{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 量子相位估计算法\n",
    "\n",
    "[![下载Notebook](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/r2.5.0/resource/_static/logo_notebook.svg)](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/notebook/r2.5.0/mindquantum/zh_cn/case_library/mindspore_quantum_phase_estimation.ipynb) \n",
    "[![下载样例代码](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/r2.5.0/resource/_static/logo_download_code.svg)](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/notebook/r2.5.0/mindquantum/zh_cn/case_library/mindspore_quantum_phase_estimation.py) \n",
    "[![查看源文件](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/r2.5.0/resource/_static/logo_source.svg)](https://gitee.com/mindspore/docs/blob/r2.5.0/docs/mindquantum/docs/source_zh_cn/case_library/quantum_phase_estimation.ipynb)\n",
    "\n",
    "## 概述\n",
    "\n",
    "量子相位估计算法(Quantum Phase Estimation Algorithm，简称QPE)，是很多量子算法的关键。假设一个幺正算符 $U$，这个幺正算符作用在其本征态 $|u\\rangle$ 上会出现一个相位 $e^{2\\pi i \\varphi}$，现在我们假设 $U$ 算符的本征值未知，也就是 $\\varphi$ 未知，但是 $U$ 算符和本征态 $|u\\rangle$ 已知，相位估计算法的作用就是对这个相位 $\\varphi$ 进行估计。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![quantum phase estimation](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/r2.5.0/docs/mindquantum/docs/source_zh_cn/images/quantum_phase_estimation.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 算法解析\n",
    "\n",
    "量子相位估计算法的实现需要两个寄存器(register)，第一寄存器包含$t$个初始在 $|0\\rangle$ 的量子比特，比特数和最后相位估计的结果的精度和算法的成功概率相关；第二个寄存器初始化在幺正算符 $U$ 的本征态 $|u\\rangle$ 上。相位估计算法主要分为三步：\n",
    "\n",
    "### 步骤一\n",
    "\n",
    "对第一寄存器的所有量子比特进行 [Hadamard](https://www.mindspore.cn/mindquantum/docs/zh-CN/r0.10/core/gates/mindquantum.core.gates.HGate.html) 门操作，对第二寄存器连续进行 `控制U` 门操作，其中 $U$ 门的幂次依次为 $2^0, 2^1,...,2^{t-1}$，控制比特依次为 $q_{t-1}, q_{t-2},..., q_{1}, q_{0}$。这时第一寄存器中的态就会变为\n",
    "\n",
    "$$\n",
    "|\\psi_1\\rangle=\\frac{1}{2^{t/2}}\\left(|0\\rangle+e^{i2\\pi 2^{t-1}\\varphi}|1\\rangle\\right)\\left(|0\\rangle+e^{i2\\pi2^{t-2}\\varphi}|1\\rangle\\right)...\\left(|0\\rangle+e^{i2\\pi 2^{0}\\varphi}|1\\rangle\\right) = \\frac{1}{2^{t/2}}\\sum_{k=0}^{2^t-1}e^{i2\\pi\\varphi k}|k\\rangle\n",
    "$$\n",
    "\n",
    "其中$k$为直积态的十进制表示，比如 $k=0$ 表示第一寄存器中t个比特全部在基态 $|00...00\\rangle$， $k=2$ 表示 $|00...10\\rangle$，以此类推。\n",
    "\n",
    "### 步骤二\n",
    "\n",
    "对第一寄存器的进行量子傅里叶变换的逆变换(Inverse Quantum Fourier Transform)，在线路中表示成 $QFT^\\dagger$, 对 $|\\psi_1\\rangle$ 进行逆量子傅里叶变换可得 $|\\psi_2\\rangle$\n",
    "\n",
    "$$\n",
    "|\\psi_2\\rangle=QFT^\\dagger|\\psi_1\\rangle =\\frac{1}{2^t}\\sum_{x=0}^{2^t-1}a_x|x\\rangle\n",
    "$$\n",
    "\n",
    "其中\n",
    "\n",
    "$$\n",
    "a_x=\\sum_{k=0}^{2^t-1}e^{2\\pi i k(\\varphi-x/2^t)}\n",
    "$$\n",
    "\n",
    "为本征基矢 $|x\\rangle$ ($x=0.1,...,2^t$) 对应的概率幅。由上式可得，当 $2^t\\varphi$ 为整数，且满足 $x=2^t\\varphi$ 时，概率幅取最大值1，此时第一寄存器的末态可以精确反映 $\\varphi$；当 $2^t\\varphi$ 不是整数时，$x$ 为 $\\varphi$ 的估计，且$t$越大，估计精度越高。\n",
    "\n",
    "### 步骤三\n",
    "\n",
    "对第一寄存器的量子比特进行测量，得到第一寄存器的末态 $f=\\sum_{x}^{2^t-1}a_x|x\\rangle$, $x=0,1,...,2^t$，从中找到最大的振幅 $a_{max}$，其对应的本征基矢 $|x\\rangle$ 中的 $x$ 再除以 $2^t$ 即为相位的估计值。\n",
    "\n",
    "## QPE代码实现\n",
    "\n",
    "下面用一个实例来演示如何在MindSpore Quantum实现量子相位估计算法，选择 [T](https://www.mindspore.cn/mindquantum/docs/zh-CN/r0.10/core/gates/mindquantum.core.gates.TGate.html) 门作为进行估计的幺正算符，由定义\n",
