{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "5dce612a-d480-446f-a188-2a66143a6713",
   "metadata": {},
   "source": [
    "# UCC Functions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6ccfc1b-7609-449d-87d2-d003e61e6962",
   "metadata": {},
   "source": [
    "## Overview\n",
    "\n",
    "In this notebook, we will introduce the basic functions of the `UCC` class. We will use `UCCSD` as a typical example. Other classes such as `kUpCCGSD` share the same interface.\n",
    "\n",
    "We will show that, along with nice high-level interface, the `UCC` class also offers great flexibility and allows users to easily build their own algorithms."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67eb6493-0847-4cd2-af67-321ddafa88e5",
   "metadata": {},
   "source": [
    "## Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8a3c0d3e-721a-4e98-a06f-d9b5c994ebe6",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tencirchem import UCCSD\n",
    "from tencirchem.molecule import h2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ecdc7fdb-6243-49e3-9c41-87937e0e4658",
   "metadata": {},
   "source": [
    "TenCirChem provides a set of molecules in the `molecule` module for debugging and fast-prototyping.\n",
    "\n",
    "TenCirChem uses PySCF `Mole` object to handle molecules. In other words, custom molecules can be built with exact the same interface as PySCF."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2043eb93-f371-4832-981f-e30078f856dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pyscf.gto.mole.Mole at 0x7f132bfd3990>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "h2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce3461a5-3022-495a-afe4-3c3cae27c10a",
   "metadata": {},
   "source": [
    "## Hello world\n",
    "\n",
    "This cell illustrates a simple UCC calculation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "157f4dab-9416-4611-ac85-181f8913e048",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "################################ Ansatz ###############################\n",
      " #qubits  #params  #excitations initial condition\n",
      "       4        2             3               RHF\n",
      "############################### Circuit ###############################\n",
      " #qubits  #gates  #CNOT  #multicontrol  depth #FLOP\n",
      "       4      15     10              1      9  2160\n",
      "############################### Energy ################################\n",
      "       energy (Hartree)  error (mH) correlation energy (%)\n",
      "HF            -1.116706   20.568268                 -0.000\n",
      "MP2           -1.129868    7.406850                 63.989\n",
      "CCSD          -1.137275   -0.000165                100.001\n",
      "UCCSD         -1.137274    0.000000                100.000\n",
      "FCI           -1.137274    0.000000                100.000\n",
      "############################# Excitations #############################\n",
      "     excitation configuration     parameter  initial guess\n",
