From 1ab2caaab6afddcebaff9a50ffe6f2bfd34e8f81 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=E4=BB=98=E5=8D=9A?=
 <8260283+Curry-fb@user.noreply.gitee.com>
Date: Wed, 13 Nov 2024 04:39:31 +0000
Subject: [PATCH] homework by curry_fb@163.com
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

Signed-off-by: 付博 <8260283+Curry-fb@user.noreply.gitee.com>
---
 homework/homework1110/homework1.ipynb | 146 +++++++++++++++
 homework/homework1110/homework2.ipynb | 260 ++++++++++++++++++++++++++
 2 files changed, 406 insertions(+)
 create mode 100644 homework/homework1110/homework1.ipynb
 create mode 100644 homework/homework1110/homework2.ipynb

diff --git a/homework/homework1110/homework1.ipynb b/homework/homework1110/homework1.ipynb
new file mode 100644
index 000000000..af2a9bcc7
--- /dev/null
+++ b/homework/homework1110/homework1.ipynb
@@ -0,0 +1,146 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "953ec0bb-05b4-4be3-a5f4-84ad51e87f34",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/ma-user/anaconda3/envs/Mindquantum-0.9.0/lib/python3.9/site-packages/mindquantum/simulator/__init__.py:17: UserWarning: Disable mqvector gpu backend due to: Malloc GPU memory failed: cudaErrorInsufficientDriver, CUDA driver version is insufficient for CUDA runtime version\n",
+      "  from .available_simulator import SUPPORTED_SIMULATOR\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Numpy 是一个功能强大的Python库，主要用于对多维数组执行计算。\n",
+    "# Simulator 是模拟器，可以模拟量子计算机的计算过程。\n",
+    "import numpy as np                          # 导入numpy库并简写为np\n",
+    "from mindquantum.simulator import Simulator # 导入模拟器"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "8fced7ce-a554-424e-a376-76844add2a86",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from mindquantum.core.gates import H, X, RZ\n",
+    "from mindquantum.core.circuit import Circuit\n",
+    "from mindquantum.simulator import Simulator"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "93ec0ede-f04b-47d8-89ef-248034e1cde8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-------------sim----------------\n",
+      "mqvector simulator with 4 qubits (little endian), dtype: mindquantum.complex128.\n",
+      "Current quantum state:\n",
+      "[1.+0.j 0.+0.j 0.+0.j 0.+0.j 0.+0.j 0.+0.j 0.+0.j 0.+0.j 0.+0.j 0.+0.j\n",
+      " 0.+0.j 0.+0.j 0.+0.j 0.+0.j 0.+0.j 0.+0.j]\n",
+      "-------------res----------------\n",
+      "shots: 10000\n",
+      "Keys: q3 q2 q1 q0│0.00   0.017       0.033        0.05       0.066       0.083\n",
+      "─────────────────┼───────────┴───────────┴───────────┴───────────┴───────────┴\n",
+      "             0000│▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒\n",
+      "                 │\n",
+      "             0001│▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓\n",
+      "                 │\n",
+      "             0010│▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒\n",
+      "                 │\n",
+      "             0011│▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒\n",
+      "                 │\n",
+      "             0100│▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒\n",
+      "                 │\n",
+      "             0101│▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒\n",
+      "                 │\n",
+      "             0110│▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒\n",
+      "                 │\n",
+      "             0111│▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒\n",
+      "                 │\n",
+      "             1000│▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒\n",
+      "                 │\n",
+      "             1001│▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒\n",
+      "                 │\n",
+      "             1010│▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒\n",
+      "                 │\n",
+      "             1011│▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒\n",
+      "                 │\n",
+      "             1100│▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒\n",
+      "                 │\n",
