update to v2.4.0

PaddlePaddle · Apr 24, 2023 · 09959fe · 09959fe
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diff --git a/README.md b/README.md
@@ -33,7 +33,7 @@ English | [简体中文](README_CN.md)
   </a>
   <!-- PyPI -->
   <a href="https://pypi.org/project/paddle-quantum/">
-    <img src="https://img.shields.io/badge/pypi-v2.3.0-orange.svg?style=flat-square&logo=pypi"/>
+    <img src="https://img.shields.io/badge/pypi-v2.4.0-orange.svg?style=flat-square&logo=pypi"/>
   </a>
   <!-- Python -->
   <a href="https://www.python.org/">
@@ -83,7 +83,7 @@ pip install paddle-quantum
 or download all the files and finish the installation locally,
 
 ```bash
-git clone http://github.com/PaddlePaddle/quantum
+git clone https://github.com/PaddlePaddle/quantum
 cd quantum
 pip install -e .
 ```

diff --git a/README_CN.md b/README_CN.md
@@ -34,7 +34,7 @@
   </a>
   <!-- PyPI -->
   <a href="https://pypi.org/project/paddle-quantum/">
-    <img src="https://img.shields.io/badge/pypi-v2.3.0-orange.svg?style=flat-square&logo=pypi"/>
+    <img src="https://img.shields.io/badge/pypi-v2.4.0-orange.svg?style=flat-square&logo=pypi"/>
   </a>
   <!-- Python -->
   <a href="https://www.python.org/">
@@ -84,7 +84,7 @@ pip install paddle-quantum
 用户也可以选择下载全部文件后进行本地安装，
 
 ```bash
-git clone http://github.com/PaddlePaddle/quantum
+git clone https://github.com/PaddlePaddle/quantum
 cd quantum
 pip install -e .
 ```

diff --git a/applications/credit_risk_analysis/config.toml b/applications/credit_risk_analysis/config.toml
@@ -0,0 +1,13 @@
+# The config for credit risk analysis model
+# number of assets.
+num_assets = 4
+# basic default probabilities.
+base_default_prob = [0.15, 0.25, 0.39, 0.58]
+# sensitivity.
+sensitivity = [0.37, 0.21, 0.32, 0.02]
+# loss given default.
+lgd = [5, 1, 3, 4]
+# confidence level.
+confidence_level = 0.99
+# degree of simulation. Higher the degree, preciser the simulation.
+degree_of_simulation = 4
diff --git a/applications/credit_risk_analysis/credit_risk.py b/applications/credit_risk_analysis/credit_risk.py
@@ -0,0 +1,52 @@
+# !/usr/bin/env python3
+# Copyright (c) 2020 Institute for Quantum Computing, Baidu Inc. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import argparse
+import os
+import warnings
+import logging
+import time
+from typing import Dict
+
+warnings.filterwarnings('ignore')
+os.environ['PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION'] = 'python'
+
+import toml
+from paddle_quantum.finance import CreditRiskAnalyzer
+
+
+def main(args):
+    time_start = time.strftime("%Y%m%d-%H:%M:%S", time.localtime())
+    logging.info(f"Job start at {time_start:s}")
+
+    parsed_configs: Dict = toml.load(args.config)
+
+    # input credit portfolio settings
+    num_assets = parsed_configs["num_assets"]
+    base_default_prob = parsed_configs["base_default_prob"]
+    sensitivity = parsed_configs["sensitivity"]
+    lgd = parsed_configs["lgd"]
+    confidence_level = parsed_configs["confidence_level"]
+    degree_of_simulation = parsed_configs["degree_of_simulation"]
+
+    estimator = CreditRiskAnalyzer(num_assets, base_default_prob, sensitivity, lgd, 
+                                   confidence_level, degree_of_simulation)
+    print("The Value at Risk of these assets are", estimator.estimate_var())
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser(description="Credit risk analysis with paddle quantum.")
+    parser.add_argument("--config", type=str, help="Input the config file with toml format.")
