slight update

README.md

@@ -29,7 +29,7 @@ Paddle Quantum（量桨）是基于百度飞桨开发的量子机器学习工具
 - 易用性
    - 高效搭建量子神经网络
    - 多种量子神经网络模板
-   - 丰富量子算法教程（10+用例）
+   - 丰富的量子算法教程（10+用例）
 - 可拓展性
    - 支持通用量子电路模型
    - 高性能模拟器支持20多个量子比特的模拟运算

docs/source/introduction.rst

@@ -21,7 +21,7 @@ Paddle Quantum （量桨）
 
   - 高效搭建量子神经网络
   - 多种量子神经网络模板
-  - 丰富量子算法教程（10+用例）
+  - 丰富的量子算法教程（10+用例）
 
 - 可拓展性
 

introduction/PaddleQuantum_Tutorial_CN.ipynb

@@ -137,7 +137,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "metadata": {
     "colab": {},
     "colab_type": "code",
@@ -171,7 +171,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -179,7 +179,7 @@
     "from paddle import fluid\n",
     "from paddle.complex import matmul, transpose, trace\n",
     "from paddle_quantum.circuit import UAnsatz\n",
-    "from paddle_quantum.utils import hermitian, random_pauli_str_generator, pauli_str_to_matrix\n",
+    "from paddle_quantum.utils import dagger, random_pauli_str_generator, pauli_str_to_matrix\n",
     "from paddle_quantum.state import vec, vec_random, density_op, density_op_random"
    ]
   },
@@ -210,7 +210,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "量子计算 (Quantum Computing QC )是利用量子物理中特有的现象 (量子叠加态，量子纠缠)来设计相应的量子算法以解决 (物理、化学、计算机等领域)特定的任务。现有的量子计算有好几种模型，比如有基于绝热定理的绝热计算模型以及基于测量的MBQC模型等等。在本手册中，我们主要讨论目前影响力最大、使用最广泛的量子电路（Quantum circuit）模型。在量子电路的框架下，运算最基本的组成单元是量子比特 (qubit)。这与经典计算机中比特 (bit) 的概念很相似。经典比特只能处于0和1两种状态中的某一种 (物理图景上可以对应高低电位)。与之不同的是，量子比特不仅可以处于两个状态$\\lvert {0}\\rangle$ 还有 $\\lvert {1}\\rangle$还可以处于两者的叠加态 (稍后我们来具体讲解下这一概念)。而所谓的量子计算，就是利用量子逻辑门操控这些量子比特。逻辑门运算的基本理论是线性代数，在此我们假定读者已经具备一定的线性代数基础。"
+    "量子计算 (Quantum Computing QC )是利用量子物理中特有的现象 (量子叠加态，量子纠缠)来设计相应的量子算法以解决 (物理、化学、计算机等领域)特定的任务。现有的量子计算有好几种模型，比如有基于绝热定理的绝热计算模型以及基于测量的MBQC模型等等。在本手册中，我们主要讨论目前影响力最大、使用最广泛的量子电路（Quantum circuit）模型。在量子电路的框架下，运算最基本的组成单元是量子比特 (qubit)。这与经典计算机中比特 (bit) 的概念很相似。经典比特只能处于0和1两种状态中的某一种 (物理图景上可以对应高低电位)。与之不同的是，量子比特不仅可以处于两个状态$\\lvert {0}\\rangle$ 还有 $\\lvert {1}\\rangle$还可以处于两者的叠加态 (稍后我们来具体讲解下这一概念)。而常见的量子计算模型，就是利用量子逻辑门操控这些量子比特。逻辑门运算的基本理论是线性代数，在此我们假定读者已经具备一定的线性代数基础。"
    ]
   },
   {
@@ -325,7 +325,7 @@
    "source": [
     "### <a name=\"gate\">什么是量子逻辑门?</a>\n",
     "\n",
-    "在经典计算机中，我们可以在经典比特上施加基本的逻辑运算( 非门 NOT, 与非门 NAND, 异或门 XOR, 与门 AND, 或门 OR)并组合成更复杂的运算。而量子计算则有完全不同的一套逻辑运算，它们被称为量子门 (quantum gate)。我们并不能在一个量子计算机上编译现有的C++程序。因为**经典计算机和量子计算机有不同的逻辑门构造，所谓的量子算法是需要利用这些量子门的特殊性来构造的**。\n",
+    "在经典计算机中，我们可以在经典比特上施加基本的逻辑运算( 非门 NOT, 与非门 NAND, 异或门 XOR, 与门 AND, 或门 OR)并组合成更复杂的运算。而量子计算则有完全不同的一套逻辑运算，它们被称为量子门 (quantum gate)。我们并不能在一个量子计算机上编译现有的C++程序。因为**经典计算机和量子计算机有不同的逻辑门构造，所以量子算法是需要利用这些量子门的特殊性来构造的**。\n",
     "\n",
     "量子门在数学上可以被表示成酉矩阵 (unitary matrix)。酉矩阵操作可以保证向量的长度不变，这是个很好的性质。不然我们对一个纯态量子比特进行操作，会让它劣化成混合态导致其无法接着使用。那么什么是酉矩阵呢？\n",
     "\n",
@@ -535,7 +535,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -599,7 +599,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -654,7 +654,7 @@
    "source": [
     "### <a name=\"QNN\">示例: 如何创建量子神经网络 QNN?</a>\n",
     "\n",
-    "所谓的QNN就是通常可以表示为一些单比特量子旋转门和双比特门的组合。其中一个可以高效利用硬件的架构是只包含 $\\{R_x, R_y, R_z, \\text{CNOT} \\}$这四种量子门的模板。它们很容易在NISQ设备（通常是超导量子比特）上实现，因为CNOT只需要实施在相邻量子比特上。一个例子可见下图：\n"
+    "QNN通常可以表示为一些单比特量子旋转门和双比特门的组合。其中一个可以高效利用硬件的架构是只包含 $\\{R_x, R_y, R_z, \\text{CNOT} \\}$这四种量子门的模板。它们很容易在NISQ设备（通常是超导量子比特）上实现，因为CNOT只需要实施在相邻量子比特上。一个例子可见下图：\n"
    ]
   },
   {
@@ -673,7 +673,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -742,7 +742,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -797,7 +797,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -835,7 +835,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -914,14 +914,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "metadata": {
     "scrolled": true
    },
    "outputs": [
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -988,15 +988,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[[ 0.00042441+0.j  0.00029816+0.j  0.00019942+0.j ...  0.00039183+0.j\n",
-      "  -0.00052556+0.j  0.00061023+0.j]]\n"
+      "[[ 1.36742103e-03+0.j -1.15621578e-03+0.j  1.59433644e-03+0.j ...\n",
+      "   2.36662139e-04+0.j -2.47895067e-05+0.j -9.47071583e-04+0.j]]\n"
      ]
     }
    ],
@@ -1046,17 +1046,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[[ 0.11259972+0.j -0.28258005+0.j  0.03785432+0.j -0.13651589+0.j]\n",
-      " [-0.28258005+0.j  0.70916239+0.j -0.09499913+0.j  0.34260002+0.j]\n",
-      " [ 0.03785432+0.j -0.09499913+0.j  0.01272605+0.j -0.04589457+0.j]\n",
-      " [-0.13651589+0.j  0.34260002+0.j -0.04589457+0.j  0.16551185+0.j]]\n"
+      "[[0.62679516+0.j 0.38101066+0.j 0.13601748+0.j 0.26505304+0.j]\n",
+      " [0.38101066+0.j 0.23160536+0.j 0.08268109+0.j 0.16111808+0.j]\n",
+      " [0.13601748+0.j 0.08268109+0.j 0.02951643+0.j 0.05751775+0.j]\n",
+      " [0.26505304+0.j 0.16111808+0.j 0.05751775+0.j 0.11208305+0.j]]\n"
      ]
     }
    ],
@@ -1102,7 +1102,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1140,12 +1140,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1223,7 +1223,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
@@ -1338,7 +1338,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
@@ -1346,8 +1346,8 @@
      "output_type": "stream",
      "text": [
       "随机生成的矩阵 H 是:\n",
-      "[[ 0.73879472-2.77555756e-17j -0.12692858+1.29870005e-01j]\n",
-      " [-0.12692858-1.29870005e-01j  0.26120528+0.00000000e+00j]] \n",
+      "[[ 0.55386934+2.77555756e-17j -0.282673  -8.48178487e-02j]\n",
+      " [-0.282673  +8.48178487e-02j  0.44613066+0.00000000e+00j]] \n",
       "\n",
       "不出所料，H 的特征值是:\n",
       "[0.2 0.8]\n"
@@ -1377,7 +1377,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1404,7 +1404,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1425,7 +1425,7 @@
     "        U = U_theta(self.theta)\n",
     "        \n",
     "        # 埃尔米特转置运算\n",
-    "        U_dagger = hermitian(U)\n",
+    "        U_dagger = dagger(U)\n",
     "        \n",
     "        # 计算损失函数函数\n",
     "        loss = matmul(U_dagger, matmul(self.H, U)).real[0][0]\n",
@@ -1435,24 +1435,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "iter: 0   loss: 0.7911\n",
-      "iter: 1   loss: 0.5878\n",
-      "iter: 2   loss: 0.2913\n",
-      "iter: 3   loss: 0.2244\n",
-      "iter: 4   loss: 0.2142\n",
-      "iter: 5   loss: 0.2071\n",
-      "iter: 6   loss: 0.2031\n",
-      "iter: 7   loss: 0.2013\n",
-      "iter: 8   loss: 0.2005\n",
-      "iter: 9   loss: 0.2002\n",
-      "损失函数的最小值是:  0.20019311438922205\n"
+      "iter: 0   loss: 0.5581\n",
+      "iter: 1   loss: 0.3127\n",
+      "iter: 2   loss: 0.2455\n",
+      "iter: 3   loss: 0.2072\n",
+      "iter: 4   loss: 0.2010\n",
+      "iter: 5   loss: 0.2002\n",
+      "iter: 6   loss: 0.2000\n",
+      "iter: 7   loss: 0.2000\n",
+      "iter: 8   loss: 0.2000\n",
+      "iter: 9   loss: 0.2000\n",
+      "损失函数的最小值是:  0.20000008859899268\n"
      ]
     }
    ],
@@ -1521,7 +1521,7 @@
    "source": [
     "### <a name=\"VQE\"> 变分量子特征求解器 (VQE) -- 无监督学习</a>\n",
     "\n",
-    "正如大名鼎鼎的John Preskill 所言，目前阶段理想的可容错的量子计算机还是遥遥无期。我们目前只能造出有噪音的，中等规模量子计算系统（NISQ）。现在一个利用 NISQ 的量子设备很有希望的算法种类就是量子-经典混合算法。人们期望这套方法也许可以在某些应用中超越经典计算机的表现。变分量子特征求解器（VQE）就是里面的一个杰出代表。它利用参数化的电路搜寻广阔的希尔伯特空间，并利用经典机器学习中的梯度下降来找到最优参数，并接近一个哈密顿量的基态（也就是找到一个埃尔米特矩阵的最小特征值）。这个电路其实你之前见过。为了确保你能理解, 我们来一起过一遍以下两量子比特 (2-qubit)的例子。\n",
+    "目前阶段，大规模的可容错的量子计算机还未实现。我们目前只能造出有噪音的，中等规模量子计算系统（NISQ）。现在一个利用 NISQ 的量子设备很有前景的算法种类就是量子-经典混合算法。人们期望这套方法也许可以在某些应用中超越经典计算机的表现。变分量子特征求解器（VQE）就是里面的一个重要应用。它利用参数化的电路搜寻广阔的希尔伯特空间，并利用经典机器学习中的梯度下降来找到最优参数，并接近一个哈密顿量的基态（也就是找到一个埃尔米特矩阵的最小特征值）。为了确保你能理解, 我们来一起过一遍以下两量子比特 (2-qubit)的例子。\n",
     "\n",
     "假设我们想找到如下哈密顿量的基态：\n",
     "\n",
@@ -1541,12 +1541,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "我们已经学会如何建造这个电路了。如果你忘了, 请转到 <a href=\"#QNN\">这里</a>。 有关VQE的更多信息, 请参见 [[博客]](https://www.mustythoughts.com/post/variational-quantum-eigensolver-explained)"
+    "我们已经学会如何建造这个电路了。如果你忘了, 请转到 <a href=\"#QNN\">这里</a>。"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1570,7 +1570,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1609,7 +1609,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 21,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -1728,7 +1728,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.9"
+   "version": "3.6.10"
   }
  },
  "nbformat": 4,

paddle_quantum/SSVQE/Paddle_SSVQE.py

@@ -22,7 +22,7 @@ import numpy
 from paddle.complex import matmul
 from paddle import fluid
 from paddle_quantum.circuit import UAnsatz
-from paddle_quantum.utils import hermitian
+from paddle_quantum.utils import dagger
 from paddle_quantum.SSVQE.HGenerator import H_generator
 
 SEED = 14  # 固定随机种子
@@ -67,7 +67,7 @@ class Net(fluid.dygraph.Layer):
         U = U_theta(self.theta, N)
 
