diff --git a/README.md b/README.md
index cc144f0a85be62316039ae3d6bd2602c9c57c8ec..d20c6b41a3b88c152c5257f05c9ce11eaa6036e8 100644
--- a/README.md
+++ b/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
diff --git a/docs/source/introduction.rst b/docs/source/introduction.rst
index 39aafaa5ae36f30d057c779fb590c752301629fe..1b95441ad18d72886bcbbecf130ab18cc74cda73 100644
--- a/docs/source/introduction.rst
+++ b/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
diff --git a/introduction/PaddleQuantum_Tutorial_CN.ipynb b/introduction/PaddleQuantum_Tutorial_CN.ipynb
index a84cfa2437fd353e744ca9d8e01758f232991d4a..dba1df2559d7e97ed1650e59493f979f36a841fb 100644
--- a/introduction/PaddleQuantum_Tutorial_CN.ipynb
+++ b/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": [
"### 什么是量子逻辑门?\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": [
"### 示例: 如何创建量子神经网络 QNN?\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": {
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\n",
+ "image/png": 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UtHfz0SIiok11+iA+Bzwg6QDgPcAtwFcbTRUREa2rUyDW2zbF60I/a3sukNtOERFbuTrPQdwv6SzgJOAQSeOA7ZuNFRERbatTII6jeO3nKbbvkjQZ+HizsSIiNjdl9iWtffat5x3R2me3pc4opruAT3as3076ICIitnp1RjG9WNJiSX+S9JCkRyT9cSzCRUREe+p0Un8WOB64GXgC8DbggiZDRURE+2pNtWF7JTDe9iO2vwxMbzZWRES0rU4n9QOSdgCul3Q+cCcjm8MpIiIeR+r8oX9jud8sileOTgJe32SoiIhoX51RTLdJegLwDNvnjkGmeBzJsMOIrVedUUyvA64HLi3XD5S0sM7JJU2XtELSSkmzK9oPkXStpPWSju1qO1nSzeXXybV+moiIGDV1bjF9EDgYuBfA9vXA1KEOkjQemAscDuwLHC9p367dbgfeDHy969g9gHOAvys/+xxJu9fIGhERo6ROgXjYdvdzD5u9o7rCwcBK26tsPwQsoJjPaeNJ7Ftt3wg82nXsa4DLba+zfQ9wORk5FRExpuqMYlom6QRgvKRpwOnAz2sctxdwR8f6aoorgjqqjt2reydJM4GZAJMnT6556ojoJf1KsUGdK4h/BvYDHgQuAu4DzmgwU22259kesD0wYcKEtuNERGxV6oxiegB4f/k1HGsohsRuMLHcVvfYl3cde9UwPz8iIrbAoAViqJFKto8c4tyLgWmSplL8wZ9BMStsHZcBH+3omH41cFbNYyMiYhT0uoL4TxT9ABcBvwA0nBPbXi9pFsUf+/HAfNvLJM0BltheKOlFwHeB3YHXSTrX9n6210n6EEWRAZhje93wfrSIiNgSvQrE04HDKCbqOwG4BLjI9rK6J7e9CFjUte3sjuXFFLePqo6dD8yv+1kRETG6Bu2kLifmu9T2ycCLgZXAVeVVQUREbOV6dlJL2hE4guIqYgrwGYpbQhERsZXr1Un9VeB5FLeIzrX