbWNEUet0fztUTbujX7g2rwBtS3ICfje376t8ssj9c2t4b0YHuX7gEAALIYlXQA6EZKC2JCutOuMf0LLM/dW91aSS9P0Q0+Hf6AdVg+89eva/pPX07ZcT4TNU2ByGOvRdM7AACAnoqQDgDdyLA+0VPbc5wOfXbaEH126uC4c/dWN8UdOxLhSrphmn9uGIZ2H2x5nTc3V7Xba5kr6c2sTwcAAL0IIR0AupFhpblRW5W5nXa5nXbdc/G0uHMDoZYwnee23ms9U+E16ebZ54mmwB8p85p0q+3jAAAAeipCOgB0IzlOhwYX55p+T/3H+HHD4/dZbwurQN7o7ZgAHR3SqaQDAIDeg5AOAN3MiL6tU97dKUJ6YY5TYwdYr1nPVLiSHjJV0g81+iKPjXZs8Fbb3LomnUo6AADoTQjpANDNRIV0R/I/xkcPKNCAotau64kC++h++SlfNxzSvYHW0HyosbXiHUoQ0g3D0I6qBgVD6Yd4cyWdPdMBAEBvQkgHgG7G3M29ONeV9NxxAwpUVuiJ/D62f4E8rug/+qeP6KNPT4lvPBfLf3i6u9c0/byq3ht53Oizrnj/e8UnOvVXr+kHT7Tuh15Z59VX/vBuwtcyN46juzsAAOhNCOkA0M18ccYwXXfaGH3z1DH6wvShkeNnTxoYd+5RZQUqK2oN6UW5To3sG101z3M7FAxFV6sLPU5df/rYqGOBkKFQyIiafv6Nv6yIPE4U0n+9ZJMk6fEPdkeO3b1ko5ZtSdwNvsa8Jj1ASAcAAL2Hs6sHAADITHGuS9+Zd3Tc8V9+cbLOmDBAVfU+/fyFDZKkcQMKVWaa7l6Q49KofvnaUF4nSTp6YKF+/JlJ+uf7u6PuFQoZctjjv8f1BUMJp583JQjpNnM7+sOq6n0WZ7aqbTKvSWe6OwAA6D2opANAD1HocemLM4bpouOHRY4dNbBQA0yVdJfDplGH159PHVaiF248WWP6F0S2awsLGoacjvhw7Q2EEjZyS1RJt5LrSr4tXNR0dyrpAACgF6GSDgA9TGm+Ww9fNkP1Xr+GlORGdV1v9AU1Y2TLlmzmCntsU7dQSHLY40O6PxhKOP28yR+wPG4ldl18LLZgAwAAvRWVdADogc6cWKbPTWtZr26ebt7oC+qcYwbpts9M0sJzJkSOXzxzWNT1158xVk5TSA93kfcFQlGN48wafUFtKK/VZY+8p492V0eOm2e7/+DJ1WrwBuRM0ZU+ak06jeMAAEAvQiUdAHqRqcOK5XbadfkJI6OOHz2wSCt+NFf5OU5trqjXpMFFemtra2M3l8MmX7AlpCeqpDf6grpy8QfaU92k97Yf0IafnKO91U1RVfG/v7tLf393V8pxmrd2I6QDAIDehJAOAL3AywtO1ltbDujimcMTntO3oGX6+7FDiyVJJ47tpzs/f6yOGlioKxe/rwZfUP5g4kr6G5sqI03lmv0hHWzw6YQ7XznisTPdHQAA9CaEdADoBcYOKNTYAYUZXWOz2SKh3u1smZ6erHFcbNd385T3I5GomzwAAEBPxJp0AEBK4ZDuC4bUnGZoNq8rPxJeprsDAIBehEo6ACAl1+FGb5//3dtpX/PDJ1e3y2snWgMPAADQE1FJBwCk5E7Rjd1KQ5r7pv/piuP120umJXzeHzT0yxc3ZPz6AAAA3REhHQCQkqsNIT0dr/2/U3Xa+AHKcSa//1/fSd0RHgAAoCcgpAMAUqqs87bbvSYMKoo8zs9pWXXldLRupn7q+P6Rx3d9cYqklvXtbMUGAAB6A9akAwBSKq9tbpf7DCzyaO6EAVq/r1aSVOhp+c+QzdYa0n936XGSpF0HGzW+rFA/emqNmvxBrd9Xq/11Xp05oUx2uy3+5gAAAD0AlXQAQEZ++tlj5HFF/+fjy7Os918fVpobeTxlaLH+cPmMqPXt4Wnu5sjtdtiV53bq6IFFstlsGljskSRd/9iH+sZfVujnL27Q/1bv07vbDrTTOwIAAMgeVNIBAGk7emChvvKpETr32EF64LUtmjdpoCRp+og+GlTk0V1LNkXOHV6ap6XfPkW/emmjThnXXyeM7SdJentrVeSccAXdbqqkO2Kq5AOLPNpe1aDdB5skSQ+9vi3y3PZF50ZV4QEAALq7rKik33///Ro5cqQ8Ho9mzZql9957L+G5ixcvls1mi/rxeDydOFoA