         # 计算损失函数
-        loss_struct = matmul(matmul(hermitian(U), H), U).real
+        loss_struct = matmul(matmul(dagger(U), H), U).real
 
         # 输入计算基去计算每个子期望值，相当于取 U^dagger*H*U 的对角元
         loss_components = [

paddle_quantum/VQSD/Paddle_VQSD.py

@@ -20,7 +20,7 @@ you could check the corresponding Jupyter notebook under the Tutorial folder.
 import numpy
 from paddle import fluid
 from paddle_quantum.circuit import UAnsatz
-from paddle_quantum.utils import hermitian
+from paddle_quantum.utils import dagger
 from paddle.complex import matmul, trace
 from paddle_quantum.VQSD.HGenerator import generate_rho_sigma
 
@@ -70,7 +70,7 @@ class Net(fluid.dygraph.Layer):
         U = U_theta(self.theta, N)
 
         # rho_tilde 是将 U 作用在 rho 后得到的量子态 U*rho*U^dagger
-        rho_tilde = matmul(matmul(U, self.rho), hermitian(U))
+        rho_tilde = matmul(matmul(U, self.rho), dagger(U))
 
         # 计算损失函数
         loss = trace(matmul(self.sigma, rho_tilde))

paddle_quantum/circuit.py

@@ -33,7 +33,7 @@ from paddle.fluid import dygraph
 from paddle.fluid.layers import reshape, cast, eye, zeros
 from paddle.fluid.framework import ComplexVariable
 
-from paddle_quantum.utils import hermitian, pauli_str_to_matrix
+from paddle_quantum.utils import dagger, pauli_str_to_matrix
 from paddle_quantum.intrinsic import *
 from paddle_quantum.state import density_op
 
@@ -166,7 +166,7 @@ class UAnsatz:
         state = dygraph.to_variable(density_op(self.n)) if input_state is None else input_state
 
         assert state.real.shape == [2 ** self.n, 2 ** self.n], "The dimension is not right"
-        state = matmul(self.U, matmul(state, hermitian(self.U)))
+        state = matmul(self.U, matmul(state, dagger(self.U)))
 
         if store_state:
             self.__state = state

paddle_quantum/utils.py

@@ -46,7 +46,7 @@ __all__ = [
     "von_neumann_entropy",
     "relative_entropy",
     "NKron",
-    "hermitian",
+    "dagger",
     "random_pauli_str_generator",
     "pauli_str_to_matrix"
 ]
@@ -220,7 +220,7 @@ def NKron(matrix_A, matrix_B, *args):
     return reduce(lambda result, index: np_kron(result, index), args, np_kron(matrix_A, matrix_B), )
 
 
-def hermitian(matrix):
+def dagger(matrix):
     r"""计算矩阵的埃尔米特转置，即Hermitian transpose。
 
     Args:
@@ -233,12 +233,12 @@ def hermitian(matrix):
 
     .. code-block:: python
     
-        from paddle_quantum.utils import hermitian
+        from paddle_quantum.utils import dagger
         from paddle import fluid
         import numpy as np
         with fluid.dygraph.guard():
             rho = fluid.dygraph.to_variable(np.array([[1+1j, 2+2j], [3+3j, 4+4j]]))
-            print(hermitian(rho).numpy())
+            print(dagger(rho).numpy())
 
     ::
 

tutorial/GPU/GPU_Tutorial_CN.ipynb

@@ -17,11 +17,11 @@
     "\n",
     "> 注意，本篇教程具有时效性。同时不同电脑也会有个体差异性，本篇教程不保证所有电脑可以安装成功。\n",
     "\n",
-    "众所周知，在深度学习中，大家都会选择使用 GPU 来进行神经网络模型的训练，因为与 CPU 相比，GPU在浮点数运算方面有着显著的优势。因此，使用 GPU 来训练神经网络模型逐渐成为共同的选择。在 Paddle Quantum 中，我们的量子态和量子门也采用基于浮点数的复数表示，因此我们的模型如果能部署到 GPU 上进行训练，也会显著提升训练速度。\n",
+    "在深度学习中，大家通常会使用 GPU 来进行神经网络模型的训练，因为与 CPU 相比，GPU在浮点数运算方面有着显著的优势。因此，使用 GPU 来训练神经网络模型逐渐成为共同的选择。在 Paddle Quantum 中，我们的量子态和量子门也采用基于浮点数的复数表示，因此我们的模型如果能部署到 GPU 上进行训练，也会显著提升训练速度。\n",
     "\n",
     "## 2. GPU 选择\n",
     "\n",
-    "在这里，我们推荐选择 Nvidia 的硬件设备，其 CUDA(Compute Unified Device Architecture) 对深度学习的框架支持更好。我们的 PaddlePaddle 也可以比较方便地安装在 CUDA 上。\n",
+    "在这里，我们使用 Nvidia 的硬件设备，其 CUDA(Compute Unified Device Architecture) 对深度学习的框架支持比较好。我们的 PaddlePaddle 也可以比较方便地安装在 CUDA 上。\n",
     "\n",
     "## 3. 配置 CUDA 环境\n",
     "\n",
@@ -128,7 +128,6 @@
     "from paddle import fluid\n",
     "from paddle.complex import matmul, transpose\n",
     "from paddle_quantum.circuit import UAnsatz\n",
-    "from paddle_quantum.utils import hermitian\n",
     "import matplotlib.pyplot as plt\n",
     "import numpy\n",
     "from paddle_quantum.VQE.chemistrysub import H2_generator\n",
@@ -338,7 +337,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.3"
+   "version": "3.7.7"
   }
  },
  "nbformat": 4,

tutorial/Gibbs/GIBBS_Tutorial_CN.ipynb

@@ -40,7 +40,7 @@
     "from paddle.complex import matmul, trace\n",
     "from paddle_quantum.circuit import UAnsatz\n",
     "from paddle_quantum.state import density_op\n",
-    "from paddle_quantum.utils import state_fidelity, partial_trace, hermitian, pauli_str_to_matrix"
+    "from paddle_quantum.utils import state_fidelity, partial_trace, pauli_str_to_matrix"
    ]
   },
   {
@@ -385,7 +385,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.9"
+   "version": "3.7.7"
   }
  },
  "nbformat": 4,

tutorial/Q-Autoencoder/Quantum_Autoencoder_Tutorial_CN.ipynb

@@ -38,7 +38,7 @@
     "from paddle import fluid\n",
     "from paddle_quantum.circuit import UAnsatz\n",
     "from paddle.complex import matmul, trace, kron\n",
-    "from paddle_quantum.utils import hermitian, state_fidelity, partial_trace"
+    "from paddle_quantum.utils import dagger, state_fidelity, partial_trace"
    ]
   },
   {
@@ -182,7 +182,7 @@
     "    def forward(self):\n",
     "        # 生成初始的编码器 E 和解码器 D\\n\",\n",
     "        E = Encoder(self.theta)\n",
-    "        E_dagger = hermitian(E)\n",
+    "        E_dagger = dagger(E)\n",
     "        D = E_dagger\n",
     "        D_dagger = E\n",
     "\n",
@@ -268,7 +268,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.9"
+   "version": "3.7.7"
   }
  },
  "nbformat": 4,

tutorial/Q-Classifier/Quantum_Classifier_Tutorial_CN.ipynb

@@ -922,7 +922,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.10"
+   "version": "3.7.7"
   }
  },
  "nbformat": 4,

tutorial/Q-GAN/QGAN_Tutorial_CN.ipynb

@@ -126,7 +126,7 @@
     "import paddle\n",
     "from paddle import fluid\n",
     "from paddle_quantum.circuit import UAnsatz\n",
-    "from paddle_quantum.utils import partial_trace, hermitian, state_fidelity\n",
+    "from paddle_quantum.utils import partial_trace, dagger, state_fidelity\n",
     "from paddle import complex\n",
     "from progressbar import *"
    ]
@@ -228,7 +228,7 @@
     "        \"\"\"\n",
     "        state = self.target_state\n",
     "        state = complex.reshape(state, [1] + state.shape)\n",
-    "        density_matrix = complex.matmul(hermitian(state), state)\n",
+    "        density_matrix = complex.matmul(dagger(state), state)\n",
     "        state = partial_trace(density_matrix, 2, 4, 2)\n",
     "\n",
     "        return state.numpy()\n",
@@ -239,7 +239,7 @@
     "        \"\"\"\n",
     "        state = self.generator(self.gen_theta).run_state_vector()\n",
     "        state = complex.reshape(state, [1] + state.shape)\n",
-    "        density_matrix = complex.matmul(hermitian(state), state)\n",
+    "        density_matrix = complex.matmul(dagger(state), state)\n",
     "        state = partial_trace(density_matrix, 2, 4, 2)\n",
     "\n",
     "        return state.numpy()"
@@ -482,7 +482,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.9"
+   "version": "3.7.7"
   }
  },
  "nbformat": 4,

tutorial/QAOA/QAOA.ipynb

@@ -839,7 +839,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.10"
+   "version": "3.7.7"
   },
   "pycharm": {
    "stem_cell": {

tutorial/QAOA/QAOA_En.ipynb

@@ -820,7 +820,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.10"
+   "version": "3.7.7"
   },
   "pycharm": {
    "stem_cell": {

tutorial/SSVQE/SSVQE_Tutorial_CN.ipynb

@@ -36,7 +36,7 @@
     "from paddle.complex import matmul, transpose\n",
     "from paddle import fluid\n",
     "from paddle_quantum.circuit import UAnsatz\n",
-    "from paddle_quantum.utils import random_pauli_str_generator, pauli_str_to_matrix, hermitian"
+    "from paddle_quantum.utils import random_pauli_str_generator, pauli_str_to_matrix, dagger"
    ]
   },
   {
@@ -75,7 +75,7 @@
      "output_type": "stream",
      "text": [
       "Random Hamiltonian in Pauli string format = \n",
-      " [[-0.370073566586669, 'x0'], [0.5866720906246325, 'x0'], [-0.9723198195208609, 'x0,y1'], [0.7007292863508459, 'y0,y1'], [0.80763905789957, 'z1'], [-0.7395686405536626, 'z0'], [0.8988849291817222, 'y0'], [-0.617070687255681, 'z0,z1'], [0.8230276264234271, 'y1,z0'], [0.11655495624091028, 'y1']]\n"
+      " [[-0.9208973013017021, 'y0,y1'], [0.198490728051139, 'y1'], [0.9587239095910918, 'x1'], [0.07176335076663909, 'x1'], [-0.7920788641602743, 'x1'], [-0.5028229022143014, 'x0'], [-0.14565978959930526, 'z1,y0'], [0.5965836192828249, 'z1,z0'], [0.6251774164147041, 'y1,y0'], [-0.1580838163596252, 'z0,x1']]\n"
      ]
     }
    ],
@@ -170,7 +170,7 @@
     "        U = U_theta(self.theta, N)\n",
     "        \n",
     "        # 计算损失函数\n",
-    "        loss_struct = matmul(matmul(hermitian(U), H), U).real\n",
+    "        loss_struct = matmul(matmul(dagger(U), H), U).real\n",
     "\n",
     "        # 输入计算基去计算每个子期望值，相当于取 U^dagger*H*U 的对角元 \n",
     "        loss_components = [\n",
@@ -235,11 +235,11 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "iter: 10 loss: -8.5409\n",
-      "iter: 20 loss: -8.8731\n",
-      "iter: 30 loss: -9.1038\n",
-      "iter: 40 loss: -9.2157\n",
-      "iter: 50 loss: -9.2681\n"
+      "iter: 10 loss: -3.8289\n",
+      "iter: 20 loss: -3.9408\n",
+      "iter: 30 loss: -3.9473\n",
+      "iter: 40 loss: -3.9480\n",
+      "iter: 50 loss: -3.9481\n"
      ]
     }
    ],
@@ -297,14 +297,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "The estimated ground state energy is:  [-2.33059408]\n",
-      "The theoretical ground state energy:  -2.3654429645786506\n",
-      "The estimated 1st excited state energy is:  [-1.69552621]\n",
-      "The theoretical 1st excited state energy:  -1.6867829339244156\n",
-      "The estimated 2nd excited state energy is:  [1.11478154]\n",
-      "The theoretical 2nd excited state energy:  1.1321233803877833\n",
-      "The estimated 3rd excited state energy is:  [2.91133876]\n",
-      "The theoretical 3rd excited state energy:  2.920102518115284\n"
+      "The estimated ground state energy is:  [-1.07559121]\n",
+      "The theoretical ground state energy:  -1.0756323552750124\n",
+      "The estimated 1st excited state energy is:  [-0.72131763]\n",
+      "The theoretical 1st excited state energy:  -0.7213113808180259\n",
+      "The estimated 2nd excited state energy is:  [0.72132372]\n",
+      "The theoretical 2nd excited state energy:  0.7213113808180256\n",
+      "The estimated 3rd excited state energy is:  [1.07558513]\n",
+      "The theoretical 3rd excited state energy:  1.0756323552750122\n"
      ]
     }
    ],
@@ -357,7 +357,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.10"
+   "version": "3.7.7"
   },
   "pycharm": {
    "stem_cell": {