9yzFLFRERret1BXESxeyt7wJOlx7roxZg27s1nC0iIlo0aIGwnXc+RERsw1IEIiKiUgpERERUSoGIiIhKKRAREVEpBSIiIiqlQERERKUUiIiIqJQCERERlVIgIiKiUgpERERUGvKVoxERMbQpsy9p7bNvPe+IRs6bK4iIiKiUAhEREZVSICIiolIKREREVEqBiIiISo0WCEnTJa2QtFLS7Ir2HSVdXLb/QtKUcvsUSX+RdH359fkmc0ZExOYaG+YqaTwwFzgMWA0slrTQ9vKO3U4B7rH9bEkzgI8Bx5Vtt9g+sKl8sfXbGocdRoylJq8gDgZW2l5l+yFgAXBU1z5HAReWy98CXqWOl19HRER7miwQewF3dKyvLrdV7mN7PfBH4Cll21RJ10n6iaSXNpgzIiIq9OuT1HcCk23fLemFwPck7Wf7vs6dJM0EZgJMnjy5hZgREVuvJq8g1gCTOtYnltsq95G0HfAk4G7bD9q+G8D2UuAWYJ/uD7A9z/aA7YEJEyY08CNERGy7miwQi4FpkqZK2gGYASzs2mchcHK5fCxwhW1LmlB2ciPpmcA0YFWDWSMioktjt5hsr5c0C7gMGA/Mt71M0hxgie2FwJeAr0laCayjKCIAhwBzJD0MPAq8w/a6prJCRrxERHRrtA/C9iJgUde2szuW/wq8oeK4bwPfbjJbRET0liepIyKiUgpERERUSoGIiIhKKRAREVEpBSIiIiqlQERERKUUiIiIqJQCERERlVIgIiKiUgpERERUSoGIiIhKKRAREVEpBSIiIiqlQERERKUUiIiIqJQCERERlVIgIiKiUgpERERUSoGIiIhKKRAREVEpBSIiIiqlQERERKVGC4Sk6ZJWSFopaXZF+46SLi7bfyFpSkfbWeX2FZJe02TOiIjYXGMFQtJ4YC5wOLAvcLykfbt2OwW4x/azgf8GfKw8dl9gBrAfMB24oDxfRESMkSavIA4GVtpeZfshYAFwVNc+RwEXlsvfAl4lSeX2BbYftP1bYGV5voiIGCNNFoi9gDs61leX2yr3sb0e+CPwlJrHRkREg7ZrO8CWkDQTmFmu/knSipai7An8YaQH62OjmGRzyTYyyTYyyTYybWbbe7CGJgvEGmBSx/rEclvVPqslbQc8Cbi75rHYngfMG8XMIyJpie2BtnNUSbaRSbaRSbaR6ddsTd5iWgxMkzRV0g4Unc4Lu/ZZCJxcLh8LXGHb5fYZ5SinqcA04JoGs0ZERJfGriBsr5c0C7gMGA/Mt71M0hxgie2FwJeAr0laCayjKCKU+30DWA6sB06z/UhTWSMiYnON9kHYXgQs6tp2dsfyX4E3DHLsR4CPNJlvFLV+m6uHZBuZZBuZZBuZvsym4o5ORETEpjLVRkREVEqBiIiISikQERFR6XH9oFzbJO0BYHtd21keLyQ9jY1Pxa+x/R9t5hmKpF1s/6ntHBFtSCf1MEmaDJwPvAq4FxCwG3AFMNv2ra2F60HSTbb3b/HzDwQ+T/Ew5IaHHidS/A5PtX1tO8l6k3S77cl9kONxVVihP4prObfbwXT87oBr3Md/+CQ9x/av284BuYIYiYuBTwEnbng2o5xp9g0UExK+uK1gkv5hsCbg6WOZpcJXgLfb/kXnRkkvBr4MHNBGqDLDuwdrAnYZyyybBRiksEq6lz4urKXlQGvFVdKrgQuAm9n0HyXPlnSq7R+1lW0IP6LF31unFIjh29P2xZ0bykKxQNKHWsq0wcXA/wSq/nW00xhn6bZzd3EAsH21pJ3bCNTho8DHKR7K7NZ2P91X6NPCWubo2+IKfBo4tPuqvpydYRHw3DZClRk+M1gT8OQxjNJTCsTwLZV0AcU05RtmnJ1EMWXIda2lKtwI/JvtX3Y3SDq0hTydfijpEuCrbPp7exNwaWupCtcC37O9tLtB0ttayNOpnwsr9Hdx3Y5iJuhua4DtxzhLt7cA7wEerGg7foyzDCoFYvjeRPGio3PZ9L7mhqlD2nQGcN8gbceMYY7N2D5d0uEU7/ro/L3NLZ+4b9NbKCaJrNL2BGr9XFihv4vrfGCxpAVs+rubQfv/X10M/NL2z7sbJH1w7ONUSyd1RJ8bpLAu7IPCiqS/BdbZXlvR9rS2O9MlPZfq393y9lI9NgLyr7YfaDPHUFIghqmclvwU4Gg2/Y/u+8CXbD/cUrTObMcAf9NP2XqRNM/2zKH3HHv9nC2iaSkQwyTpIoqhmRey8f7mRIo+iD1sH9dStH7PtsdgTcANtieOZZ5NAvR3ticBZ1H8K/hpFAMQfk9R9M+zfW9b2WCTfEcDT6XP8g1G0g9tH952jir9lC19EMP3Qtv7dG1bDVwt6TdtBOrQz9nWArdR/NHdwOX6U1tJtFE/Z/sGxTM2r7B9F4CkpwNvLtte3V40YGO+l3flO5mW80l6wWBNwIFjGGXzAH2crVMKxPCtk/QG4Nu2HwWQNI7iOYh7Wk3W39lWAa+yfXt3g6Q7KvYfS/2cbYrtTV4oWf4hPk/SW1rK1GmwfB+T9NaWMm2wGPgJmxb+DZ48tlE208/ZHpMCMXwzgI8Bc8uHlaD4H/TKsq1N/ZztU8DuwGZ/hCmeTG/Tp+jfbLdJeh9w4YYO3/Kp6jezcWROm/o5368oniG5ubuhDwp/P2d7TPogRmCQkRHft/2r9lIV+jzbc6geUZJsg5C0OzCbItuG213/QTGs+jzbrV4Z9nM+SccCN9leUdF2tO3vjX2qxz6/b7N1avtBlscdSWcCX6e4R/2L8gvgIkmzWwtG32d7H8VUJKJ4v/g15XKy9WD7Httn2n6O7T3Kr+faPpOiY7hV/ZzP9req/gCXdh/TMF36OVunXEEMU9nZu1/3kFFJOwDLbE9rJ1myjVQ/Z+ulXyYSHEw/50u2etIHMXyPUjxjcFvX9meUbW1KtpHp22ySbhysiWLYa6v6OV+ybbkUiOE7A/ixpJvZ2Ak3GXg2MKutUKUzSLaROIP+zfY04DVsPgpNwGbTNLSgn/Ml2xZKgRgm25dK2ofN55hfvGH677Yk28j0czbgB8Autq/vbpB01Zin2Vw/50u2LZQ+iIiIqJRRTBERUSkFIiIiKqVAxDZL0kRJ35d0s6RVkj4raccax1W+Z1nSnA0vZpJ0hqQnDrLfayVdJ+kGScslvb3cfrSkfWt8fq39IrZUCkRskyQJ+A7Fy26mAdOAJ7AFU2vYPtv2/y5XzwA2KxCStgfmAa+zfQBwEHBV2Xw0UOcPf939IrZIOqljmyTpVcA5tg/p2LYbxbMQk4BjgQHbs8q2H1C8zvWq8griixQzld4FzLC9VtJXKEan/A3wb8AK4A+2X9HxGXsAvwb2tv2Xju1/Xx77x/Lr9cArgZnADsBK4I0UM3127wcwF5gAPAD8k+1fj8ovKrZpuYKIbdV+wCavybR9H3ArxfMPvewMLLG9H8WMnOd0neczwO8opuh+RVfbOop5im6TdJGkEyWNK189uRD4F9sH2r4F+I7tF5VXGr8CThlkv3nAP9t+IfBe4IJh/zYiKuQ5iIjhexS4uFz+d4pbVbXZfpuk/YFDKf6gH0Yx+2m350n6MMWMvLsAl3XvIGkX4O+BbxZ3zQAYsh8loo4UiNhWLae4jfSY8hbT0yluDT2PTa+wd+pxrmHfp7V9E3CTpK8Bv6W6QHwFONr2DZLeDLy8Yp9xwL22Dxxuhoih5BZTbKt+DDxR0psAJI0HPgF8tuwbuBU4UNI4SZMonrLeYBwbi8sJwE8rzn8/sGv3Rkm7SHp5x6YD2TgHVPcxuwJ3lh3bJ1adu7wt9tvyRVGocECvHzyirhSI2Ca5GJ1xDHBsOQfT3cCjtj9S7vIzin/ZLwc+A1zbcfifgYMl/ZKiI3lOxUfMAy6VdGXXdgHvk7RC0vXAuWy8elgA/Es5BPZZwL9STNn+M4qObQbZ70TgFEk3AMso3s0QscUyiimCx0YRXQQcY/vaofaP2BakQERERKXcYoqIiEopEBERUSkFIiIiKqVAREREpRSIiIiolAIRERGVUiAiIqLS/we/hA+xePQf4AAAAABJRU5ErkJggg==\n",
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