+      "             1101│▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒\n",
+      "                 │\n",
+      "             1110│▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒\n",
+      "                 │\n",
+      "             1111│▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒\n",
+      "                 │\n",
+      "{'0000': 656, '0001': 660, '0010': 588, '0011': 596, '0100': 617, '0101': 635, '0110': 640, '0111': 623, '1000': 642, '1001': 604, '1010': 627, '1011': 623, '1100': 646, '1101': 643, '1110': 580, '1111': 620}\n"
+     ]
+    }
+   ],
+   "source": [
+    "# TODO: 请根据图中所示构建量子线路\n",
+    "circ = Circuit()\n",
+    "\n",
+    "# 在这里添加量子门...\n",
+    "for i in range(4):\n",
+    "    circ += H.on(i)\n",
+    "for i in range(4):\n",
+    "    circ += RZ(np.pi / 2).on(i)\n",
+    "for i in range(1,4):\n",
+    "    circ += X.on(i, i-1)\n",
+    "    circ += RZ(np.pi / 2).on(i)\n",
+    "    circ += X.on(i, i-1)\n",
+    "circ.measure_all()\n",
+    "circ.svg()\n",
+    "\n",
+    "\n",
+    "# TODO: 使用模拟器运行线路，打印运行后的量子态\n",
+    "sim = Simulator('mqvector', 4)\n",
+    "# 在这里补充代码...\n",
+    "print(\"-------------sim----------------\")\n",
+    "print(sim)\n",
+    "\n",
+    "\n",
+    "# TODO: 使用模拟器采样线路结果10000次\n",
+    "# sim = Simulator('mqvector', circ.n_qubits)\n",
+    "res = sim.sampling(circ, shots=10000)\n",
+    "print(\"-------------res----------------\")\n",
+    "\n",
+    "print(res)\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Mindquantum-0.9.0",
+   "language": "python",
+   "name": "mindquantum-0.9.0"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/homework/homework1110/homework2.ipynb b/homework/homework1110/homework2.ipynb
new file mode 100644
index 000000000..549968d32
--- /dev/null
+++ b/homework/homework1110/homework2.ipynb
@@ -0,0 +1,260 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "b64efa2c-a091-4778-9f76-ab5084925ab5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/ma-user/anaconda3/envs/Mindquantum-0.9.0/lib/python3.9/site-packages/mindquantum/simulator/__init__.py:17: UserWarning: Disable mqvector gpu backend due to: Malloc GPU memory failed: cudaErrorInsufficientDriver, CUDA driver version is insufficient for CUDA runtime version\n",
+      "  from .available_simulator import SUPPORTED_SIMULATOR\n"
+     ]
+    }
+   ],
+   "source": [
+    "from openfermion.chem import MolecularData\n",
+    "from openfermionpyscf import run_pyscf\n",
+    "from mindquantum.core.operators import Hamiltonian\n",
+    "from mindquantum.algorithm import get_qubit_hamiltonian, HardwareEfficientAnsatz\n",
+    "from mindquantum.core.gates import X, H, RY\n",
+    "from mindquantum.core.circuit import Circuit\n",
+    "from mindquantum.simulator import Simulator\n",
+    "from mindquantum.framework import MQAnsatzOnlyLayer\n",
+    "import mindspore.nn as nn\n",
+    "from matplotlib import pyplot as plt\n",
+    "from mindquantum import *\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "c6fe6ef6-d8b5-443b-91a7-7b76762b8157",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_H2_ham(d):\n",
+    "    \"\"\"\n",
+    "    根据键长生成H2分子哈密顿量\n",
+    "    Args:\n",
+    "        d (float): 键长，单位埃米\n",
+    "    Returns:\n",
+    "        Hamiltonian: H2分子哈密顿量\n",
+    "    \"\"\"\n",
+    "    mol = MolecularData([(\"H\", (0, 0, 0)), (\"H\", (0, 0, d))], \"sto3g\", multiplicity=1)\n",
+    "    mol = run_pyscf(mol, run_fci=1)\n",
+    "    return Hamiltonian(get_qubit_hamiltonian(mol)), mol.fci_energy, mol.hf_energy\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "aca73c0a-88ad-469c-8322-9e97871fff57",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #ff0000; text-decoration-color: #ff0000; font-weight: bold\">                                       Circuit Summary                                       </span>\n",
+       "╭──────────────────────┬────────────────────────────────────────────────────────────────────╮\n",