      "0        (3, 2)          1001  1.082849e-16       0.000000\n",
      "1        (1, 0)          0110  1.082849e-16       0.000000\n",
      "2  (1, 3, 2, 0)          1010 -1.129866e-01      -0.072608\n",
      "######################### Optimization Result #########################\n",
      "            e: -1.1372744055294384\n",
      "          fun: array(-1.13727441)\n",
      "     hess_inv: <2x2 LbfgsInvHessProduct with dtype=float64>\n",
      "   init_guess: [0.0, -0.07260814651571333]\n",
      "          jac: array([-9.60813938e-19, -1.11022302e-16])\n",
      "      message: 'CONVERGENCE: NORM_OF_PROJECTED_GRADIENT_<=_PGTOL'\n",
      "         nfev: 6\n",
      "          nit: 4\n",
      "         njev: 6\n",
      "     opt_time: 0.010929584503173828\n",
      " staging_time: 3.337860107421875e-06\n",
      "       status: 0\n",
      "      success: True\n",
      "            x: array([ 1.08284918e-16, -1.12986561e-01])\n"
     ]
    }
   ],
   "source": [
    "uccsd = UCCSD(h2)\n",
    "uccsd.kernel()\n",
    "uccsd.print_summary(include_circuit=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a442afb-91f0-45c3-9e99-a8d658ea30f1",
   "metadata": {},
   "source": [
    "UCC class has a convenient method `print_summary` for a quick glance of the calculation.\n",
    "The summary contains 5 parts:\n",
    "\n",
    "- Ansatz.\n",
    "- Circuit. TenCirChem by default uses a compact circuit for excitations (see the Circuit section below), in which multi-control gates are involved. The circuit part also gives an estimation of the FLOP required for a full statevector simulation of the circuit.\n",
    "- Energy. Includes optimized UCC energy along with HF, MP2, CCSD and FCI energy for reference. \n",
    "- Excitations. Includes optimized UCC parameters and their initial guess. See the documentation for the convention of excitation operators and configurations.\n",
    "- Optimization Results. This is simply a modified SciPy `OptimizeResult` object.\n",
    "\n",
    "`print_summary` by default will not print circuit information since it is sometimes significantly more time-consuming than other parts.\n",
    "\n",
    "One may print different parts of the summary separately using corresponding standalone method such as `print_ansatz`."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01b20f71-155f-48f8-856f-6291c726fc0b",
   "metadata": {},
   "source": [
    "## The Circuit\n",
    "\n",
    "TenCirChem uses TensorCircuit `circuit` object for quantum circuit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "44c78fae-fe0a-4bb7-8443-e177af236a63",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tensorcircuit.circuit.Circuit at 0x7f131c17fe10>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "circuit = uccsd.get_circuit()\n",
    "circuit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1859849e-d495-45c1-80ea-c4b8b50f628b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1123.61x367.889 with 1 Axes>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "circuit.draw(output=\"mpl\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aeb43d9d-053a-4981-a667-9f770cba670b",