tutorial/VQE/VQE_Tutorial_CN.ipynb

@@ -43,7 +43,7 @@
     "from paddle import fluid\n",
     "from paddle.complex import matmul, transpose\n",
     "from paddle_quantum.circuit import UAnsatz\n",
-    "from paddle_quantum.utils import hermitian, pauli_str_to_matrix\n",
+    "from paddle_quantum.utils import pauli_str_to_matrix\n",
     "from paddle_quantum.VQE.chemistrysub import H2_generator"
    ]
   },
@@ -105,7 +105,20 @@
    "cell_type": "code",
    "execution_count": 4,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Collecting openfermionpyscf\n",
+      "  Using cached https://pypi.tuna.tsinghua.edu.cn/packages/cb/6e/01bc7d2e478ea75b9b5dd262c876f5e7d32105e713b588af5b0121def125/openfermionpyscf-0.4.tar.gz (13 kB)\n",
+      "Requirement already satisfied: openfermion>=0.5 in c:\\users\\v_liurenyu\\anaconda3\\envs\\test_paddle\\lib\\site-packages (from openfermionpyscf) (0.11.0)\n",
+      "Collecting pyscf\n",
+      "  Using cached https://pypi.tuna.tsinghua.edu.cn/packages/c0/f1/fe52a94e92acf3fa8c5543d86cfed6fea0526011d2ff2dda352f9cf9eaef/pyscf-1.7.4.tar.gz (7.5 MB)\n"
+     ]
+    }
+   ],
    "source": [
     "!pip install openfermionpyscf\n",
     "clear_output()"
@@ -115,30 +128,7 @@
    "cell_type": "code",
    "execution_count": 5,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The generated h2 Hamiltonian is \n",
-      " (-0.04207897647782277+0j) [] +\n",
-      "(-0.04475014401535163+0j) [X0 X1 Y2 Y3] +\n",
-      "(0.04475014401535163+0j) [X0 Y1 Y2 X3] +\n",
-      "(0.04475014401535163+0j) [Y0 X1 X2 Y3] +\n",
-      "(-0.04475014401535163+0j) [Y0 Y1 X2 X3] +\n",
-      "(0.17771287465139946+0j) [Z0] +\n",
-      "(0.17059738328801055+0j) [Z0 Z1] +\n",
-      "(0.12293305056183797+0j) [Z0 Z2] +\n",
-      "(0.1676831945771896+0j) [Z0 Z3] +\n",
-      "(0.1777128746513994+0j) [Z1] +\n",
-      "(0.1676831945771896+0j) [Z1 Z2] +\n",
-      "(0.12293305056183797+0j) [Z1 Z3] +\n",
-      "(-0.2427428051314046+0j) [Z2] +\n",
-      "(0.1762764080431959+0j) [Z2 Z3] +\n",
-      "(-0.24274280513140462+0j) [Z3]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# 操作系统信息\n",
     "sysStr = platform.system()\n",
@@ -356,14 +346,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "iter: 20 loss: -1.0024\n",
-      "iter: 20 Ground state energy: -1.0024 Ha\n",
-      "iter: 40 loss: -1.0935\n",
-      "iter: 40 Ground state energy: -1.0935 Ha\n",
-      "iter: 60 loss: -1.1153\n",
-      "iter: 60 Ground state energy: -1.1153 Ha\n",
-      "iter: 80 loss: -1.1174\n",
-      "iter: 80 Ground state energy: -1.1174 Ha\n"
+      "iter: 20 loss: -1.0843\n",
+      "iter: 20 Ground state energy: -1.0843 Ha\n",
+      "iter: 40 loss: -1.1313\n",
+      "iter: 40 Ground state energy: -1.1313 Ha\n",
+      "iter: 60 loss: -1.1356\n",
+      "iter: 60 Ground state energy: -1.1356 Ha\n",
+      "iter: 80 loss: -1.1361\n",
+      "iter: 80 Ground state energy: -1.1361 Ha\n"
      ]
     }
    ],
@@ -429,7 +419,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -502,7 +492,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.10"
+   "version": "3.7.7"
   }
  },
  "nbformat": 4,

tutorial/VQSD/VQSD_Tutorial_CN.ipynb

@@ -35,7 +35,7 @@
     "import scipy\n",
     "from paddle import fluid\n",
     "from paddle_quantum.circuit import UAnsatz\n",
-    "from paddle_quantum.utils import hermitian\n",
+    "from paddle_quantum.utils import dagger\n",
     "from paddle.complex import matmul, trace, transpose"
    ]
   },
@@ -182,7 +182,7 @@
     "        U = U_theta(self.theta, N)\n",
     "\n",
     "        # rho_tilde 是将 U 作用在 rho 后得到的量子态 U*rho*U^dagger \n",
-    "        rho_tilde = matmul(matmul(U, self.rho), hermitian(U))\n",
+    "        rho_tilde = matmul(matmul(U, self.rho), dagger(U))\n",
     "\n",
     "        # 计算损失函数\n",
     "        loss = trace(matmul(self.sigma, rho_tilde))\n",
@@ -345,7 +345,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.10"
+   "version": "3.7.7"
   },
   "pycharm": {
    "stem_cell": {