6H3u//JxmjS4KDIVvTTfrR+eN1EzRpZqxshS2Ww2XX3KaP3ovAm6+PhhcjvsuvtLU+Ry2LXwnAmRgC5J4wYUxt3fHMxjQ3e4km6l3huwPF5V75VhGBm9RwAAgGzQ5ZX0xx9/XAsWLNCDDz6oWbNm6Z577tG8efO0ceNGDRgwwPKaoqIibdy4MfI7VRQA6FjnTR6k8yYPSnpOjtOhr580WpJ0+wXHyJ2gY/up4/vrh+dOiGogd/zIUh1VVqDR/Qrizo8N6W6nXb5ASFJLQ7tCjyvq+cfe26XvP7FaP/3sMfrKp0bE3W9fTZMq67yaPLQk6fsBAADoCl1eSb/77rt11VVX6YorrtDEiRP14IMPKi8vT4888kjCa2w2mwYOHBj5KSsr68QRAwBSSRTQpZY/w686ebROHNcv6vwXbzxZD351etz5A4taQ/rcCWV68psnRH6P7TofChn6/hOrJUm/fHGjrMxe9Io+c99b2lZZn96bAQAA6ERdGtJ9Pp9WrFihuXPnRo7Z7XbNnTtXy5cvT3hdfX29RowYoWHDhumCCy7Q2rVrE57r9XpVW1sb9QMAyD6JZkXNHtNXBTlOOew2fXryIE0aXKyZo0olSZX10SH9HVMzuSEluVHP7atpitrG7eNPatpr6AAAAO2mS6e7V1VVKRgMxlXCy8rKtGHDBstrxo8fr0ceeUSTJ09WTU2NfvWrX+mEE07Q2rVrNXTo0LjzFy1apNtuu61Dxg8A6HhHlRVq5c1nKmQY8rgckqT+hTmSpH9+8IlOPqq/ig5PeV/1SXXkuk0VdVqyrkJTh5Xo40+qdeWjH+jMia3/vQmxZh0AAGShLl+TnqnZs2dr9uzZkd9POOEETZgwQQ899JB+8pOfxJ2/cOFCLViwIPJ7bW2thg0b1iljBQC0j9jp8/0LWkL6G5sqdctTa3TPxdMkSVv2t05hD4QMXfXnD1SQ41RxbkuIX7KuIvJ8VUwVHgAAIBt0aUjv16+fHA6HKioqoo5XVFRo4MCBad3D5XJp2rRp2rJli+XzOTk5ysnJOeKxAgCyR7iSLklPrdqrt7Ye0N+/Pktb98evM6/3Biy7wFfV+zp0jAAAAG3RpWvS3W63pk+frqVLl0aOhUIhLV26NKpankwwGNTq1as1aFDyrsMAgJ4jJ6ayXlnn1a3/XatNFek3g6uqi6+k1zT59dU/vqufPLtOjT7r7d0AAAA6UpdPd1+wYIEuv/xyzZgxQzNnztQ999yjhoYGXXHFFZKkyy67TEOGDNGiRYskSbfffrs+9alPaezYsaqurtYvf/lL7dy5U1//+te78m0AADrR+IHxe62/vbWlaZzdJoUslptPGlyktXtbm4fGNp2TpBfXluvNzVV6c3OVHHabfnDuhPYbNAAAQBq6fAu2iy66SL/61a90yy23aOrUqVq1apVeeOGFSDO5Xbt2ad++fZHzDx06pKuuukoTJkzQueeeq9raWr399tuaOHFiV70FAEAnO3FsP9335Wka1S8/7rmZo0p1ycyW3iM/Pn+ipgwr0Ylj++lP/3e8fnHh5Mh5b26u0ke7q/XHZds16ZYXtHLXIa3YcSjy/OsbK7Vy1yEZhqE/vLlNb2yq7Pg3BgAAej2bYfSu9ra1tbUqLi5WTU2NioqKuno4AIAjsL+uWf9e8Ym+cNxQNfqCChmGBha37Ku+ZX+9Jg8tibtm7d4anXfvMkmS22GXLxiS1BLuq+q92lbZEHX+pycP0rMft3xZvOPO8zrw3QAAgJ4qkxza5dPdAQBoqwGFHn3z1LGWz1kFdKm1M7ykSECXpFW7q+ULhOLODwd0qaUJXUEO/+kEAAAdp8unuwMA0Jn6FeTopHH94o6HA/qls4YnvHbXgcYOGxcAAIBEJR0A0MvY7Tb95cpZkqQdVQ0qynVp6foKvbSuQpfPHqkTx/XT397dZXntroMNmji485ZKPf7+Lv15+U49fNkMDS7J7bTXBQAAXYdKOgCg1xrZL1+l+W59ccYwPXzZDJ1oUWE/79jWLT53JqmkG4ahRc+v18+eW6fYdi+GYejv7+7SPz/Yra8/+oG2WOznbuV7/1mttXtr9auXNqb5jgAAQHdHJR0AgBifnzZET3y4R1fMGalbz5+k0S9t1G9f2aIdCUK6YRjaW9Osh97YJkn64oxhOqqsZZu4uma/nlq1Vzc/tSZyfkVts5751onaVFGnklyXBhR5ko6ntsnfTu8MAABkO0I6AAAxbv/sMTprUplOHT9AkjSyb8tWb6t2V8swDIUM6bZn1qo0360bzhinO1/YoIde3xa5/tUN+3VUWaF2HWjUFx58W/vrovdkX72nRn97d6d++OQaje6Xr1f+36mSJH8wpGv/ulJ989266uRRkfPtNlsHv2MAAJAtCOkAAMQoyHHq7GNap7mfdvQAuZ12rd9Xqw93V6uyzqs/L98pSXI57FEBXZKWrKvQ1SeP1rce+zAuoIf98MmWyvq2qgbVNftV6HFpxc5Denl9hSTp8Q92R87tVXulAgDQyxHSAQBIoTTfrfMnD9Z/Vn6iGx9bJW8gGHnuly/Grxf/YOchjVr4fNr331Bep+dX79Of3tph+fzqT2q060CjhvfNixxbs6dGtc1+nTAmfh09AADovmgcBwBAGhacdZSGlORq18FGVdR6lWgG+sRBmXd/f/z93QkDuiSV1zbrjLtf05MffiJJCoUMffHB5fryw+9q5a5DGb8eAADIXoR0AADSMKQkV/+8ZrbmjO2r0ny3Hrn8eN16/kRJ0pkTy/SLCyfr/04YqSevO0E/v/BYfev0sbps9gg9+rWZKe/97xWfRP3+8oKT487xBw3d/sw61XsDemrVHjX5W6r5D72+tR3eHQAAyBZMdwcAIE1DSnL1t69/SoZhyGazyTAMHVVWqOOG91Gu2xE576Ljh0ddd/sFk2S32bRlf70Wv71DkjR1WIm+dfpYXfnoB1Hn/vILkzV2QKHl6x9q9Osrf3hXq3ZXR44tXb9f3kBQOU5H1LnhMa7YeUjvbT+ob5w8WnY7DegAAMh2hHQAADJkOzzX3Wazac7Y1GvCL5s9MvI4HNJnjirV6UcP0J/+73hdsfh9SdIXpg/VF2cMkyRdeeIo/XHZ9rh7mQO6JAVChl5Zv1/ry+t02ewRKshx6tq/rtDybQd0zSljdM/LmyVJg0s8umDqkEzfKgAA6GRMdwcAoBP965rZumz2CN1wxjjZbDaddvQA/fXKWTr5qP5acOZRkfO+f87RevZbJ2ry0GJJLaE+kWv/tlL3Lt2sh17fqlc27NerGyvV7A9FArokba9qiDx+eV2Fvvm3FTrY4JMkNfuDMgx6yAMAkA1sRi/7r3Jtba2Ki4tVU1OjoqLMm/sAANCZapv9qm7w643NlfrRU2uSnuuw25Tvdqi2ORD33MXHD9ONc4/SLU+v0UvrWrZ5u2TmcF0yc5i++OByXX7CSP3g3Akd8h4k6ZUNFaqo9eqSmcNTnwwAQA+TSQ6lkg4AQBYr8rg0vG+eLpg6OK5z/JkTy6J+D4aMSEAfO6Ag6rnH3t+tTy1aGgnokvT6xv265i8r5A2E9Ps3WvZ6//5/PtZX//iuvIFgS5O6D/dEtpxbur5C6/bWSpI+2l2tP7y5TaFQ63f9uw826rH3dikUMhQKGfrz8h3asr9egWBIX1v8gRY+sVo3Pb5Ky7ceaKe/OwAA9DysSQcAoBso9Lj01HVztGp3tcqKcrT7YJOGl+bpjU2VOm38AG2trNfm/fWSWqbG3/2lKbrvlS0qr23WaxsrLe+5t6Y56vf9tc167P3dkqRjb31J/lBIhiG9u32YLp01ItLkbusd5+qC+9+SJPUtcOtz04ZKkj5z3zIdavSr0RdUyDD00+fWK8dp13PXnxh5jSc/3KMnP9yj7YvOjaztBwAArQjpAAB0E26nPbI2fUTffEnSypvPlNtp1wc7Dum51Xt149yj1K8gR5J054WTVdfs17E/finqPscNL9GBBp92HmiMOv7c6n2Rx75gKPL4H+/tjqriv75pf+Txur21mjcpoPtf3aJDjX5J0gtryyNr3L2BkNbtq4t7L7sONkbeAwAAaEVIBwCgG8vPaflP+ewxfTV7TN+45ws9Lp177EB9/EmNxg0o0DnHDtKXZgzTsx/v1fy/fxh17r8++CTu+rCbn14befy1xa3bxlXWefWbpZv10OvbIsfe234w6tq3t1TF3W/51gOEdAAALBDSAQDo4X536fS4Y2ccXaaBRR4FDUODS3L10e5qrdtXm/G9N++v1/aYinys8BR6sz8v36kmf1CXzhoht5MWOQAAhPFfRQAAeqFct0Mv3HiSXrrxZE0a3DqVffbovtrwk7P