+    main(parser.parse_args())
diff --git a/applications/credit_risk_analysis/introduction_cn.ipynb b/applications/credit_risk_analysis/introduction_cn.ipynb
@@ -0,0 +1,214 @@
+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 信贷风险分析\n",
+    "\n",
+    "*Copyright (c) 2023 Institute for Quantum Computing, Baidu Inc. All Rights Reserved.*"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "风险分析的任务是评估给定资产在面临金融系统波动时的风险，即在险价值 Value at Risk（VaR）。该价值可以衡量在金融系统下给定资产的潜在损失。决定资产在险价值的因素有很多，包括金融机构对于风险的不同偏好、金融系统的波动特征和资产本身的含风险性质。2014 年 Rutkowski 与 Tarca [1] 给出了一个计算风险资本的数学模型。该模型认为整体金融系统的波动应该遵循高斯布朗运动，从而能够计算出较高可信度下处于该系统中资产损失上界的最小值。"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "当应用于信贷风险分析问题时，该模型可以用于评估银行的信贷风险监管资本，即银行所持有信贷资产组合在当前金融系统下的在险价值。在该模型中，我们记给定的信贷资产组合为 $\\{ 0, ..., K-1 \\}$, 且决定资产组合总损失的随机变量为\n",
+    "$$\n",
+    "\\mathcal{L} = \\sum_{k=0}^{K - 1} \\lambda_k X_k(Z)\n",
+    "。$$\n",
+    "这里，$\\lambda_k$ 为第 $k$ 个信贷资产的违约损失（loss given default）；$Z$ 为描述金融系统波动过程的潜在随机变量（latent random variable），且其概率分布默认为标准正态分布；$X_k(Z)$ 为描述金融系统下资产违约过程的伯努利随机变量。具体地，当 $Z = z$ 时，随机变量 $X_k(Z)$ 的参数 $p_k(z)$，即第 $k$ 个资产的违约概率，取决于该资产对于系统风险的敏感度（sensitivity）$\\rho_k \\in [0, 1]$ 以及无系统风险下该资产的基础违约概率（base default probability）$p_k^{(0)} \\in [0, 1]$。在标定置信度（confidence level）之后，信贷资产组合的在险价值应定义为『于置信度 $\\alpha$ 下，信贷资产损失上界的最小值』，即\n",
+    "$$\n",
+    "\\textrm{VaR}_\\alpha(\\mathcal{L}) := \\inf \\{ x \\,|\\, \\textrm{Pr}(\\mathcal{L} \\leq x) \\geq \\alpha \\}\n",
+    "。$$\n",
+    "例如，若银行持有的一组信贷资产在置信度 $99\\%$ 下的在险价值正好为 $100$ 万，那么当该资产组合出现违约情况时，由于违约而造成的资产损失超过 $100$ 万的概率不高于 $1\\%$。在经典计算中，信贷风险分析问题中的在险价值可以通过经典蒙特卡罗（classical Monte Carlo）方法和二分搜索（bisection search）方法估算：\n",
+    "1. 根据资产组合性质，选定初始 VaR 猜测值 $\\check{x}$。\n",
+    "2. 使用经典蒙特卡罗算法估算概率值 $p_{\\check{x}} = \\textrm{Pr}(\\mathcal{L} \\leq \\check{x})$。\n",
+    "3. 将概率值 $p_{\\check{x}}$ 与 $\\alpha$ 比较，并根据比较结果和二分搜索方法更新猜测值 $\\check{x}$。\n",
+    "4. 若更新值达到收敛标准，输出 $\\check{x} = \\textrm{VaR}_\\alpha(\\mathcal{L})$；否则返回第 $2$ 步。"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 量子解决方案\n",
+    "\n",
+    "不同于经典算法，量子计算可以使用量子振幅估计（quantum amplitude estimation）算法替换上述第 2 步中使用的经典蒙特卡罗算法，来提高概率值 $p_{\\check{x}}$ 的估算效率。通过量子叠加和纠缠的特性，量子方案与经典方案相比有望在采样次数上获取平方加速的优势 [2]。接下来我们展示如何使用量桨来模拟该量子方案，从而完成信贷风险分析的在险价值计算问题。"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 在线演示"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "我们已经给出了一个设置好的参数，可以直接用于风险资产组合的在险价值计算。只需要在 `config.toml` 这个配置文件中进行对应的配置，然后输入命令 \n",
+    "`python credit_risk.py --config config.toml`\n",
+    "即可对配置好的资产组合进行在险价值计算。\n",
+    "\n",
+    "这里，我们给出一个在线演示的版本，可以在线进行测试。首先定义配置文件的内容："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cra_toml = r\"\"\"\n",
+    "# 用于信贷风险分析模型的整体配置文件。\n",
+    "# 资产数量。\n",
+    "num_assets = 4\n",
+    "# 基础违约概率。\n",
+    "base_default_prob = [0.15, 0.25, 0.39, 0.58]\n",
+    "# 敏感度。\n",
+    "sensitivity = [0.37, 0.21, 0.32, 0.02]\n",
+    "# 违约损失。\n",
+    "lgd = [5, 1, 3, 4]\n",
+    "# 置信度。\n",
+    "confidence_level = 0.99\n",
+    "# 估计精度系数。系数越高，则估计的结果越精确。\n",
+    "degree_of_simulation = 4\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "量桨的金融模块实现了量子解决方案的数值模拟。我们可以从 ``paddle_quantum.finance`` 模块里导入 ``CreditRiskAnalyzer`` 来解决配置好的在险价值计算问题。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------------------------------------\n",
+      "Begin bisection search for VaR with confidence level >= 99.0%.\n",