tutorial/VQSVD/VQSVD_Tutorial_CN.ipynb

@@ -377,7 +377,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "主程序段总共运行了 191.72587513923645 秒\n"
+      "主程序段总共运行了 216.55158972740173 秒\n"
      ]
     },
     {
@@ -412,11 +412,11 @@
     "        \n",
     "        # 获取量子神经网络的酉矩阵表示\n",
     "        U = U_theta(self.theta)\n",
-    "        U_dagger = hermitian(U)\n",
+    "        U_dagger = dagger(U)\n",
     "        \n",
     "        \n",
     "        V = U_theta(self.phi)\n",
-    "        V_dagger = hermitian(V)\n",
+    "        V_dagger = dagger(V)\n",
     "        \n",
     "        # 初始化损失函数和奇异值存储器\n",
     "        loss = 0 \n",
@@ -475,7 +475,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -523,7 +523,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAEKCAYAAAAfGVI8AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/d3fzzAAAACXBIWXMAAAsTAAALEwEAmpwYAABFwElEQVR4nO3dd3iUZdbA4d9JJ6EEQu9FIJRAAlEEUYoKuiqiUnQtoO7ay+qC9Vtl13Xt6Np1VVREsYsFK1VAUCAgXUAC0mtCSULa+f54JoWQMiGTTMq5r2suZt6ZeedkEubM084jqooxxpiaK8DfARhjjPEvSwTGGFPDWSIwxpgazhKBMcbUcJYIjDGmhgvydwAnomHDhtq2bVt/h2GMMVXKkiVL9qpqo4LHq2QiaNu2LYsXL/Z3GMYYU6WIyObCjlvXkDHG1HCWCIwxpoazRGCMMTVclRwjMKYy2L17N+PGjWPt2rVkZ2f7OxxjAAgICCA6Oponn3ySxo0be/UcSwTGnKBx48YxaNAgXn/9dYKDg/0djjEAZGRkMHnyZMaNG8fbb7/t1XNqTNfQZwnbGPbIRyx6oA8XPPIxnyVs83dIpopbu3YtV1xxhSUBU6kEBwdz5ZVXsnbtWq+fUyNaBJ8lbOPeT1Zwn77HyYHrGHnkXe79JAKA4XEt/Bydqaqys7MtCZhKKTg4uFTdlTWiRfDEt+uonbGXkYFzCBBlZOBcamfs44lv1/k7NGOM8bsakQi2J6VyW9CnBJIFQABZ3Br0CduTUv0cmTFlExgYSGxsLN27d+eCCy4gKSnJb7HMnj2bBQsW+Ox8n332GatXr869/cADD/DDDz/47PwFff755zz66KNePTYlJYWoqCgOHjx4zPHhw4fz/vvvAy7+Hj16EB0dTffu3fnoo49yH7dw4UL69OlDbGwsXbp0YcKECSQmJtKyZcvjvsnHxsayaNEiJkyYQIsWLYiNjaVjx45cfPHFx7w/ZVEjEkFMvVRGBs4hWNwbHCpZjAycS/d6aX6OzNQknyVs47RHZ9Lunq847dGZPhmnqlWrFsuWLWPlypU0aNCAF154wQeRnpjiEkFmZmapz1cwEfzrX//irLPOOuH4SjJs2DDuuecerx4bHh7O0KFD+fTTT3OPJScnM2/ePC644AKWL1/OuHHjmDZtGmvXruWLL77g7rvvZsmSJQCMGTOGV199Nfd3N2rUKNq2bUvr1q358ccfc8+5du1aDh06RJ8+fQC44447WLZsGevXr2f06NEMHjyYPXv2lPlnrxGJ4Jlm3yMcuxNbANn8t9l3forI1DQ541TbklJRYFtSKvd+ssKnkxb69u3Ltm3ufBs3buScc86hd+/enH766bkDh7t27eKiiy6iZ8+e9OzZM/eDe+LEiXTv3p3u3bvzzDPPAJCYmEiXLl3461//Srdu3RgyZAipqa4V/eyzz9K1a1d69OjBpZdeSmJiIi+//DJPP/00sbGx/Pjjj4wdO5YbbriBPn36cNdddzFhwgSefPLJ3Hi7d+9OYmIiAG+//TY9evSgZ8+eXHnllSxYsIDPP/+c8ePHExsby8aNGxk7dmzut+oZM2YQFxdHTEwM11xzDUePHgVc+ZkHH3yQXr16ERMTU+iA6amnnsqqVatybw8cOJDFixfz5ptvcssttwDwxRdf0KdPH+Li4jjrrLPYtWvXcee57LLLmDp1au7tTz/9lKFDhxIeHs6TTz7JfffdR7t27QBo164d9913H0899RTgph43a9YMcK26rl27FnrOqVOncumllxb6+x49ejRDhgzh3XffLfT+0qgRg8Xt01aBHPuNJFQy3XFjfGT0Kz8VeV/CliTSs45t8qdmZDHhi1UMj2vB/iPp3PjOkmPuf//6vl6/dlZWFjNmzODaa68F4LrrruPll1+mY8eOLFq0iJtuuomZM2dy2223MWDAAD799FOysrI4fPgwS5YsYdKkSSxatAhVpU+fPgwYMID69euzfv163nvvPf73v/8xatQoPv74Y6644goeffRRNm3aRGhoKElJSURGRnLDDTdQu3Ztxo0bB8Drr7/O1q1bWbBgAYGBgUyYMKHQ2FetWsW///1vFixYQMOGDdm/fz8NGjRg2LBhnH/++YwYMeKYx6elpTF27FhmzJhBp06duOqqq3jppZf429/+BkDDhg1ZunQpL774Ik8++SSvvfbaMc8fPXo0H3zwAf/85z/ZsWMHO3bsID4+npUrV+Y+pn///ixcuBAR4bXXXuPxxx/P/RDPMXToUP7yl7+wb98+oqKimDp1am4iWbVqVe77kCM+Pp7nnnsOcN/sO3fuzMCBAznnnHMYM2YMYWFhjBo1itjYWJ577jmCgoJ4//33+fDDD4v8vffq1atUs4OKUiNaBNwwDyYkw4Rk0u/cyFGC+b72MHfcmApQMAnkSErJKNN5U1NTiY2NpWnTpuzatYuzzz6bw4cPs2DBAkaOHElsbCzXX389O3bsAGDmzJnceOONgPsmWq9ePebNm8dFF11EREQEtWvX5uKLL87tnmjXrh2xsbEA9O7dO/cbfI8ePbj88st55513CAoq+vvkyJEjCQwMLPZnmDlzJiNHjqRhw4YANGjQoNjHr1u3jnbt2tGpUyfAdbPMnTs39/6LL774uHjzGzVqVG7L4oMPPjgu0QBs3bqVoUOHEhMTwxNPPHFMCyJHSEgIw4YN46OPPmLv3r0kJCQwdOjQYmPP8cADD7B48eLcb/TnnHMOAE2aNKF79+7MmDGDZcuWERQURPfu3Ys8j6/2nK8RLYL8Quo2ZHbH8by0MoQ2uw7RqUkdf4dkqonivsGf9uhMthUyOaFFZC0AGkSElKoFkCNnjCAlJYWhQ4fywgsvMHbsWCIjI1m2bFmpz1dQaGho7vXAwMDcrqGvvvqKuXPn8sUXX/Dwww+zYsWKQp8fERGRez0oKOiYgdC0tPIZo8uJOTAwsNCxiRYtWhAVFcWvv/7K+++/z8svv3zcY2699VbuvPNOhg0bxuzZs4tszVx22WU89NBDqCoXXnhh7nTirl27smTJEnr27Jn72CVLlhAfH597u0OHDtx444389a9/pVGjRrkti5zuoSZNmnDZZZcV+7MmJCQcc84TVaEtAhGJFJGPRGStiKwRkb4i0kBEvheR9Z5/65d3HD2G38GqwGgmzU8s75cyBoDxQztTK/jYb8a1ggMZP7SzT84fHh7Os88+y1NPPUV4eDjt2rXL7VJQVZYvXw7AmWeeyUsvvQS47qTk5GROP/10PvvsM1JSUjhy5Aiffvopp59+epGvlZ2dzR9//MGgQYN47LHHSE5O5vDhw9SpU4dDhw4V+by2bduydOlSAJYuXcqmTZsAGDx4MB9++CH79u0DYP/+/QBFnq9z584kJiayYcMGACZPnsyAAQNK9X6NHj2axx9/nOTkZHr06HHc/cnJybRo4dYYvfXWW0WeZ+DAgaxfv54XXnjhmA/tcePG8cgjj+S2SBITE3nmmWcYP3484BJpzrf59evXExgYSGRkJOBaNNOnT+f9998vcnwA4OOPP+a7774rMVl4o6K7hv4LfKOq0UBPYA1wDzBDVTsCMzy3y1WDiBCu65JBw4TnOXAkvbxfzhiGx7XgkYtjaBFZC8G1BB65OManCxrj4uLo0aMH7733HlOmTOH111+nZ8+edOvWjWnTpgHw3//+l1mzZhETE0Pv3r1ZvXo1vXr1YuzYsZxyyin06dOHv/zlL8TFxRX5OllZWVxxxRXExMQQFxfHbbfdRmRkJBdccAGffvpp7mBxQZdccgn79++nW7duPP/887ldO926deP+++9nwIAB9OzZkzvvvBOASy+9lCeeeIK4uDg2btyYe56wsDAmTZrEyJEjiYmJISAggBtuuKFU79WIESOYOnUqo0aNKvT+CRMmMHLkSHr37p3bZVWYgIAARowYwb59+45JRrGxsTz22GNccMEFdOrUiU6dOvHSSy/RubNL/JMnT6Zz587ExsZy5ZVXMmXKlNwutMjISPr27UuTJk1o3779Ma+XMxjfsWNH3nnnHWbOnEmjRsftM1Nq4qs+phJfSKQesAxor/leVETWAQNVdYeINANmq2qxX5Pi4+O1rBvT7PrheZrMu5/vT5vK2WefW6ZzmZopPj7eNkgyXrnnnntYtGgR3377LSEhIRXymoX9fYrIElU9ri+pIlsE7YA9wCQRSRCR10QkAmiiqjs8j9kJNCnsySJynYgsFpHFvpg326T/lWQH1eLslK/KfC5jjCnOo48+yqxZsyosCZRWRSaCIKAX8JKqxgFHKNAN5GkpFNpEUdVXVTVeVeN90RQirB4BPUbCyo/JTjlQ9vMZY0wVVZGJYCuwVVUXeW5/hEsMuzxdQnj+3V1hEcVfAxkpTHr5cZ9NwzLGmKqmwhKBqu4E/hCRnP7/M4HVwOfAGM+xMcC0ioqJ5nHsanAyrcIzi5znbYwx1V1FryO4FZgiIiHA78DVuGT0gYhcC2wGCh/GLydNbv2eISIV+ZLGGFOpVGgiUNVlQGGrH86syDiOIYJmZ7Ny1XIat+lCk7phfgvFGGP8oWaUmCjB4R8eo9NHZzN19jJ/h2JMqVgZat8pTRlqcKWoL7/8cmJiYujevTv9+/fn8OHDDBo0iG+//faYxz7zzDPceOONJCYmUqtWLeLi4ujSpQunnHIKb775po9/ktKzRADU6XkhoZJB5tIppKSXvlyuMV47tBMmnQuHjq9meSKsDLXvlKYMNbjFeU2aNGHFihWsXLkyd+/qghVEwVURzVkB3KFDBxISElizZg1Tp07lmWeeYdKkST79WUrLEgFAk64cahzPRdnf8cmSP/wdjanO5jwOWxbCnMd8fmorQ12xZah37NiRW4YCXOmL0NBQRowYwVdffUV6enru+7h9+/ZCy3a0b9+eiRMn8uyzz5b06y1fqlrlLr1791Zfy142VfXBuvr3R5/RrKxsn5/fVD/H/R2+8afjL4tedfcdPaL66lmqEyJVH6zr/v3fWapL33H3H957/HO9EBERoaqqmZmZOmLECP36669VVXXw4MH622+/qarqwoULddCgQaqqOmrUKH366adzn5OUlKSLFy/W7t276+HDh/XQoUPatWtXXbp0qW7atEkDAwM1ISFBVVVHjhypkydPVlXVZs2aaVpamqqqHjhwQFVVH3zwQX3iiSdyYxszZoyed955mpmZWej93bp1002bNunKlSu1Y8eOumfPHlVV3bdvX+7zP/zww2PO9+GHH2pqaqq2bNlS161bp6qqV155Ze7P1KZNG3322WdVVfWFF17Qa6+99rj3bOLEifrAAw+oqur27du1U6dOqqo6adIkvfnmm1VVdf/+/Zqd7T4H/ve//+mdd9553HkSEhK0UaNGeuqpp+r999+f+36rqp533nn62WefqarqI488on//+99VVXXTpk3arVu3Y85z4MABDQsLO+78ZVXY5ySwWAv5TLUWgYd0vZCjIZH0Pfwdc9eXfeWyMcdJ3gI561VUIWlLmU9pZaj9V4Y6NjaW33//nfHjx7N//35OPvlk1qxZAxy7wUz+bqHCaCVYw1TjylAXKTiMgCs/5Zm3dtBufiIDOzf2d0Smqrm6mHIlRw9CWhJ5C+fV3T7J0+cdEVX884tgZaiLjrkiylDnJM6LL76YgIAApk+fTpcuXbjwwgu54447WLp0KSkpKfTu3bvIeBMSEujSpcuJ/bA+Yi2CfIJb9WJ0v07M/W0P63cVXU7XmFKb8zhogUWLmu2zsQIrQ13xZajnz5/PgQOuPE16ejqrV6+mTZs2gEsQgwYN4pprrim2NZCYmMi4ceO49dZbSxW/r1kiKGBM5HLeC3mYN+dvLPnBxnhr68+QVaDkeVa6O+4jVobae74oQ71x40YGDBiQ+z7Ex8dzySWX5N5/2WWXsXz58uMSwcaNG3Onj44aNYrbbruNq6++ulTx+1qFlaH2JV+UoS7Sqs/gwzF8FfMM513i31+OqdysDLWpzCprGeqqIfo8qN2E845+4+9IjDGmQlgiKCgwGOKuRH/7lrk/LyHDitEZY6o5SwSF6e2KoS77/Fm+X+2bFaCm+gkICCAjI8PfYRhznIyMDAICvP94t0RQmMjWcMZ4Th98PkO6FrphmjFER0czefJkSwamUsnIyGDy5MlER0d7/RwbLDbmBO3evZtx48axdu3aY+bHG+NPAQEBREdH8+STT9K48bHroYoaLLYFZcU5uJ3ZX3/ArLCz+OeF3f0djalkGjduzNtvv+3vMIwpM+saKk7COwxc8yA/LvqZ7Ump/o7GGGPKhSWC4sRdiUogowNm8PZPm/0djTHGlAtLBMWp2wyJ/hOXh8zlo0Ubba8CY0y1ZImgJPHXUjv7IP3S5/PJ0m3+jsYYY3zOEkFJ2g1AG3aiX719TJq/iezsqjfLyhhjimOJoCQBAcgN8wgd8g827jliexUYY6odSwTeCArlvJjmtK+dyRvzE/0djTHG+JQlAi+F/PQM07mZX377g8S9R/wdjjHG+IwlAm+1PpWwzENMP3MPbaLC/R2NMcb4jCUCb7XuC42iabfpfUTE39EYY4zPVGgiEJFEEVkhIstEZLHnWAMR+V5E1nv+rV+RMXlNBOKvge1LefHdD5k0f5O/IzLGGJ/wR4tgkKrG5it8dA8wQ1U7AjM8tyunHqMhqBZdt3/K3sNH/R2NMcb4RGUoOnchMNBz/S1gNnC3v4IpVq1IGP0OA5rHMjCi8H1MjTGmqqnoFoEC34nIEhG5znOsiaru8FzfCRS6AYCIXCcii0Vk8Z49fpzL3/EsxJME1u085L84jDHGRyo6EfRX1V7AucDNInJG/jvVbY5Q6NJdVX1VVeNVNb5Ro0YVEGox1n/PH2+MZegzc1i65YB/YzHGmDKq0ESgqts8/+4GPgVOAXaJSDMAz7+7KzKmE5K8lVZbPqV/2CYm2QIzY0wVV2GJQEQiRKROznVgCLAS+BwY43nYGGBaRcV0wmJGQkgd7opawPQVO9iRbHsVGGOqropsETQB5onIcuBn4CtV/QZ4FDhbRNYDZ3luV26htaHnaGKSZlBXD9leBcaYKq3CEoGq/q6qPT2Xbqr6sOf4PlU9U1U7qupZqrq/omIqk95XI1lHub9FAu8u2kJqepa/IzLGmBNSqkQgIvEiMtrTtZPT3VMZpqBWvKbdIe5K4rp3Izk1g08Stvo7ImOMOSFeJQIRaSIiC3FdOu+SN8VzIvBUOcVW+V34PO0HXEGPlvV4Y57tVWCMqZq8bRE8DewCooCUfMc/xA361lhy9BD3dNzGxj1H+HHDXn+HY4wxpeZtIjgTuF9VC06a3wi09m1IVczcx+m76Cb6NMpk7yErO2GMqXq8TQS1gPRCjjcC0nwXThXUawySncnUkzdwSe+W/o7GGGNKzdtEMBcYm++2ikggribQDF8HVaU07AhtT0eWvkl2VpaVnTDGVDneJoK7gL+KyPdAKG6AeDVwGnBvOcVWdcRfA0lb+OjDt7nwhXkkpRTWeDLGmMrJq6mfqrpaRGKAG4GjQBhuoPiFfAXjaq7o8yGiEWeHrydsxFAiQmvmjFpjTNXk9SeWqu4EHizHWKquoBC4aRH1I6IY5u9YjDGmlLxdR3CLiFxRyPErROQm34dVBUVEAZCWlsYLszYwY80uPwdkjDHe8XaM4G/AH4UcTwTu8FUwVd6C5wh9pQ+f/JLI87M2+DsaY4zxireJoCVQWGW1rZ77DECD9siBRO7vtIWELUm2V4ExpkrwNhHsBGILOd4LsOW0OToOhTrNOSP5C+qEBdleBcaYKsHbRPAu8KyInC0iwZ7LEOAZYEq5RVfVBAZB7zEEbZrJDTEBtleBMaZK8DYRPAjMB77F1RpKAb4GFgD/KJ/QqqheV4EEcmXIbFTV9iowxlR6XiUCVc1Q1cuAzsDlwJ+BaFW9VFUzyjPAKqducxj2HHX7XcPQbk1trwJjTKVXqv0IVHW9qn6gqh+q6vryCqrKi7scojpwTf92tleBMabS83pBmYiMxlUhbUyBBKKqto6qoM0LiN84g5gWg3hj3iYuO7k1AQHi76iMMeY4XiUCEXkCt5ZgFrAdsB1YSvLHIuTHJ3lo2BBS6kUjlgOMMZWUty2Cq4DLVPWj8gymWom9AmY+TOzuz6DXI/6OxhhjiuTtGEEAsKwc46h+ajeCrsNg2RT2Hkjin1+sYsNuK1FtjKl8vE0ErwLH1RoyJeh9NaQlE7ZuGh8u3srSLUn+jsgYY47jbddQJPBnETkb+BU4Zsqoqt7m47iqh7b9of1AaocFs+i+M608tTGmUvL2k6kreV1D0QXus4HjoojAVdMAiPAcSk7JoF54sP9iMsaYArzdmGaQr17Qs8XlYmCbqp4vIu2AqUAUsAS4UlWr1xZfWRmwew2PJATz+fLtzL1rEMGBpVrCYYwx5cYfn0a3A2vy3X4MeFpVTwIOANf6Iaby9e19MOlc+rYMYUdyGt+s3OnviIwxJpfXiUBEBonIqyLyjYjMzH8pxTlaAucBr3luCzAYyJmW+hYw3Ovoq4qYUZB+mDOOzqFtVDhvzN/k74iMMSaXtzuUjcUVmasDDAT2APVxZahXl+L1ngHuArI9t6OAJFXN9NzeCrQoIobrRGSxiCzes2dPKV6yEmgZD01iCFj8Blf3a2t7FRhjKhVvWwTjgFs8hecygHtVNQ54BzjszQlE5Hxgt6ouOZFAVfVVVY1X1fhGjRqdyCn8RwTir4ZdKxjVbJftVWCMqVS8TQTtgR88148CtT3XnwfGenmO04BhIpKIGxweDPwXiBSRnEHrlsA2L89XtfQYBSG1qbXhSy49uZXtVWCMqTS8TQT7cN1C4D6ou3uuRwG1vDmBqt6rqi1VtS1wKTBTVS/H1S8a4XnYGGCalzFVLaF14LrZcNa/uKpvW9urwBhTaXibCH4Ehniuf4DbrWwS8B7wfRljuBu4U0Q24BLL62U8X+XVsCMEBNCqfi3bq8AYU2l4u6DsFiDMc/0RIBPX1fMB8O/SvqiqzgZme67/DpxS2nNUWT//D1Z8xDWD32Xehr2s3nGQ3m3q+zsqY0wN5u2Csv35rmfj5v6bExEcDn8sJJ7VLLrvTMJDrOyEMca/vJ0+miUijQs5HiUi1rdRGt0ugrB6yJI3CQ8JQlVJSqleC6mNMVWLt2MERW2rEgrYp1hphIRDz8tg9TQ4vIcrXl/E7VOX+TsqY0wNVmy/hIjc6bmqwA0ikn/NQCBwOrC2nGKrvnpfDYtehmVTGB57CYG2haUxxo9K6qC+1fOvAH8B8ncDpQOJwA2+D6uaaxwNg/8B7Qcwsnkrf0djjKnhik0EqtoOQERmARerqtVF8JUzxuVePZiWwdSftzAqvhWR4SF+DMoYUxN5NUagqoMKJgEROUlEwop6jvHCzhXwy2tsO5DKf6av5b2f//B3RMaYGsjbWUP/EZExnusiIj8AvwE7RKRPeQZYra34EKbfRZeIw/TrEMXbPyWSkZVd8vOMMcaHvJ01dDmwznP9XKAncCrwNvBoOcRVM/QeC5oFSydzzWnt2JGcxrerbK8CY0zF8jYRNMGViAb4E/CBqv4MPAfElUdgNUKD9tBhMCx9i8GdGtAmKpw35tleBcaYilWaonNtPNeHADM814Moeo2B8Ub8NXBwGwEbvufqfm1ZuiWJBNurwBhTgbxNBB8D74rI90AD4FvP8VhgQznEVXN0OgcadoJDOxgR34o6obZXgTGmYnmbCO4EnsXtRna2qh7xHG8GvFQegdUYgcFw0yI4+VpqhwYx2vYqMMZUMG+nj2aq6lOqeruqJuQ7/rSqvlZ+4dUQAQGgCslbGdOvLdmqTLa9CowxFaTIBWUi0gtYpqrZnutFUtWlPo+spvn6Llj1Ga3uWMXfh3QmrlWkvyMyxtQQxa0sXgw0BXZ7riuFDwwrru6QKYuOQ+DnV2HdV9w86CI+S9jGaY/OZHtSKs0jazF+aGeGx7Xwd5TGmGqouETQDtiT77opTx0GQ73WsPgNPks/hbs//pWjmW5x2bakVO79ZAWAJQNjjM8VmQhUdXNh1005CQiE3mNg5kO8v20mRzMbHHN3akYWj3+z1hKBMcbnvC0x0V5E7hSR50XkORG5Q0SsleBrcVdCQBCnp3xX6N3bk9O4fvJipv68hV0H0yo4OGNMdVXiPoki8nfcPsWBuPECARoBj4nI3ar6dPmGWIPUaQJjvuCDdw9BcuZxd4eHBPLr1mS+XbULgG7N6zI4ujGDohvTs2Wk7WtgjDkhoqpF3ynSH5iDqyf0VM7exSISBYwDxgMDVHV+BcSaKz4+XhcvXlyRL1mhPkvYxr2frCA1I2/7h1rBgTxycQwXxjZn3a5DzFy7m1lrd7Nk8wGyFRpEhHD3OZ0ZfXJrP0ZujKnMRGSJqsYXPF5Si+BG4G1VvT//QVXdB9wrIs2Am4AKTQTV3XBm0bv1dC7d95dCZw1FN61LdNO63DTwJJJTMpizfg+z1+6mSV1XFXzNjoM8MG0lDw3vTnTTuv78UYwxVUBJieBU4Jpi7n8TeN1n0RgnNYlW275i/lXXw5znYcSbrtuoEPXCgxnWsznDejbPPZacmkFqRhYNItwmN9OWbWPh7/sZHN2Y006KIjykxB5BY0wNUlLX0BEgWlUL3TFFRFoB61Q1vJziK1R17xoiZT88Fe2qk+5d5/Y4Pn/iCZ/uhVkbeHHWBo6kZxESFMCp7aMY1LkRg6Mb0yYqwoeBG2Mqs6K6hkpKBNlAU1XdXcT9TYDtqlrigjLPbmZzgVBcS+QjVX3QM/toKhAFLAGuVNX04s5V7RMBwPtXwZpp7npQGNz+a5GtAm+kZ2bzS+L+3LGF3/e6clHtG0UwuHNjzurahFPbR/kicmNMJXWiYwQA54lIchH3RZYihqPAYFU9LCLBwDwR+RpX0O5pVZ0qIi8D12KF7HALtnOuZsOcx8rUKggJCuC0kxpy2kkN+cf5XUnce4RZ63Yzc+1u3v5pMxv2HM5NBN+s3EmvNpE0rmM7kRpTE3iTCEoaAyi6SZH/Qa7pcdhzM9hzUWAw8GfP8beACdT0RHBoJ6zPt5YgKx2WTYG6zaHvLRBc9g/otg0juLphO64+rR1Hjmay/4hrhO09fJQb3lnC38/uxK1ndiQlPZO1Ow/Z9FRjqrFiE4Gqelum2isiEojr/jkJeAHYCCSpas6k+a1AoUtnReQ64DqA1q2r+RTJOY+7VkB+2Rkw8yH45XUYMN4tPgsM9snLRYQGERHq/hSiIkL4+vbTqR/uBpp/XL+X6ycvoUFECAM7NWJgdGMGdGxEvfC81/4sYRtPfLvO6iIZU0UVO0ZQbi8qEgl8CvwDeFNVT/IcbwV8rardi3t+tR8jeLk/7Fxx/PH67SCiEWz9Geq3hQH3QM9LQcrvm3pyagaz17lxhTm/7eFASgaBAULv1vUZFN0YRXluxnpSM/ISV86aB0sGxlQuJzRYXJ5E5AEgFbgbNyCdKSJ9gQmqOrS451b7RFAcVVj/vWsdhNWDsV9W2EtnZSvL/khi1lo3trB6x8EiH9sishbz7xlcYbEZY0pWVCLwaddPCQE08rQEEJFawNnAGmAWMMLzsDHAtIqKqUoSgU5D4Lo5MOptdyx5K7x2Fqz7xiWKchIYIPRuU59xQzsz/fbTWXjvmUU+dnuS22EtLd/qaGNM5VSRK4uaAW95xgkCgA9U9UsRWQ1MFZF/AwnYAjXvBARAuKdC6cEdcGQPvDcaWp4Mg/8P2g8s9xCa1gujRWQttiUdv61m88haZGcr/R+bSZO6YZzaPopT20dxSrsG1Kvlm7ENY4xv+K1rqCxqdNdQUbIy3MyiOY/DwW0uEVz+MQSWb64vri7SOd2b8sqc3/np970s3ZJEemY2Iq5Y3qntoujbIYqT2zWgbpglBmMqQqUbIygLSwTFyEiDJW+6ZDDkIXds/yZoUH5Vw72ZNZSWkcWyP5L4aeM+Fv6+j4QtSaRnZRMg8J+LYrj0lNakZWSRkZVNHUsMxpSLMiUCEamPm98/CGhMgbEFVW3smzC9Y4mgFLYnwKuDoMsFMOh+aBzt74gAlxiWbjnAwt/386eYpkQ3rcv3q3dx/eTFTLu5PzEt67H/SDohQQHUDrXaSMb4QllWFgO8DXTDLfjahZeLyEwl0KA9DLgLfnoB1nwBPUbBwHvccT8KCw6kX4eG9OvQMPdY+0YR3DK4Ix2b1Abglbkbee3HTXRvUY9T2zegb/so4ts2sMRgjI952yI4hNt3YGn5h1QyaxGcgCP7YP4z8PP/3MrkO9dAcC1/R1WsZX8k8cPqXSz8fR/L/kgiM1sJDBBiWtSjbwc3+Bzfpn7uYjhjTPHK2jW0DPiLqlaKT19LBGVwaCfs+NVNQVWF+f+F2D9D7Qrt3Su1lPRMlmw+wMLf97Hw9/0s9ySG9g0jmDluIOD2YWgTFW5lto0pQlkTwQDg/3C7kq1UVb9ODrdE4CM7lrvxg6BQ6HM99Lstb0pqJXfkqEsMR45mcm5MM1SV+H//wIBOjZg4OhZV5aff9xHXqj61QvKK41o5DFOTlTURtADeB/oWdr83