12amDI8c+PXlQ5PFNc49Sab478vuW/fWqrI1e256Odftqddsz63Tj4x/qusNbyIWnyG+vatCmivgp8gAA9AZU0gEA6KVK8lrC9qdG99Xf392lT08epLu+NEU5Tod+8tlj1K8gR1+YMVRj+xfIabfJ6bDr+jPGav7pY7WxvE5fePBtNfqCkQZ0uS6HmvxBy9daeM7RWvS/DXHHn19dLqllPfxzH+9Tca5L7+88KIfNpv/OP1ETB7NdKgCgd2GfdAAAoIMNvqgKeTr+uGy7fvLsOknS+LJCvXjTydp5oEHlNc2aOapUWysb9LPn1umqk0br+FGluvCBtzWo2KOjBxbpN0s3p7z/rFGl+sUXJus/K/fohDF91eQP6tSj+stms8kwjEh3ePPjsJfXVShoGJo3aWBG7wkAgI6QSQ4lpAMAgDYxDEPPfrxPa/bW6LxjB2ny0JK0rgsEQ1q3r1bHDimWP2jI7bRr7d4ardh5SNsqGzRzVKluenyVvIFQ3LVfmjFUWysbtGp3ta45ZbSuPHG0Pve7t1Sa79ZjV39KlXVeuRx2zbpjqSRp+cLTVVXn01/e2aGhffLkD4Z049yj5LC3BH1/0JA3EFShx9Wef2sAAIhCSE+CkA4AQPZ7beN+XfvXlQmnzyczoDBH++u8kqSfX3isfvHCRh1o8EWe/+lnj1GO066FT6xWIGSoIMepp+fPUZ88t577eK/OOXZQZBs7AADaAyE9CUI6AADdw97qJj29aq/8wZDe33FQDrtNDptNpx49QFV1Xj385jY1+jIP8TabZPV/P8W5LtU0+TV5aLH+c+0Jcjnsaj78JYHH5VAgGFJdc0B9MlwWAAAAIT0JQjoAAD1DdaNPb2yuUo7Trne3HdSIvnn6cNchPbVqr+X5LodNOU6H6r2BtO4/un++dh9slD9oRIJ9vtuhx66erWOHFmc01k8ONWpISW7c2vkwfzCkYMhQyDDkcTpkt1ufBwDongjpSRDSAQDouQzD0P46rwo9Tv1vdblqmvzqW+DWqeMHyGG3KWQYuuvFjdpT3aTzJg9SKCQ5HTbtOtCoMQMK1OwP6v/96yOFkvzf0Zj++frheRNkGNLxo0pVmOOUP2jonW0HNK6sQEUelz451KQN5bV68sM9em1jpSTpijkj9f/OGq9fvrhR+TkOfeVTI/TUh3u1o6pBz6/Zp7rmli8PPjNlsH5z8VRV1fv02sb9euStHZo5so9uPX8S4R0AuilCehKEdAAAkMzL6yr05uZKHTeij8YOKNCw0jy9sKZcL60t18vr90ed63HZleN0qKbJ365juGz2CP33o72qbmy97w1njNNNZx6V1vXbqxr02sb9umTmcHlcjnYdGwAgc4T0JAjpAACgrXYdaNSvXtqotXtrVFnnVW1zelPnkxnRN08j+ubrjU2VKc89/egBKvQ4Vd3oV57boYuOHyaH3ab3th9Uv4Ic7TjQoFmjSvWbpVu0fl+tXA6bfnTeRDX6gjpQ71WjP6g8l0NV9V7tONCok8b109fmjFKffLf21TQpZEhDSnKP+D0BAKIR0pMgpAMAgPbgD4a0YV+dHHabBhTlKNfl0MvrKzRxUJFK8tzaW92kkGFowqAiuRx2bausV6MvqKBhaFifPC1dX6FTxvfXoOJcBUOGHnx9q0b0zdMnh5r06Ns7lOt26OHLZmh0v3x9/oG39eGu6g57L33yXKptDijf7dCr/+9U9Y3pbm8Yhl7bWKnjhvdRcZ4rcuzNzVWaMKhITrstqqGeLxDS8m0HdMKYvnI57NpcUafiPJcGFHo67D0EQ4aa/UHl5zg77DUAoK0I6UkQ0gEAQHezZX+d7n91q4aU5CrX7dCAwhy9sblKz3zU2iSv0OPUrFGlen1TpfzBlv+9y3M71OgLyu2w69Tx/bVkfYVlZ3uzKUOLdeH0oVr9SY3Ka5t105lHacWOQ/rZ8+s1YVCRfvrZYxQIhvT0R3v193d3Ra77+YXHatrwPrrl6TV6Z9tBSdI5xwzUxEFFumvJJhV5nFr0+ckaXOLRss1VqvcG9KXjh2lvdZPe335Q72w/qEZfQCP65uvMCWWaNrxEI/rmS5Iq67ya//eV+tTovrrpzKPU5Auqqt6rAUU5cjvs8gZC+sETq/Xc6n1afMVMzR7TN+o9NfuDChmG8twEeABdg5CeBCEdAAD0BIZhaPP+euW6HBpc0lKNdzvtqqzzavWeap00rn+kil2S51b/whwdqPdqU0W93tpSJbtNMiTl5zh1sMGnqjqvnvhwT1e/rShFHqdG9y/Qqt3VkWO3fWaSfv/GNu2pbrK8piTPpVe+faqWbalSvtuhDeV1+us7O2UY0nPXn6i+BTkyDEM/fGqNdh9s1INfmW5ZfQ8EQ1q1u1qTh5bI7bTrUINPdrtNxbmujnq7AHowQnoShHQAAIB4Dd6Arlj8vpp8QZXkubR+X628gZAavIGobvcuh01Ou13eQFC5LoeOH1Ua6WAf3qrO7bRrQGFLlXtIn1zNGFGqqnqv3txcKW8gpH01zZH7jRtQoAKPUxv21anJn/m+95ka1S9f26saoo595VPDNbgkV33y3KqobdaaPbVatqVSzf5Q3PVuh10zRvbRxEFFWl9eK7fDrhvmtlT3a5p8CoYkXzCoem9QlbXNGlSSq0mDi2QY0sBijw41+pTncuqTQ43qk+9W33y3tlY2aFCxR26nXWv31mpfTZMuPG5o5MsDwzAUMiS7rWVav9Nh7/C/TwDaFyE9CUI6AABA+vbXNmvHgUaNH1gobyCo/gU5stls8gdDsklyOuyqa/Zr8/56jRtQoEKPS4ZhJNwTPmxbZb3sNptG9suPHAuFWrayK8lzq6bJr50HGrT7UKP65LlVlOvS06v2KBRqCbtr9tRoT3WTyoo86pPnUp88t+YdM1Df+8/HKaf0dwdFHqcmDCrSoUaf9hxqkjcQksthV5M/qMHFHhXlujSqX75cDrtChqFtlQ3qk+/S2P4Fys9xyu20a3+dVy67TXXNAQVChryBoPrkuVWQ41Shx6WKumbVNvk1e0xf+QIh9SvIkcfl0MyRpSrKdepAg08el0Nef1Cl+W6FjJYvDDL9ksAbCMomm3YdbNCY/gUpPxtAT0RIT4KQDgAA0P2F/xc2NvC9vbVKG8vrdNK4/jIMQ6v31OjjT2o0b9JANfoCavIHZRgt69SXrt+vBl9AkwYXa+3eGm3ZX69Pje6rMf3zI/d9Z9sBOew29clzq7bJr40VdXI57Dp+ZB9J0oqdh1TT5FdxrksleW7VNwdawvDhKnl1o19bKuuVEwnNdvmCIQ0s8sgXDOlggy8yA8Fua/nSIxAMRc1e6GxOuy3yhUDYkJJcNfoCavAFNaZ/gYKhkBp9QTX5gnI6bJowqEj57palE/XegApynAqGDNV5A9pUUaeg6Q0N7ZOrISW5MiQdqPdqRN98uR129S1wq94b0LbKBg0s9shuk44dUqxCj0s1TX6N6JunPLdTjb6WXRVqm/zaeaBRA4s9ynU7VFXnkz8Y0qASj0IhQ/k5Tjkddo0vK1SBxymX3abg4VkJuw82Khgy1OANaEifXNltNjX7gyr0OA/PWrCprtkvu82moX1ytaG8TqX5bhXnuuR02DSg0KPdBxvlC4Z0oN6ncQMKlOt2yOWwyyapzhtQjtOecgvE3QcbVZznUpGHZRQ9HSE9CUI6AAAAuoI/GJLDZpMvGFKO0y6bzaZAMCSH3aaaJr/yc5xyOewKhgyt2l2tPdVNKsl1aWg4RAaC6leQo22VDapu9GnngUaFDEOGpDH9C3SowaftBxrU7A/KGwip0OOULxCS3dZy/yJPS8D0BULyBUNyO+zaX9esvdXNKs13a2tlvQ41+Npla8GerjDHqTpv9N8nu01y2G0yDClw+EsJj8veMhPk8MwFj9MhXzCkwSUe1TUHtPNAY+RLIEny+oM6amChQoahTw41afThmSZ5bofycpzKcdpbvghpDqiy3iu3w645Y/upyONUfo5Te6ubVNPkV5O/5bMyql++GrxBHWr0qbrRp1y3Q3lup4pzXWr2txx32G3yBwwN75snu82m6kafDjX6VNccUP/CHPkCIfXJdysUMrS9qkEFHqeKPC7lOFs+q43+oApynBpY5FG9N6A9h5o0sNgjj8uhPdWN6pufozy3Q47DX/44HTY57TbZ1PoFW8unuJU5oVqF1fwcp5x2m0KGoWDIkGFIn548KKuXghDSkyCkAwAAANZCoZZwGDIMDSrxqNkXUtAwtGr3IRXkuJTndqiyzttSJXY7lOd2qMEb1Lp9tQoGW8Jcocepem9QhmHIGwipJNel6ka/jh5UqKp6r5p8IfmCLVV6j9OhitpmBY2WyniOy67SPLcONPjkcti0Zk+tAqGQcl1O7T7YKH+o5QuOQNBQocepkf3yVVHbrHpvUENKciUZWrevTh6nXXXNAdV5/apu9KvZH1QwZMhxOBz2yXcp1+VQjtOhirpmOe025TgdOtTok9PeEh4LPS41+gI61OhXoccpbyAkGZI/FIoKkTnOlh0G0LXW3T4vq3dwyCSHZu+7AAAAANCp7HabhvfNi/ye42yZrn360WVJr5s+ok+HjqurGIYRmXUgtSyvqPcGtLe6SWWFHhV4nLLbpHpvQLXNATX7g3LZ7SotcCtkGKpu8Ku6yafqRr9K8lyRLwpavuhw6KiBBWrwBlXfHFBts19Ou00HGnySWqrwew41qTjPrWZ/y9KCJn9QpXluGTLkC4QUCBmqrPOq3htQfXNATodNHpej5YuMkKFDDT4ZknJdLZVsqaWxY11zQPmHp+fXNvvlsNsjlfY+eW71yXMp193yxUie26F6b8t7K8hxKcfVMqU//MWE02FTsy+og41+hQxDo/vlq6reK28gpP6FOapt8ssbCCkQNBQIheQPGlHLHyTJvGglegWLLe64YUh1zf6WL0psksNmi3z50lMQ0gEAAADAgs1mi3xREVaQ49RRZYVRxwo9LhVarCsv8rg0XHlxx6MvPuJhoofJ3kn7AAAAAAD0MoR0AAAAAACyBCEdAAAAAIAsQUgHAAAAACBLENIBAAAAAMgShHQAAAAAALIEIR0AAAAAgCxBSAcAAAAAIEsQ0gEAAAAAyBKEdAAAAAAAsgQhHQAAAACALEFIBwAAAAAgSxDSAQAAAADIEoR0AAAAAACyBCEdAAAAAIAsQUgHAAAAACBLENIBAAAAAMgShHQAAAAAALKEs6sH0NkMw5Ak1dbWdvFIAAAAAAC9QTh/hvNoMr0upNfV1UmShg0b1sUjAQAAAAD0JnV1dSouLk56js1IJ8r3IKFQSHv37lVhYaFsNltXDyep2tpaDRs2TLt371ZRUVFXDweIw2cU2Y7PKLIdn1F0B3xOke26w2fUMAzV1dVp8ODBstuTrzrvdZV0u92uoUOHdvUwMlJUVJS1HzZA4jOK7MdnFNmOzyi6Az6nyHbZ/hlNVUEPo3EcAAAAAABZgpAOAAAAAECWIKRnsZycHN16663Kycnp6qEAlviMItvxGUW24zOK7oDPKbJdT/uM9rrGcQAAAAAAZCsq6QAAAAAAZAlCOgAAAAAAWYKQDgAAAABAliCkAwAAAACQJQjpWer+++/XyJEj5fF4NGvWLL333ntdPST0EosWLdLxxx+vwsJCDRgwQJ/97Ge1cePGqHOam5t13XXXqW/fviooKNCFF16oioqKqHN27dql8847T3l5eRowYIC+853vKBAIdOZbQS9x5513ymaz6cYbb4wc4zOKrrZnzx595StfUd++fZWbm6tjjz1WH3zwQeR5wzB0yy23aNCgQcrNzdXcuXO1efPmqHscPHhQl156qYqKilRSUqIrr7xS9fX1nf1W0AMFg0HdfPPNGjVqlHJzczVmzBj95Cc/kbmfNJ9RdLY33nhD559/vgYPHiybzaannnoq6vn2+kx+/PHHOumkk+TxeDRs2DD94he/6Oi3ljFCehZ6/PHHtWDBAt16661auXKlpkyZonnz5mn//v1dPTT0Aq+//rquu+46vfPOO1qyZIn8fr/OOussNTQ0RM656aab9Mwzz+hf//qXXn/9de3du1ef//znI88Hg0Gdd9558vl8evvtt/Xoo49q8eLFuuWWW7riLaEHe//99/XQQw9p8uTJUcf5jKIrHTp0SHPmzJHL5dL//vc/rVu3TnfddZf69OkTOecXv/iF7r33Xj344IN69913lZ+fr3nz5qm5uTlyzqWXXqq1a9dqyZIlevbZZ/XGG2/o6quv7oq3hB7m5z//uR544AHdd999Wr9+vX7+85/rF7/4hX77299GzuEzis7W0NCgKVOm6P7777d8vj0+k7W1tTrrrLM0YsQIrVixQr/85S/14x//WL///e87/P1lxEDWmTlzpnHddddFfg8Gg8bgwYONRYsWdeGo0Fvt37/fkGS8/vrrhmEYRnV1teFyuYx//etfkXPWr19vSDKWL19uGIZhPP/884bdbjfKy8sj5zzwwANGUVGR4fV6O/cNoMeqq6szxo0bZyxZssQ45ZRTjBtuuMEwDD6j6Hrf+973jBNPPDHh86FQyBg4cKDxy1/+MnKsurrayMnJMf7xj38YhmEY69atMyQZ77//fuSc//3vf4bNZjP27NnTcYNHr3DeeecZX/va16KOff7znzcuvfRSwzD4jKLrSTKefPLJyO/t9Zn83e9+Z/Tp0yfqv/Xf+973jPHjx3fwO8oMlfQs4/P5tGLFCs2dOzdyzG63a+7cuVq+fHkXjgy9VU1NjSSptLRUkrRixQr5/f6oz+jRRx+t4cOHRz6jy5cv17HHHquysrLIOfPmzVNtba3Wrl3biaNHT3bdddfpvPPOi/osSnxG0fX++9//asaMGfriF7+oAQMGaNq0aXr44Ycjz2/fvl3l5eVRn9Hi4mLNmjUr6jNaUlKiGTNmRM6ZO3eu7Ha73n333c57M+iRTjjhBC1dulSbNm2SJH300UdatmyZzjnnHEl8RpF92uszuXz5cp188slyu92Rc+bNm6eNGzfq0KFDnfRuUnN29QAQraqqSsFgMOp/HCWprKxMGzZs6KJRobcKhUK68cYbNWfOHB1zzDGSpPLycrndbpWUlESdW1ZWpvLy8sg5Vp/h8HPAkXrssce0cuVKvf/++3HP8RlFV9u2bZseeOABLViwQD/4wQ/0/vvv6/rrr5fb7dbll18e+YxZfQbNn9EBAwZEPe90OlVaWspnFEfs+9//vmpra3X00UfL4XAoGAzqZz/7mS699FJJ4jOKrNNen8ny8nKNGjUq7h7h58zLkroSIR1AQtddd53WrFmjZcuWdfVQgIjdu3frhhtu0JIlS+TxeLp6OECcUCikGTNm6I477pAkTZs2TWvWrNGDDz6oyy+/vItHB0j//Oc/9be//U1///vfNWnSJK1atUo33nijBg8ezGcUyAJMd88y/fr1k8PhiOtCXFFRoYEDB3bRqNAbzZ8/X88++6xeffVVDR06NHJ84MCB8vl8qq6ujjrf/BkdOHCg5Wc4/BxwJFasWKH9+/fruOOOk9PplNPp1Ouvv657771XTqdTZWVlfEbRpQYNGqSJEydGHZswYYJ27dolqfUzluy/9QMHDoxrGBsIBHTw4EE+ozhi3/nOd/T9739fF198sY499lh99atf1U033aRFixZJ4jOK7NNen8nu8t9/QnqWcbvdmj59upYuXRo5FgqFtHTpUs2ePbsLR4bewjAMzZ8/X08++aReeeWVuClB06dPl8vlivqMbty4Ubt27Yp8RmfPnq3Vq1dH/UG5ZMkSFRUVxf2PK5CpM844Q6tXr9aqVasiPzNmzNCll14aecxnFF1pzpw5cVtXbtq0SSNGjJAkjRo1SgMHDoz6jNbW1urdd9+N+oxWV1drxYoVkXNeeeUVhUIhzZo1qxPeBXqyxsZG2e3RMcDhcCgUCkniM4rs016fydmzZ+uNN96Q3++PnLNkyRKNHz8+a6a6S6K7ezZ67LHHjJycHGPx4sXGunXrjKuvvtooKSmJ6kIMdJRrr73WKC4uNl577TVj3759kZ/GxsbIOddcc40xfPhw45VXXjE++OADY/bs2cbs2bMjzwcCAeOYY44xzjrrLGPVqlXGCy+8YPTv399YuHBhV7wl9ALm7u6GwWcUXeu9994znE6n8bOf/czYvHmz8be//c3Iy8sz/vrXv0bOufPOO42SkhLj6aefNj7++GPjggsuMEaNGmU0NTVFzjn77LONadOmGe+++66xbNkyY9y4ccYll1zSFW8JPczll19uDBkyxHj22WeN7du3G0888YTRr18/47vf/W7kHD6j6Gx1dXXGhx9+aHz44YeGJOPuu+82PvzwQ2Pnzp2GYbTPZ7K6utooKyszvvrVrxpr1qwxHnvsMSMvL8946KGHOv39JkNIz1K//e1vjeHDhxtut9uYOXOm8c4773T1kNBLSLL8+dOf/hQ5p6mpyfjmN79p9OnTx8jLyzM+97nPGfv27Yu6z44dO4xzzjnHyM3NNfr162d8+9vfNvx+fye/G/QWsSGdzyi62jPPPGMcc8wxRk5OjnH00Ucbv//976OeD4VCxs0332yUlZUZOTk5xhlnnGFs3Lgx6pwDBw4Yl1xyiVFQUGAUFRUZV1xxhVFXV9eZbwM9VG1trXHDDTcYw4cPNzwejzF69Gjjhz/8YdS2VHxG0dleffVVy/8Hvfzyyw3DaL/P5EcffWSceOKJRk5OjjFkyBDjzjvv7Ky3mDabYRhG19TwAQAAAACAGWvSAQAAAADIEoR0AAAAAACyBCEdAAAAAIAsQUgHAAAAACBLENIBAAAAAMgShHQAAAAAALIEIR0AAAAAgCxBSAcAoBs79dRTdeONN3b1MKLYbDY99dRTXT0MAAC6JZthGEZXDwIAALTNwYMH5XK5VFhYqJEjR+rGG2/stND+4x//WE899ZRWrVoVdby8vFx9+vRRTk5Op4wDAICexNnVAwAAAG1XWlra7vf0+Xxyu91tvn7gwIHtOBoAAHoXprsDANCNhae7n3rqqdq5c6duuukm2Ww22Wy2yDnLli3TSSedpNzcXA0bNkzXX3+9GhoaIs+PHDlSP/nJT3TZZZepqKhIV199tSTpe9/7no466ijl5eVp9OjRuvnmm+X3+yVJixcv1m233aaPPvoo8nqLFy+WFD/dffXq1Tr99NOVm5urvn376uqrr1Z9fX3k+f/7v//TZz/7Wf3qV7/SoEGD1LdvX1133XWR1wIAoDchpAMA0AM88cQTGjp0qG6//Xbt27dP+/btkyRt3bpVZ599ti688EJ9/PHHevzxx7Vs2TLNnz8/6vpf/epXmjJlij788EPdfPPNkqTCwkItXrxY69at029+8xs9/PDD+vWvfy1Juuiii/Ttb39bkyZNirzeRRddFDeuhoYGzZs3T3369NH777+vf/3rX3r55ZfjXv/VV1/V1q1b9eqrr+rRRx/V4sWLI6EfAIDehOnuAAD0AKWlpXI4HCosLIyabr5o0SJdeumlkXXq48aN07333qtTTjlFDzzwgDwejyTp9NNP17e//e2oe/7oRz+KPB45cqT+3//7f3rsscf03e9+V7m5uSooKJDT6Uw6vf3vf/+7mpub9ec//1n5+fmSpPvuu0/nn3++fv7zn6usrEyS1KdPH913331yOBw6+uijdd5552np0qW66qqr2uXvDwAA3QUhHQCAHuyjjz7Sxx9/rL/97W+RY4ZhKBQKafv27ZowYYIkacaMGXHXPv7447r33nu1detW1dfXKxAIqKioKKPXX79+vaZMmRIJ6JI0Z84chUIhbdy4MRLSJ02aJIfDETln0KBBWr16dUavBQBAT0BIBwCgB6uvr9c3vvENXX/99XHPDR8+PPLYHKIlafny5br00kt12223ad68eSouLtZjjz2mu+66q0PG6XK5on632WwKhUId8loAAGQzQjoAAD2E2+1WMBiMOnbcccdp3bp1Gjt2bEb3evvttzVixAj98Ic/jBzbuXNnyteLNWHCBC1evFgNDQ2RLwLeeust2e12jR8/PqMxAQDQG9A4DgCAHmLkyJF64403tGfPHlVVVUlq6dD+9ttva/78+Vq1apU2b96sp59+Oq5xW6xx48Zp165deuyxx7R161bde++9evLJJ+Neb/v27Vq1apWqqqrk9Xrj7nPppZfK4/Ho8ssv15o1a/Tqq6/qW9/6lr761a9GproDAIBWhHQAAHqI22+/XTt27NCYMWPUv39/SdLkyZP1+uuva9OmTTrppJM0bdo03XLLLRo8eHDSe33mM5/RTTfdpPnz52vq1Kl6++23I13fwy688EKdffbZOu2009S/f3/94x//iLtPXl6eXnzxRR08eFDHH3+8vvCFL+iMM87Qfffd135vHACAHsRmGIbR1YMAAAAAAABU0gEAAAAAyBqEdAAAAAAAsgQhHQAAAACALEFIBwDg/7dfxwIAAAAAg/ytp7GjLAIAmJB0AAAAmJB0AAAAmJB0AAAAmJB0AAAAmJB0AAAAmJB0AAAAmJB0AAAAmJB0AAAAmAg5ACAyA2r5XQAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "plt.figure(figsize=(12, 6))\n", + "plt.plot(objective_func_vals)\n", + "plt.xlabel(\"iteration\")\n", + "plt.ylabel(\"loss\")\n", + "plt.show()\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "quantum1", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +}