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
 

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/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": {