+       "│<span style=\"font-weight: bold\"> </span><span style=\"color: #3b3b95; text-decoration-color: #3b3b95; font-weight: bold\">Info</span><span style=\"font-weight: bold\">                 </span>│<span style=\"font-weight: bold\"> </span><span style=\"color: #3b3b95; text-decoration-color: #3b3b95; font-weight: bold\">value</span><span style=\"font-weight: bold\">                                                              </span>│\n",
+       "├──────────────────────┼────────────────────────────────────────────────────────────────────┤\n",
+       "│ <span style=\"font-weight: bold\">Number of qubit</span>      │ 4                                                                  │\n",
+       "├──────────────────────┼────────────────────────────────────────────────────────────────────┤\n",
+       "│ <span style=\"font-weight: bold\">Total number of gate</span> │ 13                                                                 │\n",
+       "│ Barrier              │ 2                                                                  │\n",
+       "│ Noise Channel        │ 0                                                                  │\n",
+       "│ Measurement          │ 0                                                                  │\n",
+       "├──────────────────────┼────────────────────────────────────────────────────────────────────┤\n",
+       "│ <span style=\"font-weight: bold\">Parameter gate</span>       │ 8                                                                  │\n",
+       "│ 8 ansatz parameters  │ <span style=\"color: #48c9b0; text-decoration-color: #48c9b0\">d0_n0_0, d0_n1_0, d0_n2_0, d0_n3_0, d1_n0_0, d1_n1_0, d1_n2_0, </span>    │\n",
+       "│                      │ <span style=\"color: #48c9b0; text-decoration-color: #48c9b0\">d1_n3_0</span>                                                            │\n",
+       "╰──────────────────────┴────────────────────────────────────────────────────────────────────╯\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1;38;2;255;0;0m                                       Circuit Summary                                       \u001b[0m\n",
+       "╭──────────────────────┬────────────────────────────────────────────────────────────────────╮\n",
+       "│\u001b[1m \u001b[0m\u001b[1;38;2;59;59;149mInfo\u001b[0m\u001b[1m                \u001b[0m\u001b[1m \u001b[0m│\u001b[1m \u001b[0m\u001b[1;38;2;59;59;149mvalue\u001b[0m\u001b[1m                                                             \u001b[0m\u001b[1m \u001b[0m│\n",
+       "├──────────────────────┼────────────────────────────────────────────────────────────────────┤\n",
+       "│ \u001b[1mNumber of qubit\u001b[0m      │ 4                                                                  │\n",
+       "├──────────────────────┼────────────────────────────────────────────────────────────────────┤\n",
+       "│ \u001b[1mTotal number of gate\u001b[0m │ 13                                                                 │\n",
+       "│ Barrier              │ 2                                                                  │\n",
+       "│ Noise Channel        │ 0                                                                  │\n",
+       "│ Measurement          │ 0                                                                  │\n",
+       "├──────────────────────┼────────────────────────────────────────────────────────────────────┤\n",
+       "│ \u001b[1mParameter gate\u001b[0m       │ 8                                                                  │\n",
+       "│ 8 ansatz parameters  │ \u001b[38;2;72;201;176md0_n0_0, d0_n1_0, d0_n2_0, d0_n3_0, d1_n0_0, d1_n1_0, d1_n2_0, \u001b[0m    │\n",
+       "│                      │ \u001b[38;2;72;201;176md1_n3_0\u001b[0m                                                            │\n",
+       "╰──────────────────────┴────────────────────────────────────────────────────────────────────╯\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/svg+xml": [
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+      ],
+      "text/plain": [
+       "<mindquantum.io.display.circuit_svg_drawer.SVGCircuit at 0x7ff8682e66d0>"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "circ = Circuit()\n",