    "\n",
    "$$\n",
    "T|1\\rangle=e^{i\\pi/4}|1\\rangle\n",
    "$$\n",
    "\n",
    "可知需要估计的相位角为 $\\varphi=\\frac{1}{8}$。\n",
    "\n",
    "现在假设我们不知道 [T](https://www.mindspore.cn/mindquantum/docs/zh-CN/r0.10/core/gates/mindquantum.core.gates.TGate.html) 门的相位信息，只知道幺正算符 $U$ 是 [T](https://www.mindspore.cn/mindquantum/docs/zh-CN/r0.10/core/gates/mindquantum.core.gates.TGate.html) 门且本征态为 $|1\\rangle$ ，接下来我们需要用量子相位估计算法求出其对应的本征值，即需要估计本征值指数上的相位角。\n",
    "\n",
    "首先导入相关依赖。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from mindquantum.core.gates import T, H, X, Power, BARRIER\n",
    "from mindquantum.core.circuit import Circuit, UN\n",
    "from mindquantum.simulator import Simulator\n",
    "from mindquantum.algorithm.library import qft\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[UN](https://www.mindspore.cn/mindquantum/docs/zh-CN/r0.10/core/circuit/mindquantum.core.circuit.UN.html) 可以指定量子门，目标比特和控制比特，从而在线路中搭建门操作； [Power](https://www.mindspore.cn/mindquantum/docs/zh-CN/r0.10/core/gates/mindquantum.core.gates.Power.html) 可以得到指定量子门的指数形式。因为我们已知 [T](https://www.mindspore.cn/mindquantum/docs/zh-CN/r0.10/core/gates/mindquantum.core.gates.TGate.html) 门的本征态为 $|1\\rangle$，所以第二寄存器只需1个比特，而在第一寄存器中的比特数越多，得到的结果就越准确，在这里我们使用4个比特。\n",
    "\n",
    "因此我们需要搭建5比特线路， $q_0, q_1, q_2, q_3$ 比特用于估计，属于第一寄存器， $q_4$ 属于第二寄存器用于传入 $T$ 算符的本征态。\n",
    "\n",
    "利用 [UN](https://www.mindspore.cn/mindquantum/docs/zh-CN/r0.10/core/circuit/mindquantum.core.circuit.UN.html) 对 $q_0, q_1, q_2, q_3$ 进行 [Hadamard](https://www.mindspore.cn/mindquantum/docs/zh-CN/r0.10/core/gates/mindquantum.core.gates.HGate.html) 门操作，用 [X](https://www.mindspore.cn/mindquantum/docs/zh-CN/r0.10/core/gates/mindquantum.core.gates.XGate.html) 门对 $q_4$ 进行翻转，得到 [T](https://www.mindspore.cn/mindquantum/docs/zh-CN/r0.10/core/gates/mindquantum.core.gates.TGate.html) 门的本征态 $|1\\rangle$。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<svg xmlns=\"http://www.w3.org/2000/svg\" width=\"156.8\" height=\"320.0\" xmlns:xlink=\"http://www.w3.org/1999/xlink\"><rect x=\"0\" y=\"0.0\" width=\"156.8\" height=\"320.0\" fill=\"#ffffff\" /><text x=\"20.0\" y=\"40.0\" font-size=\"16px\" dominant-baseline=\"middle\" text-anchor=\"start\" font-family=\"Arial\" font-weight=\"normal\" fill=\"#252b3a\" >q0: </text><text x=\"20.0\" y=\"100.0\" font-size=\"16px\" dominant-baseline=\"middle\" text-anchor=\"start\" font-family=\"Arial\" font-weight=\"normal\" fill=\"#252b3a\" >q1: </text><text x=\"20.0\" y=\"160.0\" font-size=\"16px\" dominant-baseline=\"middle\" text-anchor=\"start\" font-family=\"Arial\" font-weight=\"normal\" fill=\"#252b3a\" >q2: </text><text x=\"20.0\" y=\"220.0\" font-size=\"16px\" dominant-baseline=\"middle\" text-anchor=\"start\" font-family=\"Arial\" font-weight=\"normal\" fill=\"#252b3a\" >q3: </text><text x=\"20.0\" y=\"280.0\" font-size=\"16px\" dominant-baseline=\"middle\" text-anchor=\"start\" font-family=\"Arial\" font-weight=\"normal\" fill=\"#252b3a\" >q4: </text><line x1=\"48.8\" x2=\"136.8\" y1=\"40.0\" y2=\"40.0\" stroke=\"#adb0b8\" stroke-width=\"1\" /><line x1=\"48.8\" x2=\"136.8\" y1=\"100.0\" y2=\"100.0\" stroke=\"#adb0b8\" stroke-width=\"1\" /><line x1=\"48.8\" x2=\"136.8\" y1=\"160.0\" y2=\"160.0\" stroke=\"#adb0b8\" stroke-width=\"1\" /><line x1=\"48.8\" x2=\"136.8\" y1=\"220.0\" y2=\"220.0\" stroke=\"#adb0b8\" stroke-width=\"1\" /><line x1=\"48.8\" x2=\"136.8\" y1=\"280.0\" y2=\"280.0\" stroke=\"#adb0b8\" stroke-width=\"1\" /><rect x=\"72.8\" y=\"20.0\" width=\"40.0\" height=\"40\" rx=\"4\" ry=\"4\" stroke=\"#ffffff\" stroke-width=\"0\" fill=\"#5e7ce0\" fill-opacity=\"1\" /><text x=\"92.8\" y=\"40.0\" font-size=\"20px\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-family=\"Arial\" font-weight=\"normal\" fill=\"#ffffff\" >H </text><rect x=\"72.8\" y=\"80.0\" width=\"40.0\" height=\"40\" rx=\"4\" ry=\"4\" stroke=\"#ffffff\" stroke-width=\"0\" fill=\"#5e7ce0\" fill-opacity=\"1\" /><text x=\"92.8\" y=\"100.0\" font-size=\"20px\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-family=\"Arial\" font-weight=\"normal\" fill=\"#ffffff\" >H </text><rect x=\"72.8\" y=\"140.0\" width=\"40.0\" height=\"40\" rx=\"4\" ry=\"4\" stroke=\"#ffffff\" stroke-width=\"0\" fill=\"#5e7ce0\" fill-opacity=\"1\" /><text x=\"92.8\" y=\"160.0\" font-size=\"20px\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-family=\"Arial\" font-weight=\"normal\" fill=\"#ffffff\" >H </text><rect x=\"72.8\" y=\"200.0\" width=\"40.0\" height=\"40\" rx=\"4\" ry=\"4\" stroke=\"#ffffff\" stroke-width=\"0\" fill=\"#5e7ce0\" fill-opacity=\"1\" /><text x=\"92.8\" y=\"220.0\" font-size=\"20px\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-family=\"Arial\" font-weight=\"normal\" fill=\"#ffffff\" >H </text><rect x=\"72.8\" y=\"260.0\" width=\"40.0\" height=\"40\" rx=\"4\" ry=\"4\" stroke=\"#ffffff\" stroke-width=\"0\" fill=\"#5e7ce0\" fill-opacity=\"1\" /><text x=\"92.8\" y=\"280.0\" font-size=\"20px\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-family=\"Arial\" font-weight=\"normal\" fill=\"#ffffff\" >X </text></svg>"
      ],
      "text/plain": [
       "<mindquantum.io.display.circuit_svg_drawer.SVGCircuit at 0x7fe694657dc0>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# pylint: disable=W0104\n",
    "n = 4\n",
    "circ = Circuit()\n",
    "circ += UN(H, n) # 对前4个比特作用力H门\n",
    "circ += X.on(n)  # 对q4作用X门\n",
    "circ.svg()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "以 $q_4$ 为目标比特，添加控制$T^{2^i}$门。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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font-size=\"20px\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-family=\"Arial\" font-weight=\"normal\" fill=\"#ffffff\" >T^8 </text></svg>"
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      "text/plain": [
       "<mindquantum.io.display.circuit_svg_drawer.SVGCircuit at 0x7fe69464b370>"
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     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# pylint: disable=W0104\n",
    "for i in range(n):\n",
    "    circ += Power(T, 2**i).on(n, n - i - 1) # 添加T^2^i门，其中q4为目标比特，n-i-1为控制比特\n",
    "circ.svg()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "对第一寄存器中的比特进行逆量子傅里叶变换。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "<mindquantum.io.display.circuit_svg_drawer.SVGCircuit at 0x7fe60baeac70>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# pylint: disable=W0104\n",
    "circ += BARRIER\n",
    "circ += qft(range(n)).hermitian() # 对前4个比特作用量子傅立叶变换的逆变换\n",
    "circ.svg()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "选择后端、传入总比特数创建模拟器，对量子线路进行演化，得到末态。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "<mindquantum.io.display.measure_res_svg_drawer.SVGMeasure at 0x7fe60bab1040>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# pylint: disable=W0104\n",
    "from mindquantum.core.gates import Measure\n",
    "sim = Simulator('mqvector', circ.n_qubits)                      # 创建模拟器\n",
    "sim.apply_circuit(circ)                                         # 用模拟器演化线路\n",
    "qs = sim.get_qs()                                               # 获得演化得到的量子态\n",
    "res = sim.sampling(UN(Measure(), circ.n_qubits - 1), shots=100) # 在寄存器1中加入测量门并对线路进行100次采样，获得统计结果\n",
    "res.svg()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "需要注意的是，测量结果作为二进制串的读取顺序应为$|q_0q_1q_2q_3\\rangle$，因此我们得到寄存器1的测量结果为`0010`，概率幅为1，该末态可以精准地反映相位$\\varphi$。但`0010`是二进制结果，因此我们将它转回十进制后再除以$2^n$，就得到了我们最终的估计值：$\\varphi=\\frac{2}{2^4}=\\frac{1}{8}$。\n",
    "\n",
    "我们也可以通过线路演化得到的量子态 `qs` 找出第一寄存器中振幅最大值 $a_{max}$ 的位置，进而得到其对应的本征基矢 $|x\\rangle$ ，其中的 $x$ 再除以 $2^t$ 即为相位的估计值。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10100\n"
     ]
    }
   ],
   "source": [
    "index = np.argmax(np.abs(qs))\n",
    "print(bin(index)[2:])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "需要注意的是，`qs` 对应的是整个量子线路的末态，因此得到的 ``index`` 也包含第二寄存器中的比特，不能直接得到第一寄存器末态中 $a_{max}$ 对应的 $|x\\rangle$ ，需要将 ``index`` 转成二进制后将 $q4$ 对应的比特位剔除，然后得到的才是第一寄存器的 $|x\\rangle$ 。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0010\n"
     ]
    }
   ],
   "source": [
    "bit_string = bin(index)[2:].zfill(circ.n_qubits)[1:]        # 将index转换成01串并剔除q4\n",
    "bit_string = bit_string[::-1]                               # 将比特串顺序调整为q0q1q2q3\n",
    "print(bit_string)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "再将二进制转回十进制，得到我们最终的估计值。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.125"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# pylint: disable=W0104\n",
    "theta_exp = int(bit_string, 2) / 2**n\n",
    "theta_exp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可见得到的估计相位和 $\\varphi$ 近似相等。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<table border=\"1\">\n",
       "  <tr>\n",
       "    <th>Software</th>\n",
       "    <th>Version</th>\n",
       "  </tr>\n",
       "<tr><td>mindquantum</td><td>0.9.11</td></tr>\n",
       "<tr><td>scipy</td><td>1.10.1</td></tr>\n",
       "<tr><td>numpy</td><td>1.23.5</td></tr>\n",
       "<tr>\n",
       "    <th>System</th>\n",
       "    <th>Info</th>\n",
       "</tr>\n",
       "<tr><td>Python</td><td>3.9.16</td></tr><tr><td>OS</td><td>Linux x86_64</td></tr><tr><td>Memory</td><td>8.3 GB</td></tr><tr><td>CPU Max Thread</td><td>8</td></tr><tr><td>Date</td><td>Sun Dec 31 23:59:31 2023</td></tr>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<mindquantum.utils.show_info.InfoTable at 0x7fe60baa64f0>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from mindquantum.utils.show_info import InfoTable\n",
    "\n",
    "InfoTable('mindquantum', 'scipy', 'numpy')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 参考文献\n",
    "\n",
    "[1] Michael A. Nielsen and Isaac L. Chuang. [Quantum computation and quantum information](www.cambridge.org/9781107002173)"
   ]
  }
 ],
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