Zah9yRKBD+3dALMfgZUfQ2gdV9Su360QUqFbTJRZdrby44a9RNYKpmerSDbtPcKgJ2cTHCj0bBlJ3w5RZGUrb8zfRJqVwzA1VFkTwVygPvAyhQwWq+rHPorTK5YIysGuVTDrP67b6NbFrpWgWq51jMpTWkYWC3/fx0+erqSV25LJyi78b93KYZiaoqyJIAU4RVVXlkdwpWWJoBylJbsaRhlpMOlc6HkZ9B7jEkMVdigtg5gJ3xV6nwDvX9+X6St2cPuZHanv2eLTmOqmrLWGVgO2C3pNEFbP/ZuyF4LD4evx8FxvWPo2ZGUW/9xKrE5YMC0iC58l1TyyFhv3HObjpVsJC3a9nE98u5YhT8/h9qkJvDJnI3N/28Pew0crMmRjKoy3LYJzcAvK/g9YAWTkv19V95dHcEWxFkEFUYXfZ8HMf8O2JdCgA1w9Heo09XdkJ6S4chjD41qgqoinK+zjJVuZvmIHq3ccZEdyWu7jG9UJpWuzunRpVpduzetyQc/mFf5zGHOiyto1lH+XlPxPENzmYzZYXJ2pwrqvYe1XcOHzbtxgz2/QsGOVG0M4kVlDB46ks2bHQVZ7Lmt2HGLD7kO0iKzF7PGDAHj4q9XUCQvmtjM7AnA0M4vQoAr9b2FMicq6sniQj+MxVYkIRP/JXQCO7IVXB7pEMPgfcNKZcHgXfHQ1jHgT6jTxZ7TFGh7XotQzhOpHhNDvpIb0OylvFXR6Zja7D+W1FHYfOsrRTPd9SVXp+8hM6oQF5bYeujarS5fmdWleLyy31WFMZVFiiyBno3ngKlVdVyFRlcBaBH6WlQm/vg9zHoWkLdC6H9SKhN++gd5Xw/kT/R2hX2VkZfPKnI25rYfEfUdyt4moVyuYLs3q0KVZXYZ0bUrfDlHFnsvWPRhfOuEWgapmiEg7rL6QyREYBHGXQ8xIWPoWzH4MUva4+5ZNgQF3V+pWQXkLDgzglsEdc28fOZrJ2p2HPInhIKu3H2Tqz3/QsHYofTtEsffwUa54bRH3nBvNwM6NSU3PIjUji7m/7TlmTGNbUir3fuK2MLVkYHzJ266ht4C/AuPLMRZT1QSFwCl/dfsrL5sC2Zmg2fDWBRB9npt2Wr+tv6P0u4jQIHq3qU/vNvVzj2VlKxlZrivpcFomTeuF5Zbf/nH9Hq6bvIQAgYJLH1Izsnji23WWCIxPeTtY/CJwObAJWAIcyX+/qt5WLtEVwbqGKpFDO+G/PSEzr78cCcjbMvOksyD+Gug4pNw3yakuNu87wrerdvKf6WuLfMzjl/Tg3JimtneDKZWyriPoAiwFDgDtgZh8l+6+CtJUQXMed62A/AKCoMdo10W0ayVMvQwWPOuf+KqgNlERXHdGhyLXPQQI3PXxr7mlMr5btZNX524ku4iV08aUxKuvaKpqs4ZM4bb+DFnpxx7LSofdq+DiV+CM8W4QuUUvd9+6ryHhHTj5Wmg30O29bAo1fmjnQtc9/Oei7sS2rk+jOm6196x1e/hp416uO6MDABO/W0dmtnJy2wb0al2feuHWajDFK1VbXUTCgJNwA8cbVTWthKeY6u6GecXfHxgEXc7Pu52yH7b8BGu/hPrtIP5qiL0cIhoWfY4aKmccoKRZQ49cHENKet6q75XbDzL3tz28OHsjAJ2b1CG+bX13adOAlvVr2RRWcwxvxwiCgf8AtwAhuIVkR4HngPtVNaOYp/ucjRFUcZlH3W5pi9+AzfOhYWe4eVGVW5xWmaWmuz2iFyfu55fNB0jYfIBDR12yaFo3jDH92nLjQNeCyM5WAgLsva8Jyrqg7DHgMuAG3JoCgNOBR3DjDON8EaSpIYJCIWaEu+xe4wacRVyhu8kXQbfhboyhVqS/I62yaoUE0rdDVO46haxsZd3OQyzevJ/FiQeoE+b+6yenZND/sZn8a3g3LoprydHMLDKz1HZ9q2G8bRHsBK5R1ekFjp8HvKaqzcopvkJZi6Ca2v87fHQtbF/qCt51v8TNOMoZXzA+t/tgGs/P2sAlvVrSs1Uks9bu5i9vL6ZLszrEt2nAyW0bEN+2Pk3qhvk7VOMDZa01lArEFlxZLCLRQIKqVujmt5YIqrntCbB4Eqz4EDJS4LrZ0DzO31HVCBv3HGZawjZ+STxAwh8HcmcmtWpQi/g2DXLHGTo2rn1cd5Ktgq78ypoIFgJLVPXmAsdfwiWIQncuK/DYVsDbQBPcYPOrqvpfEWmA2/2sLZAIjFLVA8WdyxJBDZGW7GYZ9Rjtuo6+fwDSU9wAc5Nu/o6u2svIymb19oP8krifJZsP8EvigdxS3D/dO5hm9Wrx69Yk0jKy2XYghfs+XVlkZVdTOZQ1EZwBTAe2AQs9h08FmgPnqmoJU0dARJoBzVR1qYjUwS1MGw6MBfar6qMicg9QX1XvLu5clghqqC/vdFNPs45Cq1Ndt1HXCyHYui0qgqqyeV8KK7Yl55bfvnnKUpb9kQS4EhgFtYgMY/49Z1ZkmKYYZUoEnhM0B24Goj2H1gAvqur2EwxoGvC85zJQVXd4ksVsVe1c3HMtEdRgR/bB8nfdjKP9v0OfG+HcR/0dVY21/0g62w6kMuz5eUUWI2sTFU6zemE0q1eLpvXCiGlRjz/FuGHF5NQM6oYF2XTWClLWWUN4PvDv91EwbYE4YBHQRFV3eO7aies6Kuw51wHXAbRu3doXYZiqKCIK+t0Kp94MiXOhXit3fMsimP0f10ro/CcItEVUFaFBRAgNIkJoHlmr0BZB7dAgerSMZEdSKj9v2s+ug2kMim6cmwjOnjiHM7s05pGLe5Cdrfz9w+U0rhtKc0/SaFYvjKb1wmgYEWpTXMtRsYnA039fotLsUCYitYGPgb+p6sH83wRUVUWk0C8Wqvoq8Cq4FoG3r2eqqYAAaD8w73bKPti7AT64Cmo3gV5XQa8xENnKbyHWJEWtgv738O7HjBFkZ+sxj7ll8Em0axgBwKGjmSzevJ+dyWlkZB37Xzw4UGhS1yWGUfGtGBnfioysbGas2UXPVpE0q1f6+So2uJ2n2K4hz85kJX3oqqp61bLwLEz7EvhWVSd6jq3DuoaML2RnwfrvXbfR+u/cauU711qxuwriqw/W7Gxlf0o6O5LS2JGcys6DaexITmNnsrs9PLYFl57Smq0HUuj/2CweuySG0Se3ZuW2ZK6fvISmnlZEs7ru3+aRea2LRrVDCQoMKHHb0urqhMYIRGRAMec8B7gdyFTVEje2F/fV/y3cwPDf8h1/AtiXb7C4gareVdy5LBGYEiVtgT3roOPZkJ0Nk4dDuzMg7kpAq8RuaqZ46ZnZrN99iKZ1w4iqHcqG3Yd4cdZGlzQOuqSRM/01R4DAi5f34qEv1xTalVU/PJjHLulBRGgQtUICCQ8JJDw4iPBQdz0sKNAvXVS+SrJlHizOd6I44AncyuJXgIdUdY8Xz+sP/AisAHJ+O/fhxgk+AFoDm3HTR4vtarJEYErlyF746BrYNMdVRq3XEg5sduMJNXw3tepMVUlOzWCHpyWR06oYHteCs56ac8I7bX11W3+6Na/HtGXbeHHWRt6//lQiw0OYtmwbM9bsJiI0kFrBQYSHBFIrJJCIkEDCQ/ISS/+ODQkNCmT/kXRSM7KKrDKbw5etlzIPFnt2KXsYGAl8AnRV1Y3ePt8zxbSoVGrzy0z5iWgIYz53Ywg/PQ9LJrnjCZNdqeygUAgKs2mo1YyIEBkeQmR4CF2aHdtpUdTgduM6obw+5mRS0jNJycgi5WgWKemZpGZkkZKeRcrRzNxV1nXDgmnbMJyQIFdBd/fBo/y6NYmU9CxS07M4kp553MZCAMsfHEJoUCCvzNnImwsSWffvcwEY9+Fyvlm5Mzdh1AoOJCI0iJXbknP3w87h6w2KSkwEIhIFPICrMzQf6Keqv/jk1Y2pSA1PcpvmBIbklc6e8xjUqg+LXnab6ESfD52GQFg9/8ZqylVRg9v3/akLMS29+90Pim7MoOjGubf/ekZ7/npG+9zbqsrRzGxS07M8SSWTlPQsanvqOJ3foznRzerkPv70jg2pGxZMakYmR466xJOakXlcEsixvZBEdqJKGiO4H7c9ZSJwj6p+47NXLgPrGjInpLDd1ILCYMQkN7i8bjoc3gUBwdBpKIx+xyqiVmNVZdbQaY/OLGKxXi3m3zO4VOc60a6hh4BUYCtwk4jcVNiDVHVYqaIxxh8K201Ns2HDD3DBM3DeRNj6i9srISsjLwl8dhM07ORaCw1PqvCwTfkYHteiUn7wF1RU62X80GInV5ZKSYngbUqePmpM1VDUbmpbf3bXAwKgdR93yZGeArtXw7Ip8MOD0Cgaos+DHpdCo04VF7upsbzdoKgsSj1rqDKwriFT4ZL+cF1Ha7+ExPlwwX+h15VweI/blrPNabaa2VR6ZZ41ZEyNFtkK+lzvLin73YAzwOrPYPo4CIuETue41sJJZ0JIhD+jNaZULBEYU1rh+SqvxF4OdZrB2q/gt6/h16luU52/r3Uzj7KzXZeTMZWYJQJjyiIkHLqc7y5ZmW4P5h3L86afTv0zpB92A83Rf4JIK5hoKh/7qmKMrwQGQfsBcNptecda9HIrm7+5G56JgVfOgIQp/ovRmEJYIjCmPA24C25eCLcuhbP/BYGhbq0CQPoR+O4fsGWhK5hnjJ/YrCFjKpqqW6OweQG8NQyyMyCikdtHIfp816oICs17/KGdViTP+ERRs4asRWBMRctZqNamH9z1O1zyOrTtDys/hndHwt7f3P0Hd0DaQbcQbstCVw7DmHJgg8XG+FNYXYgZ4S6ZR91gc5Pu7r6Z/4blU4FstwJ62RRXJM9aBcbHrEVgTGURFAodBue1GOKvgcbReWUxMtNg0jn+i89UW5YIjKms6rWAfRuOPZa0BQ7tcusTpoyEeU/DPq+rwRtTKEsExlRWhRXJkwA3VnBkNxzZAz9MgOd6wUunwexHXaIwppQsERhTWRVXJK9OU7huNvxtBQx9BELruESQM9CctAW2LXUzlIwpgU0fNaa6OLQTwqNc8bsf/gnzJkK9VtDlAndp1QcCAv0dpfEjKzpnTHVXp2ne9X63QsOOsPpz+OV1WPgi1G/nFrZZ7SNTgCUCY6qj8AYQ+2d3OXrI7cB2aFdeEnjzfNda6DoM2g+y/ZprOEsExlR3oXWg+yV5tzPToW4LVzF1+bsQUhs6DoFTroM2ff0Xp/EbSwTG1DRBIXDxKy4hJM6FNV+4pHDSWS4RHNwBv8+GzudArfr+jtZUAEsExtRUQSHuw/+ks9x+zTmF7377Gr68AwKCoN0Z0GWY23CndmP/xmvKjY0aGWPcbKIgz65rvcbCX2ZC35th/yb48m8wsQukHnD3Z6YXdRZTRVVYi0BE3gDOB3aranfPsQbA+0BbIBEYpaoHKiomY0whAgKgZW93OeufsGsVbFuS10303mhIS/ZMSx0GUR3ynmuVUqukimwRvAkULJRyDzBDVTsCMzy3jTGVhQg07Q69x+Qd6zDYLVTLWdX8Yj9YOtndZ5VSq6QKSwSqOhfYX+DwhcBbnutvAcMrKh5jzAnqdytcNwv+thLOedRty5m637UGlr3jymIkTIYDVu6iqvD3YHETVd3hub4TsLakMVVFZCs49UZ3UYWv/p434JyVDs/GQrvTod0At54h/4I3U6lUmsFidbUuiqx3ISLXichiEVm8Z8+eCozMGFOiw7vcfgnZmcceT94OM/6ZN9CcOB9+/h/sXW91kCoRf7cIdolIM1XdISLNgN1FPVBVXwVeBVdrqKICNMZ4obBKqQGBrkVw9VduK05w6xUWvuCu12nutuVsNwB6jLbSF37k73f+cyBnFGoMMM2PsRhjTlRxlVJrN87bbGfow67e0flPQ6tT4Ldv3cByThJIeAfWfAmpSRUafk1XYdVHReQ9YCDQENgFPAh8BnwAtAY246aPFhxQPo5VHzWmmsjOhsM7oW5z11X03x6uhLYEQPM411qIPg9aHlcw05wAv1cfVdXLirjrzIqKwRhTyQQEuCQArtVwyxLYttiVuPh9Dix41u3l3DIesjLgp+eh7RnQPNZKavuQv8cIjDEmT1AItOnnLoPuc5VTM4+6+3atcmsXAELrQdv+0H6gq6BqM5LKxBKBMabyCq3jLuBaAePWw6a5rsWwaQ6s+woad3GJYNcq2PGrG4DOaWUYr1giMMZUHbUbQ8wIdwFXC6luC3d91Wcw93F3PaqjSwjtB0Knc9yubaZI/p41ZIwxJ65Bu7xieQPvhRvmwZCHoX5bWPYeTLvZDTwDrJ0OG2dBRmre8w/thEnnuk17ajBrERhjqoeAAGga4y79bnFVUg9syhtUnvkQ7F4NgaFu6mr7AbBzRV5tpPMn+jd+P7JEYIypnoJCoFHnvNvXfgebf3JjC7/Phpn/dq0FzXarouu1gk5DoFGXGre4rcLWEfiSrSMwxpTZpzfCig8hOwMCgt2/AOENPTWSzoBO50LdZv6N04eKWkdQs9KeMcaAGxtY9Uneh392BgSFwtBH3I5tWxa6Xdr+WOjuP5DoxhySt/kt5PJkXUPGmJqnsNpIqrBvg9vPWRX2bczbXGfdN/DN3e56gw6utdB+gJuRFFyrYmMvB5YIjDE1T3G1kcCtcm54Ut59p1znFrBtmuPWMaz4yO25cPdmd/+GH9zK5zanQVjdivkZfMgSgTGm5rlhXukeHxDgdmpr2t3t5ZyVCXt/g9Da7v75z7okIYGeGklnwElnuuRRBVgiMMaY0goMgiZd827/+QPY+otrLWya62ok7ViWlwh+eR2adIPmvfLWPVQilgiMMaasgsM8M41OB+6Ho4chZa+7L+0gTB8PmgXBEdCmr2sxRJ8PUR38GnYOSwTGGONrobXzuo3C6sL4DZA4L6/F8P0DEBzuEsGhXbB6mksOjTrn7d1QgSwRGGNMeQtv4Kqkdh3mbh/a6aarAmyeD1+Pd9drN3EJod0Z0PVCCKuXd45DO+Gjq2HEm3mzmXzEEoExxlS0/GWzu18MLXrltRY2zXUL3Tqc6RLBprkuCWycWW7lMCwRGGOMv9Vv6y69rspbw1DPU1U14R349f28xy6bAgPu9mmrwFYWG2NMZVJwDcPwl6DLhW5qKriFcHMe8+lLWiIwxpjK7MgeWP+tm3UEbuHbsik+LZ1ticAYYyqzQsth+LZVYInAGGMqs5LKYfiADRYbY0xlVtpyGCfAWgTGGFPDWSIwxpgazhKBMcbUcJYIjDGmhrNEYIwxNVyV3LxeRPYAm0/w6Q2BvT4Mp7xVpXgt1vJTleKtSrFC1Yq3rLG2UdVGBQ9WyURQFiKyWFXj/R2Ht6pSvBZr+alK8ValWKFqxVtesVrXkDHG1HCWCIwxpoariYngVX8HUEpVKV6LtfxUpXirUqxQteItl1hr3BiBMcaYY9XEFoExxph8LBEYY0wNV2MSgYi8ISK7RWSlv2MpiYi0EpFZIrJaRFaJyO3+jqk4IhImIj+LyHJPvP/0d0wlEZFAEUkQkS/9HUtJRCRRRFaIyDIRWezveIojIpEi8pGIrBWRNSLS198xFUVEOnve05zLQRH5m7/jKoqI3OH5/7VSRN4TkTCfnbumjBGIyBnAYeBtVe3u73iKIyLNgGaqulRE6gBLgOGqutrPoRVKRASIUNXDIhIMzANuV9WFfg6tSCJyJxAP1FXV8/0dT3FEJBGIV9VKv+hJRN4CflTV10QkBAhX1SQ/h1UiEQkEtgF9VPVEF6uWGxFpgft/1VVVU0XkA2C6qr7pi/PXmBaBqs4F9vs7Dm+o6g5VXeq5fghYA7Twb1RFU+ew52aw51Jpv2GISEvgPOA1f8dSnYhIPeAM4HUAVU2vCknA40xgY2VMAvkEAbVEJAgIB7b76sQ1JhFUVSLSFogDFvk5lGJ5ulqWAbuB71W1Msf7DHAXkF3C4yoLBb4TkSUicp2/gylGO2APMMnT7faaiET4OygvXQq85+8giqKq24AngS3ADiBZVb/z1fktEVRiIlIb+Bj4m6oe9Hc8xVHVLFWNBVoCp4hIpex+E5Hzgd2qusTfsZRCf1XtBZwL3Ozp5qyMgoBewEuqGgccAe7xb0gl83RhDQM+9HcsRRGR+sCFuGTbHIgQkSt8dX5LBJWUp6/9Y2CKqn7i73i85ekKmAWc4+dQinIaMMzT7z4VGCwi7/g3pOJ5vg2iqruBT4FT/BtRkbYCW/O1Bj/CJYbK7lxgqaru8ncgxTgL2KSqe1Q1A/gE6Oerk1siqIQ8g6+vA2tUdaK/4ymJiDQSkUjP9VrA2cBavwZVBFW9V1VbqmpbXHfATFX12TcrXxORCM+EATzdLEOASjnzTVV3An+ISGfPoTOBSjnBoYDLqMTdQh5bgFNFJNzz+XAmbuzQJ2pMIhCR94CfgM4islVErvV3TMU4DbgS9201Z2rbn/wdVDGaAbNE5FfgF9wYQaWflllFNAHmichy4GfgK1X9xs8xFedWYIrnbyEW+I9/wymeJ7mejfuGXWl5WlkfAUuBFbjPbp+Vm6gx00eNMcYUrsa0CIwxxhTOEoExxtRwlgiMMaaGs0RgjDE1nCUCY4yp4SwR1GAiMltEnvfTa6uIjPDHa3ujssfnC54qlhO8eNwsEbmqAkIq7LWL/T14qt5eUpExVUeWCKopzyKvFz0ljI+KyC4RmSEiZ+d72MXAvf6K0ddEpJfng+P0Iu5/X0QWVHRcxSnqg05E3qwMJbJF5DygFTAl37FET9wqIqmektPjPQudKtpDwKMiYp9lZWBvXvX1Ma4UwbVAJ+B84GsgKucBqrrfU920yhGRoIIfPJ6KrcuAawp5fBQwHKs4Wlq3A2+qalaB4//CLSTsgiuG9h/AHwXxpgN1cGUizAmyRFANeco9nA7co6ozVHWzqv6iqk+q6tR8jzuma8jzTe//ROQVzyYdW0VkfIFzdxKROSKSJiLrRORPInJYRMZ67m/r+aYYX+B5JTXxH/WcL9UTx+P5N94QkQmeroyxIrIROAoUVtnyNWCkp2Bffld4nvO+iJwjIj+KyAER2S8i34pIl2Ji8+pnEpEWIjLVc94DIvKViHQs6rylISIXi8ivnvdnv+d30CTf/ReIq06aJiKbRORhccXUcu5vLCLTPM/fLCLHJctCXrMRrsbNF4XcfUhVd6pqoqq+BvyKK3+R89wOntfbKSJHRGSpuIJ/+c9f4t9bITHdLSJ7ReRUcMUOccngspJ+HlM0SwTV02HPZZiUfhejO3BL2HsBjwGPi2eXKU/z+1MgEzgVGAs8CIT6IOYjuG/yXYCbcHWA7i/wmHbAn4GRQE8grZDzTAECgdEFjl8LvK+qR3AJ5Blci2kgkAx8kf+Ds7REJBxXbC8NGAD0xZUL/sFz3wkTkaa4Anlv4d6fM4DJ+e4fivu5nwe64d7HERxb3uFN4CTcB/tw4CqgbQkv3R+XPIusbSTOQE9cGfnuqo1rgZ6N+119DHwiItEFTlHk31shr/MkroTFgAKbHv2Me8/NiVJVu1TDC3AJbiOeNFyNpSdxuy/lf8xs4Pl8txOB9wo8Zj3wf57rQ3FJoEW++/vh6uWP9dxu67kdX+A8Cowo6nYh8d8AbMh3ewLug6aJFz/7O8CCfLdP9rxenyIeHwFk4co9HxefNz8T7sN3PZ6yLZ5jgcA+YFQxsRb6PuA+uL/0XO/leVybIs4xF/hHgWPDcV8GBNc1qMBp+e5v4/mZJxQT29+AzYUcT8QliMNAuufcqUC/En4vC3P+lrz5e8v3/owGJgG/FfYe4EpIZwNBFf3/rLpcrEVQTanqx7i65Rfgvpn1AxaKyH0lPPXXAre3A40916OB7eopi+zxCz7Y4EVERojIPE9XwmHgaaB1gYdtVe9KBb8G9M337fMaYKV6yiN7ui3eFZGNInIQ2IVrHRd8vdLojWuxHPJ0lR3GtTTqAx3KcF6A5cAPwEoR+VhEbvR02+R/7ftzXtfz2u/iElxT3Lf1bNw3ZwDU7cRV0g5XtSi81QUwEVdUbgCuJfRPVc0diBdXNfVxcftuH/DEFM/x73Fxf285nsS13Ppr4TuIpeISns/28K1pLBFUY6qapqrfq+q/VLUfrrT1hBK6QDIK3FZK93eSkxRyB3LF7a1QJE9/71TgW1ziigP+D7flZX5HvIxhDrABuEZcWezL8Gyf6PEl0Ai4Hujjeb1MoKj3xZufKQA3UB1b4NIJeKWYWA8B9Qo5HolLJKjrBx/iufyK6+ZaLyI98732Pwu8bg+gI27HsBylrTC5F5fICrNPVTeo6k+41uc4ERmU7/4ncV14/8Ali1hcIir4Hnvz9/Y9LqEVVYG3AZCmedulmlIK8ncApkKtxv3Ow3BN+tJaCzQXkeaqmvNtMp5j/+PmfPA0y3cstoTzngZsU9WHcg6ISJsTiA9weyiLyBu4GS9rcd9sJ3vOG4Vr2dykqrM8x3pR/P8Fb36mpbiEs1dLt0/vOtw3+txEJW4j9Z647pDcnwnXxfeTiPwLWIXrMlnuee1oVd1Q2AuIyFrc7+gUYIHnWGtci7E4CUAjEWmoqnuLepCqHhA36eBpEYnzxNofeNvTMsUzVtUB171TWtNxZaI/FBFV1bcK3N8d9x6YE2QtgmpIRKJEZKaIXCEiPUSknYiMxO3TO0NPfNvL73EfXG+JSE/PN/mJuG/TrkNXNRXXF3y3iHQTkX64b4fF+Q1oISKXi0h7EbmRss8CeQto6Hntz1R1n+f4Adw33b+KyEkiMgB42fMzFMrLn2kKrotpmogM8LznZ4jIUyXMHJqIa7ncLG5GViyuznwDz7+IyKme2TUnez7Ah+Hm9uds+vIv4M8i8i8R6S4i0Z6utsc98a8DvgFeEZG+ntd4E9elUpwE3B7U/Ut4HMCLQGdcKwDc7/QicWs7YnDjNifcdaNuf4uRwMty/OK203E/nzlBlgiqp8O4D67bcd0kq3AzSN7l+Nk0XlPVbOAi3Cyhn3Eftg/jkkD+vuScqYm/4LpF/q+E834BPIGbyfMrbqbJAycap+ec23HfJOuTb+2A52cYjes6WQm8gOu+OFrCKYv9mVQ1BTeb53fc3rdrce9PfVzyKSrO94CrPZfFuA+0psDp6nb8AtdFdBquS2s98BTwkKq+4znHt8B5wCDc7+Vn3F7BW/K91FhgEzATNx30XdxgbZE8XVJvAJcX9zjPY3fjWl0TPLPL7sQlkR9xY1QLPddPmCcZjMIltKvATdnFjX9NKu65pni2MY0pE08/9TLcjJqqtCG88YKINMa1PE5W1U3+jqcgEXkCqKeq/ljMVm3YGIEpFRG5CDdoux43rXIief3UpppR1d3iFp+1xrUoKpvdlNz1aEpgLQJTKp4m+f/h+qgP4NYi3OHltE5jTCVkicAYY2o4Gyw2xpgazhKBMcbUcJYIjDGmhrNEYIwxNZwlAmOMqeH+Hy6iDrJ8QcLXAAAAAElFTkSuQmCC\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -591,7 +591,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -603,7 +603,7 @@
     },
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPsAAAEICAYAAACZA4KlAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/d3fzzAAAACXBIWXMAAAsTAAALEwEAmpwYAAAUkklEQVR4nO3df4xV5Z3H8fe3yK/KYBGmgIgDAroCRSATUiq2rm4t2jTaZNNWN103caXp1mSbdP8w3WTrbvaPdrNt081u3NDV1DZdqbaauhvaqtQGTS064AhYrfwoWJAfQwUB5YcD3/3jHrID3u8zM+f+muH5vJLJ3Hm+99zzzJn7nXPv+d7neczdEZHz3/ta3QERaQ4lu0gmlOwimVCyi2RCyS6SCSW7SCaU7CKZULLLoJnZfDP7hZkdMLP3fFDDzC42s8fM7G0z22lmt7ein3I2JbuU8S7wMHBnEP8P4CQwGfgL4D4zm9ekvknA9Am684uZ7QD+HfhLoAP4OXCHux9vwL5mA1vc3fq0XQgcBOa7+2tF2w+A3e5+T737IAOnM/v56TPAcmAmsAD4q2p3MrNlZnYo8bWsxL6vAHrPJHrhJUBn9ha7oNUdkIb4N3d/A8DM/gdYWO1O7v4s8IE673sccPictreAtjrvRwZJZ/bz094+t9+hkoDNchQYf07beOBIE/sgVSjZM2Zm15rZ0cTXtSUe9jXgAjOb06ftauDl+vRaytLL+Iy5+zOUOOubmQGjgVHFz2MqD+cn3P1tM3sU+Ccz+2sqbyFuAT5St45LKTqzSxkdwDH+/2x9DPhdn/jfAGOB/cBDwBfdXWf2FlPpTSQTOrOLZELJLpIJJbtIJpTsIploault0qRJ3tHR0cxdimRl586dHDhwwKrFakp2M1sOfAcYAfyXu389df+Ojg5+/etf17LLIalSdq4uVe1Ixco+ZpnHSynbx2Y9Xn+PWe99DXUf+Uj8cYbSL+PNbASVoYw3AXOB28xsbtnHE5HGquU9+xJgq7tvd/eTwCoqn5QSkSGolmSfBvyhz8+7irazmNkKM+sys66enp4adicitWj41Xh3X+nune7e2d7e3ujdiUiglmTfDUzv8/OlRZuIDEG1JPsLwBwzm2lmo4DPAY/Xp1siUm+lS2/u3mtmdwO/oFJ6e0Ajm0SGrprq7O6+Glhdp76ISAPp47IimVCyi2RCyS6SCSW7SCaU7CKZULKLZELJLpIJJbtIJpTsIplQsotkQskukgklu0gmlOwimVCyi2RCyS6SCSW7SCaU7CKZULKLZELJLpIJJbtIJpTsIplQsotkQskukgklu0gmlOwimahpRRgz2wEcAU4Bve7eWY9OiUj91ZTshT919wN1eBwRaSC9jBfJRK3J7sATZrbezFZUu4OZrTCzLjPr6unpqXF3IlJWrcm+zN0XAzcBXzKzj557B3df6e6d7t7Z3t5e4+5EpKyakt3ddxff9wOPAUvq0SkRqb/SyW5mF5pZ25nbwI3A5np1TETqq5ar8ZOBx8zszOP8t7v/vC69EpG6K53s7r4duLqOfRGRBlLpTSQTSnaRTCjZRTKhZBfJRD0+G5+Nt956a1DtAO97X/z/dObMmWHs+PHjYWzEiBFh7MiRI1Xbd+3aFW5z+vTpMHby5Mkwtn379jB29OjRqu2p45E6jtu2bQtjF1wQP43nz59ftX3ZsmXhNrNmzQpjw5nO7CKZULKLZELJLpIJJbtIJpTsIplo+tV4d2/2Lutmx44dVdufe+65cJt9+/aFsblz55bqx6lTpwa9v9///vfhNqm/yeHDh8PY66+/HsaKMRPvceGFF4bbpK7Gd3d3h7He3t4wduutt1Ztv+KKK8JtLr/88jA2nOnMLpIJJbtIJpTsIplQsotkQskukgklu0gmNBBmEKLS0Pr168Ntfvazn4Wxt99+O4yNGTMmjKVKb9GgkPHjx5faV2pAzrhx48LYpZdeWrV9ypQp4TYTJkwIY1HZE9Ilu2hG44kTJ4bbnK90ZhfJhJJdJBNKdpFMKNlFMqFkF8mEkl0kE00vvaXmIBvqFi9eXLU9NZIrNbpq3bp1YSw12iy1v6iP119/fbjN7Nmzw1iqZJdalfeiiy6q2j569Ohwm9WrV4exX/3qV2Fs3rx5YeyGG26o2p6a/284P0dT+v2tzOwBM9tvZpv7tF1sZk+a2Zbie1wgFZEhYSD/wr4HLD+n7R5gjbvPAdYUP4vIENZvsrv7WuDNc5pvAR4sbj8I3FrfbolIvZV9czLZ3fcUt/dSWdG1KjNbYWZdZtZ14MCBkrsTkVrVfCXCK3MahfMauftKd+90985JkybVujsRKalssu8zs6kAxff99euSiDRC2dLb48AdwNeL7z8d6IbDecLJqJy0aNGicJtUiee2224LY2XLP1FpKzVCLVUOS42wmz59ehiLlqhKTRy5atWqMBYtJwWwdOnSMHbZZZeFschwfo6mDKT09hDwHHClme0yszupJPnHzWwL8GfFzyIyhPV7Znf36PRT/dMKIjIknZ8fFRKR91Cyi2RCyS6SCSW7SCa01tsgROWw1ISNqUkU29rawtjYsWPD2MmTJ8NYdHxHjhwZbpNy4sSJMJYafRdNpvnqq6+G22zYsCGMRWvHQbq8+cEPfrBq+6hRo8JtTp8+HcaGM53ZRTKhZBfJhJJdJBNKdpFMKNlFMqFkF8mE1nobhN7e3qrtqbJQKhaNDIPyJcrU/iKpUlNq9F2qj9u2bava/vzzz4fbHDp0KIwtXLgwjM2dOzeMRaXP1O+l0puIDGtKdpFMKNlFMqFkF8mEkl0kE02/Gl/mavFQEV2NT129TQ1AueCC+PC/++67YSx1DKOrzKk+puaZSw3IiQa7AKxfv75q+7PPPhtukzpWN910Uxj70Ic+FMai/qeOx3B+jqbozC6SCSW7SCaU7CKZULKLZELJLpIJJbtIJjQQZhCiElWqjJMqr6UGY6TKYan506LHTJXyUlKDdfbs2RPGotLb1q1bw21SSzUtWbIkjLW3t4exSFRGhfTvPJwNZPmnB8xsv5lt7tN2r5ntNrPu4uvmxnZTRGo1kJfx3wOWV2n/trsvLL5W17dbIlJv/Sa7u68F3mxCX0SkgWq5QHe3mW0sXuaHk6Ob2Qoz6zKzrp6enhp2JyK1KJvs9wGzgIXAHuCb0R3dfaW7d7p7Z5kLKSJSH6WS3d33ufspdz8NfBeIL5WKyJBQqvRmZlPd/Uzd5dPA5tT9zxdlllBKlddS5bDUdqlRWVEZMFXKSy1fdezYsTD29NNPh7ForrmJEyeG23z2s58NYwsWLAhjKWXmDTxf9ZvsZvYQcB0wycx2AV8DrjOzhYADO4AvNK6LIlIP/Sa7u99Wpfn+BvRFRBpIH5cVyYSSXSQTSnaRTCjZRTKhUW+DEI2GKjt6LTXyKjVaLlU2ivZXtpT3xz/+MYx1d3eHsR07dlRtv+SSS8JtUss4tbW1hbHUcYyWqEodj/NVfr+xSKaU7CKZULKLZELJLpIJJbtIJpTsIplQ6a0OUhNORqUfSJd/yq4DF0lNUnn06NEw9swzz4SxF154IYxFI+mWLl0abjNv3rwwlhpxePLkyTAWlRVzHPWmM7tIJpTsIplQsotkQskukgklu0gmdDV+EKKr7qkr7qlYI5aGKjNY54033ghjq1fH63+89tprYWz+/PlV26+77rpwm46OjjCWqnikrqxHv3dqm9TfbDjTmV0kE0p2kUwo2UUyoWQXyYSSXSQTSnaRTAxkRZjpwPeByVRWgFnp7t8xs4uBHwEzqKwK8xl3P9i4rrZeVJJJlWpSJZ5ULDWvWqqMFsUOHoz/NC+++GKpWKr/V1111aDaAUaPHh3GUstQpQbJRMfjfC2vpQzkzN4LfMXd5wIfBr5kZnOBe4A17j4HWFP8LCJDVL/J7u573H1DcfsI8AowDbgFeLC424PArQ3qo4jUwaDes5vZDGARsA6Y3Gcl171UXuaLyBA14GQ3s3HAT4Avu/vhvjGvvAGq+ibIzFaYWZeZdfX09NTUWREpb0DJbmYjqST6D9390aJ5n5lNLeJTgf3VtnX3le7e6e6d7e3t9eiziJTQb7Jb5ZLr/cAr7v6tPqHHgTuK23cAP61/90SkXgYy6u0a4PPAJjPrLtq+CnwdeNjM7gR2Ap9pSA+HgVQJKhqFBumRXO+8804YmzBhQhiLRsS9/PLL4TaPPPJIGHv11VfD2NVXXx3Grr322qrts2bNCrcpOy9cmRFsZUfRDWf9Jru7PwtEv/0N9e2OiDSKPkEnkgklu0gmlOwimVCyi2RCyS6SCU04OQipkWhlpMpyZZc7ikpsqfLamjVrwlhbW1sY+8QnPhHGrrnmmqrt48ePD7dJLUOVmpyzzFJZZUcqDmc6s4tkQskukgklu0gmlOwimVCyi2RCyS6SCZXeBiFVKisjtWZbavLFVAnw9ddfr9qemjjyyJEjYezGG28MYx/72MfC2CWXXFK1PTXaLKXs2nc5TiwZ0ZldJBNKdpFMKNlFMqFkF8mEkl0kE02/Gj+cr45GAy5SAydSV4pTsdRAmP37q07kC8CmTZuqtkdX6QFmzJgRxj71qU+FsdQcdKNGjaranppbL9oG0sf4xIkTYSz6m6Wu7g/n52iKzuwimVCyi2RCyS6SCSW7SCaU7CKZULKLZKLf0puZTQe+T2VJZgdWuvt3zOxe4C7gzNKsX3X31Y3q6PkoVf6J5k4D+M1vfhPGfvnLX1ZtT83vdvvtt4ex5cuXh7EpU6aEsagclhoIU2Yuuf5Ex7hsuXQ4G0idvRf4irtvMLM2YL2ZPVnEvu3u/9q47olIvQxkrbc9wJ7i9hEzewWY1uiOiUh9Deo9u5nNABYB64qmu81so5k9YGbx0qIi0nIDTnYzGwf8BPiyux8G7gNmAQupnPm/GWy3wsy6zKyrp6en2l1EpAkGlOxmNpJKov/Q3R8FcPd97n7K3U8D3wWWVNvW3Ve6e6e7d7a3t9er3yIySP0mu1UuW94PvOLu3+rTPrXP3T4NbK5/90SkXgZyNf4a4PPAJjPrLtq+CtxmZguplON2AF8YyA6H89I6Ufmn7Lxq73//+8PY3r17w9hTTz0Vxrq7u6u2X3nlleE2d911Vxi77LLLwtixY8fCWGTs2LFhLFVeS8VS8/VFz7fU32w4P0dTBnI1/lmg2m+vmrrIMKJP0IlkQskukgklu0gmlOwimVCyi2RCyz8NQlSSSS0LlZo4MjX54hNPPBHGNm+OP9IwbVr1YQuf/OQnw22uuuqqMFZ2dFg0eWTq8VLltdRxTI0ePHnyZNX21KSS9V7ma6jQmV0kE0p2kUwo2UUyoWQXyYSSXSQTSnaRTKj0NghlSm+pstCbb74ZxtauXRvGtmzZEsY6Ojqqtk+dOrVqO6TLUPUuUaVGm6WOVSpWdj29iEpvIjKsKdlFMqFkF8mEkl0kE0p2kUwo2UUy0fTSW6qUM9RF5Z9UOenw4cNhLDV6bcOGDWHs4MGDYWzBggVV21Olt9Ros2j0GqRLVGUm4Uyt9ZYqoaWeU6n+R8pOIDrU6cwukgklu0gmlOwimVCyi2RCyS6SiX6vxpvZGGAtMLq4/4/d/WtmNhNYBUwE1gOfd/fqE36d51JXilNX43fu3BnGUivetrW1hbFomafZs2eH25S9qp7arkzVpRGDXaLlplL9O3HiRBgbzgZyZj8BXO/uV1NZnnm5mX0Y+AbwbXefDRwE7mxYL0WkZv0mu1ccLX4cWXw5cD3w46L9QeDWRnRQROpjoOuzjyhWcN0PPAlsAw65e29xl11A9TmMRWRIGFCyu/spd18IXAosAf5koDswsxVm1mVmXan3oSLSWIO6Gu/uh4CngaXAB8zszAW+S4HdwTYr3b3T3Tvb29tr6auI1KDfZDezdjP7QHF7LPBx4BUqSf/nxd3uAH7aoD6KSB0MZCDMVOBBMxtB5Z/Dw+7+v2b2W2CVmf0z8CJw/0B2mFr+Z6iL+j5mzJhwm8mTJ4exOXPmhLEpU6aEsdSAkUWLFlVtnzlzZrhNyvHjx0v1IyrL9fb2Vm2HdAkttfxTqox29OjRqu1l5w0czvpNdnffCLznGeTu26m8fxeRYeD8/BcmIu+hZBfJhJJdJBNKdpFMKNlFMmHNnBPOzHqAM0O9JgEHmrbzmPpxNvXjbMOtHx3uXvXTa01N9rN2bNbl7p0t2bn6oX5k2A+9jBfJhJJdJBOtTPaVLdx3X+rH2dSPs503/WjZe3YRaS69jBfJhJJdJBMtSXYzW25mvzOzrWZ2Tyv6UPRjh5ltMrNuM+tq4n4fMLP9Zra5T9vFZvakmW0pvk9oUT/uNbPdxTHpNrObm9CP6Wb2tJn91sxeNrO/LdqbekwS/WjqMTGzMWb2vJm9VPTjH4v2mWa2rsibH5nZ4Bayc/emfgEjqMxhdzkwCngJmNvsfhR92QFMasF+PwosBjb3afsX4J7i9j3AN1rUj3uBv2vy8ZgKLC5utwGvAXObfUwS/WjqMQEMGFfcHgmsAz4MPAx8rmj/T+CLg3ncVpzZlwBb3X27V+aZXwXc0oJ+tIy7rwXePKf5Fiqz9EKTZusN+tF07r7H3TcUt49QmQlpGk0+Jol+NJVX1H1G51Yk+zTgD31+buXMtA48YWbrzWxFi/pwxmR331Pc3gvEU9w03t1mtrF4md/wtxN9mdkMKpOlrKOFx+ScfkCTj0kjZnTO/QLdMndfDNwEfMnMPtrqDkHlPzuVf0StcB8wi8qCIHuAbzZrx2Y2DvgJ8GV3P2spnWYekyr9aPox8RpmdI60Itl3A9P7/BzOTNto7r67+L4feIzWTrO1z8ymAhTf97eiE+6+r3iinQa+S5OOiZmNpJJgP3T3R4vmph+Tav1o1TEp9n2IQc7oHGlFsr8AzCmuLI4CPgc83uxOmNmFZtZ25jZwI7A5vVVDPU5lll5o4Wy9Z5Kr8GmacEysMpPn/cAr7v6tPqGmHpOoH80+Jg2b0blZVxjPudp4M5UrnduAv29RHy6nUgl4CXi5mf0AHqLycvBdKu+97qSyQOYaYAvwFHBxi/rxA2ATsJFKsk1tQj+WUXmJvhHoLr5ubvYxSfSjqccEWEBlxuaNVP6x/EOf5+zzwFbgEWD0YB5XH5cVyUTuF+hEsqFkF8mEkl0kE0p2kUwo2UUyoWQXyYSSXSQT/weYte4bQI3fgAAAAABJRU5ErkJggg==\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -615,7 +615,7 @@
     },
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -640,7 +640,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -671,7 +671,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -700,7 +700,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -725,11 +725,11 @@
     "        \n",
     "        # 获取量子神经网络的酉矩阵表示\n",
     "        U = U_theta(self.theta)\n",
-    "        U_dagger = hermitian(U)\n",
+    "        U_dagger = dagger(U)\n",
     "        \n",
     "        \n",
     "        V = U_theta(self.phi)\n",
-    "        V_dagger = hermitian(V)\n",
+    "        V_dagger = dagger(V)\n",
     "        \n",
     "        # 初始化损失函数和奇异值存储器\n",
     "        loss = 0 \n",
@@ -746,7 +746,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
@@ -760,12 +760,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "主程序段总共运行了 871.9080681800842 秒\n"
+      "主程序段总共运行了 1971.4076132774353 秒\n"
      ]
     },
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -852,7 +852,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.10"
+   "version": "3.7.7"
   }
  },
  "nbformat": 4,