+      "-----------------------------------------------------------------------------------\n",
+      "Lower guess: level            Middle guess: level            Upper guess: level    \n",
+      "    -1     : 0.000                  6    : 0.691                 13     : 1.000    \n",
+      "     6     : 0.691                  9    : 0.941                 13     : 1.000    \n",
+      "     9     : 0.941                 11    : 0.962                 13     : 1.000    \n",
+      "    11     : 0.962                 12    : 0.990                 13     : 1.000    \n",
+      "-----------------------------------------------------------------------------------\n",
+      "Estimated VaR is 12 with confidence level 99.0%.\n",
+      "-----------------------------------------------------------------------------------\n",
+      "该资产组合的在险价值大约为 12\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "import warnings\n",
+    "warnings.filterwarnings(\"ignore\")\n",
+    "os.environ['PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION'] = 'python'\n",
+    "\n",
+    "import toml\n",
+    "from paddle_quantum.finance import CreditRiskAnalyzer\n",
+    "\n",
+    "config = toml.loads(cra_toml)\n",
+    "num_assets = config[\"num_assets\"]\n",
+    "base_default_prob = config[\"base_default_prob\"]\n",
+    "sensitivity = config[\"sensitivity\"]\n",
+    "lgd = config[\"lgd\"]\n",
+    "confidence_level = config[\"confidence_level\"]\n",
+    "degree_of_simulation = config[\"degree_of_simulation\"]\n",
+    "\n",
+    "estimator = CreditRiskAnalyzer(num_assets, base_default_prob, sensitivity, lgd, confidence_level, degree_of_simulation)\n",
+    "print(\"该资产组合的在险价值大约为\", estimator.estimate_var())"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "___\n",
+    "\n",
+    "# 注意事项\n",
+    "\n",
+    "这里提供的模型只用于特定模型的信贷风险分析问题。\n",
+    "\n",
+    "# 引用信息\n",
+    "\n",
+    "```\n",
+    "@article{rutkowski2015regulatory,\n",
+    "  title={Regulatory capital modeling for credit risk},\n",
+    "  author={Rutkowski, Marek and Tarca, Silvio},\n",
+    "  journal={International Journal of Theoretical and Applied Finance},\n",
+    "  volume={18},\n",
+    "  number={05},\n",
+    "  pages={1550034},\n",
+    "  year={2015},\n",
+    "  publisher={World Scientific}\n",
+    "}\n",
+    "\n",
+    "@article{egger2020credit,\n",
+    "  title={Credit risk analysis using quantum computers},\n",
+    "  author={Egger, Daniel J and Guti{\\'e}rrez, Ricardo Garc{\\'\\i}a and Mestre, Jordi Cahu{\\'e} and Woerner, Stefan},\n",
+    "  journal={IEEE Transactions on Computers},\n",
+    "  volume={70},\n",
+    "  number={12},\n",
+    "  pages={2136--2145},\n",
+    "  year={2020},\n",
+    "  publisher={IEEE}\n",
+    "}\n",
+    "```\n",
+    "\n",
+    "# 参考文献\n",
+    "\n",
+    "[1] Rutkowski, Marek, and Silvio Tarca. \"Regulatory capital modeling for credit risk.\" International Journal of Theoretical and Applied Finance 18.05 (2015): 1550034.\n",
+    "\n",
+    "[2] Egger, Daniel J., et al. \"Credit risk analysis using quantum computers.\" IEEE Transactions on Computers 70.12 (2020): 2136-2145."
+   ]
+  }
+ ],
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+   "display_name": "pq",
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