+    "circ += X.on(0)  # Pauli-X门作用于量子比特2\n",
+    "circ += X.on(1)  # Pauli-X门作用于量子比特3\n",
+    "circ += BarrierGate(False)\n",
+    "depth = 1\n",
+    "for i in range(depth):\n",
+    "    circ += HardwareEfficientAnsatz(4, single_rot_gate_seq=[RY], entangle_gate=X, depth=1).circuit     # 通过HardwareEfficientAnsatz搭建Ansatz\n",
+    "    circ += BarrierGate(False)\n",
+    "circ.summary()                                                                                    # 总结Ansatz\n",
+    "circ.svg()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "76fd85cc-b6ce-4586-a97a-6ca17afe1b5d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Initialize lists to store results\n",
+    "distances = [i/20 for i in range(10, 23)]  # From 0.5 to 1.1 Angstrom with 0.05 step\n",
+    "energies = []\n",
+    "fci_energies = []\n",
+    "hf_energies = []"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "5188a7be-1f80-4494-a59a-7f0a1bf2c7e6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create simulator\n",
+    "sim = Simulator('mqvector', 4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "27666894-490b-4580-89de-1060b9dfafd8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<mindquantum.simulator.utils.GradOpsWrapper object at 0x7ff8682888b0>\n",
+      "<mindquantum.simulator.utils.GradOpsWrapper object at 0x7ff868299cd0>\n",
+      "<mindquantum.simulator.utils.GradOpsWrapper object at 0x7ff86829eb80>\n",
+      "<mindquantum.simulator.utils.GradOpsWrapper object at 0x7ff8682b4f70>\n",
+      "<mindquantum.simulator.utils.GradOpsWrapper object at 0x7ff868240a60>\n",
+      "<mindquantum.simulator.utils.GradOpsWrapper object at 0x7ff868240e80>\n",
+      "<mindquantum.simulator.utils.GradOpsWrapper object at 0x7ff868240f70>\n",
+      "<mindquantum.simulator.utils.GradOpsWrapper object at 0x7ff868240670>\n",
+      "<mindquantum.simulator.utils.GradOpsWrapper object at 0x7ff86824b1f0>\n",
+      "<mindquantum.simulator.utils.GradOpsWrapper object at 0x7ff868241160>\n",
+      "<mindquantum.simulator.utils.GradOpsWrapper object at 0x7ff86824b9d0>\n",
+      "<mindquantum.simulator.utils.GradOpsWrapper object at 0x7ff86825d820>\n",
+      "<mindquantum.simulator.utils.GradOpsWrapper object at 0x7ff86825d580>\n"
+     ]
+    }
+   ],
+   "source": [
+    "for d in distances:\n",
+    "    # Calculate Hamiltonian for current distance\n",
+    "    ham, fci_energy, hf_energy = get_H2_ham(d)\n",
+    "\n",
+    "    fci_energies.append(fci_energy)\n",
+    "    hf_energies.append(hf_energy)\n",
+    "\n",
+    "    # TODO: 获取期望值和梯度算子\n",
+    "    grad_ops = sim.get_expectation_with_grad(ham, circ)\n",
+    "    print(grad_ops)\n",
+    "\n",
+    "    # TODO: 生成待训练的神经网络\n",
+    "    net = MQAnsatzOnlyLayer(grad_ops)\n",
+    "    # TODO: 设置优化器\n",
+    "    opti = nn.Adam(net.trainable_params(), learning_rate=0.05)\n",
+    "    # TODO: 生成能对神经网络进行一步训练的算子\n",
+    "    train_net = nn.TrainOneStepCell(net, opti)\n",
+    "\n",
+    "    # 对网络进行200步训练\n",
+    "    for i in range(200):\n",
+    "        train_net()\n",
+    "\n",
+    "    # 获取第201步训练后的网络的输出，并将其添加到energies列表中\n",
+    "    energies.append(train_net().asnumpy())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "863b914f-e25d-4b79-976f-bac9f4e36aaa",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot results\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "if energies:\n",
+    "    plt.plot(distances, energies, 'b-', label='VQE Energy', linewidth=2)\n",
+    "plt.plot(distances, fci_energies, 'r--', label='FCI Energy', linewidth=2)\n",
+    "plt.plot(distances, hf_energies, 'g-.', label='HF Energy', linewidth=2)\n",
+    "plt.xlabel('Bond Length (Angstrom)', fontsize=12)\n",
+    "plt.ylabel('Energy (Hartree)', fontsize=12)\n",
+    "plt.title('H2 Molecule Energy vs Bond Length', fontsize=14)\n",
+    "plt.grid(True, linestyle='--', alpha=0.7)\n",
+    "plt.legend(fontsize=10)\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Mindquantum-0.9.0",
+   "language": "python",
+   "name": "mindquantum-0.9.0"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
\ No newline at end of file
-- 
Gitee