   "metadata": {},
   "source": [
    "TenCirChem uses the compact circuit for excitations described in [https://arxiv.org/pdf/2005.14475.pdf](https://arxiv.org/pdf/2005.14475.pdf).\n",
    "To decompose the multicontroled $R_y$ gate into elementary gates, use the `decompose_multicontrol` argument."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9f9397d7-c0f9-435c-9b83-dd1b465a7478",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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RiAqx1HfiXRp4/vSwzyuvkrr37CN/fWwznpG2eY1xsh1sTDy1jcmkYMilGnPjb8I+r94vjRh3vg6WOL9eBX2B5hl62c/U68xbwj5vU3G1Cgr62B/QadAO2m/SD95Vy/x+YZ/311c/0L9fcnMUIgof7UDkUjJydfmD6yM69877HtKGD5+yOaLTs6sdiLc2QHKmXuT3m6Rxt7wQ9nmBoDRu0hXav325/UGFiTageQZd+F/qc+6/h31e8f6gCgq7xvwJQPoC9pvwH6+pTdfhYZ/3yptLdfflV0chovDRDkTO403RVQ9tj+jc+/7f41r79sP2BoQmBYNBlZaWSpKGDh2qVatWRXSdhE10VVebQbqamsY/WLNnz1Z5eblyc3PVvXv3o79ft26drrnm5AHyAQMGaN26yHYmLSsr0+7dETxKhIi07R/5JjP7Dx5WeRy8VxmpWU6HELbSkhLVNoS48HGUDfZH1iH1pGQkxGc1pU3ka+4cqqxOiH+DZNe1Lsx1eo6zd3+Fag6HuKmNTdzY5oUqntrGZNKiZ+Q3VgcOVqo0DtpBN34u4qm+92uIbL0Rb6ozfQE3vt+hcqxeeFMjOs0f9MRNX8iN9SJe2oGslh0jPvdInZ92wGZO1IusgsjLO3ioKi7aATfWiXhpAySpV7hrl37F4/WqbM8+BRpiu4GxG9/vUDlVL4LyRXxePLQBkjvrRby0A6kZjW9lFIoah/oCkPbsiWBplq8kbKKrY8eOqqio0MqVKzV27NgTXistLdW0adMkSYMHD5bH4zn6WkVFhVq2bHnS9Vq3bq0NGyLbwKZjx8g72QhfVrrn9Ad9g2VZ8ng8apmbrvTOnaMQVXjSUjKdDiFs+Z06xcUTG5KUovqIzvPXHlbnOHj/mysvK7KBHUnKzUxJiH+DZJfui3xB6bYtcxTMTbMxmtNzY5sXqnhqG5NJdnr4q3N/3RdokZMubxy0g278XMRTfU/1RLYGZUONM30BN77foXKqXgTrI3vwxxOsjZu+kBvrRby0A96UjIjPzUgJ0g7YzIl6kZMZ2QC3JOVlp8VFO+DGOhEvbYAkpXkjm60SaKhVx/ZtbY7m9Nz4fofKqXph+SNMtgRq4qINkNxZL+KmHfB4FAw0yOsLf4ws3edMXyBZHT+jq0OHDhFfx2M1Z+HDOHbXXXfpiSeeUGFhod577z317t1bkrR8+XLdfPPN2rp1qxoaGnT77bfrySefPHpeWlqapk+frp/97GcnXO+WW27RkiVLIk52IXYqa6UHXg1/48BOraRpF0qe8PNktgvUSx887nQU4Zlwl+SL7dh4k9YWS7/7KPzzzuotXTXS/nhiLRCUHpxj1hUOR1aa9MAVUlrCPgKRPPYckh6aG/55ffOlf5tofzyn48Y2L1Tx1DYmk5p66f5XpPowxzfa5ko/uUTy0heISDzV9017pKfeC/+8UT2kG8ee/ji7ufH9DpVT9eLtNdKbn4d/3vVjpDE97Y8nEm6sF/HUDvz2fWlDBDsI/OQSqX2e/fGcjhvf71A5US/q/dL9r5o+QThaZEr/c7nki4Md7d1YJ+KpDdi5X/rVW+GfN7SLdMtZ9sdzOm58v0PlVL34YL302srwz7tyhHR27FeybpQb60U8tQPPfSStKQ7/vB9dKBW0tj8eNK66ulo5OWYGXlVVlbKzsyO6Thx8dUfH9OnT1aZNG+3atUsDBgzQoEGDVFRUpFGjRqlHjx6aONGM5B2/P5cktWrVSgcPHjzpegcOHFDr1tRwN8jNMB2TcI0vio8kF5qvXyepVQRt4rje9sfiBJ9XGtsr/PNG9yTJlSg6tJCKIngIZnyCfAaAzDRpePfTH/dN44riI8mF5uvVXuoQwUA17WDiGNMr/M9zZpo0rPEtnuFCkfTt+3R0JskF+6WlmIcXwnVmUXwkudB8XdpIhREM4yXKuABMG5AS5uTONJ80MoL7CMSnSPr23dqS5HKrhP36Ligo0IIFCzRlyhRlZGRo+/btat26tZ555hnNmzdPGzdulHRyoqtfv36N7sW1bt069esX/mbGcMa3+kupYXyZtc2VhneLWjiIMa9XOn9QeOcM7SJ1bBGdeJwwrkjKCWPFlqy0+HliCfY4b2B4A3yFraX+naIXDxBr5/aT0sNI3rfKkkZHMCCG+OTxhN8X6N/JDIohMbTINP2hcHyrPw/9JJIBncMbqPJ6pMkDoxcPYu/sPiaBHaq8DOnMCB4YRPy6IMy+QM/25mEZJIbsdOmcMMc5zu0XXruB+FbUUereLvTjPTJjKXCnhE10SSZpNXfuXFVWVqqyslLLli3Tbbfdpurqam3fvl1er1cDB55Yey+++GItXLhQxcXH5jUuW7ZMW7Zs0SWXXBLrvwIi1KmV9J3xUkoINbxllvT9c6X0yLc1Qhwa0zP0L6ee7Z1Zpiia8jJNvc4KoYOWkSp975zIZsEhfhV1lK4bbTpqp9M+T/rXc02SGEgUHfKkfzk7tAdfcjOk2yZIWenRjwuxM6ybdPHQ0I7t2kb69vhoRgMnXD5cGlgQ2rFn9jKJLiQOn9f0b0KZoeWRWbayV+TbQiAOtcmR/vWc0B58yUqT/nWClOu+7XBwCgMKpKtGhHZs51am78hKP4llypDQZ2uP6C5dMDi68SC2vB7p1rOl/JanP9Yjs51Jf7bmcq2kHNJau3atLMtSUVGRsrKyTnjttttuU35+vi677DLNnTtXL730km644QaNGjVKl112mUMRIxIDC6TbJ0k9msjcez1mFs8PzpfasTxFQrpoiElgtc1t/PWMVGlCP+nfJybm07uFbaS7zzdPqDfVV++bL901WerBU2sJaXRPM8BT0Krx11N8ZjmHH5xnkqNAoumTL905uemlPD0eaVCB6QuEcvMD95k0QPr2uKYHutNTpLP6mD5jBg89JRyfV/qXs8wT/dlNJLJbZklXDJeuGcXgZiJqkSndfd5Xy1c1MfpR0No87BDJMneIfz3amzrQJ7/x1z0ys/9+cH5ky9wh/p3VxySw8ptYwSXNZx52uHNy098VcC+vV7ppnHn4KbeJVW/yMqVLzzDjRyxjnnhyMsy419heTT8E2amldOs5LGPudgk4tHt6a9askXTysoWSlJeXp/nz5+vuu+/W9ddfr5SUFF188cV69NFH5eVRd9fp3k666zyppEL6dLv08QapIWAGNX5yidQi67SXgMuN6mGeytlYJq3dLS3dbOpAZqr0wJXhLWvlRh3yzI37/ipp+Vbp/XXHPgM/upAkbzLo39nsW7djv/TZTmnhRlMHMlKln17GzRwSX5c2JolRdkj6dJv04ZfH2sEfX8xs1mQwrJt0Rldp0x7pi2JpyeZj7eADV5DgSnRer3k6e9IAafVO6e/LpPqAGdi8aZwZ4GY/nsSWnW4GLy8bJn2yVfrnZ6YNSEuRbv+W+Z4gyZnYOrUyDzfuPSyt2CZ9sP5YX2D6FDPzC4ltcKF5uGnrPunzXdLiTcf6Av9zeWgrocC9vB7TDzi3r7SmWJq15Fhf4Max0qBC+gKJLjPNrHhzyVBp+TZp3upjdeDfJprxY/oC7pfgQ7yNO1WiS5J69uypuXPnxjIkRFmnVuZnxTbpUI3pzCRjkqt43yY9Mvs7OlRdruyMFpp23Qvq1nGA02FFnddjZi71zZc+32nqQFpK4ie5jtcmxwzyLNl87DNAkit5eDxmQ9VubaWV200dSE8hyYXk0rGFNGWoGeT8uh10Y5Ir1O/yNz95Tn/74BeygkEN7TVRd135G6X4UrVq83w9988fq6auSh6PR6P7TtGtF/3i6ANdf/tght5d8Uel+NKUlpqh2y97XH27jFL5oRLN/Pt3tadiu1J96erctkh3X/W0WuaEsei9gzweqXdH8/PZzmPtIEmu5JHiMw8/vbFKqq8xAx6DC52OKny0AZHLTjerOXy43rQBmalS17ZOR4VYap9nVv1YtuVYX8CNSS7agch4PGbbgp7tpdU7jvUFSHIljxSfefhpzqfH+gJDQ1zWMJ7QBkQuK106p680f92xOsAKR4kjiYZ5jzldogtIVI+9/H1dNPo2nT/yFn38+Ut6ZPYteuru5U6HBQARC7WTf6rj6htq9X9/vV479qxTemqmWua0111X/lad25rdyJet/6deePu/FQwGFQz6dc2503TeiO+c9jwgGkL5Li89sE0vvP1T/fbulWqV20H/88Jlmrf0WV027nblZrbSf039m/Lb9FB9Q62mPztJ7376J50/8hZt3r1abyz+jX7/o7XKTM/Re5/+RU/OuUNP3vWJfF6fbpr0Uw3sbjayenbuND07d5qmX/+CA/8K+BptYPKhDcA30Q4kH9oBfBPtQHKhDQAal5QTM+fPny/LsjRlyhSnQwFipqJqrzYWr9CkYTdJks4adJX2Hdyl3eWbHY4MACL3dSf/hfs26roJ9+mR2bdEdNxFo2/TH6Zv0DP3fKaxAy7Tr/7xPUmSZVmaMesmTbv2BT1zz2r977/M1a9f/r6O1Fae8jwgGkL9Ll/w+Usa2/9Stc7rKI/Ho4vH/Js+WD1LktSr8xnKb2M2oklLzVDPTkO1p2K7JMnj8cgfbFBtfbUkqar2oNq2KJAktcrtcPSmVpL6dhl99Dw4hzYwudAGoDG0A8mFdgCNoR1IHrQBQNOSMtEFJKN9B3epdV6+fD4zkdPj8ah9qy7ae3Cnw5EBQGRC7eSf7ri01AyN7neRPF8tyt2vy5gTO+wej6pqD0qSjtQeVl5WG6WmpJ/+PMBmoX6X7z24Ux1aHVuHpWPrbo1+3x84XKYFn7+k0f0uliT17DREV531Q938UHfd8LMCvfLxo7rj8idOOi8QDOi1RU/qzAGX2fnXQ5hoA5MPbQC+iXYg+dAO4JtoB5ILbQDQtKRcuhAAALjfqTr5xy+VEepxX3t14WMa+1WH3ePx6L+nztaDf7xSGWnZqqqp0P3ffkWpKScv5n/8eUC8q649rJ/+4RJde+509SkcIckscbJwzSt64b7Natu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      "text/plain": [
       "<Figure size 2210.55x785.944 with 1 Axes>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uccsd.get_circuit(decompose_multicontrol=True).draw(output=\"mpl\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c4b3d78-411a-4ec1-b02c-d054bb660fe4",
   "metadata": {},
   "source": [
    "For traditional Trotterized circuit, use the `trotter` argument."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b9ff5f8b-bcc1-455b-afe3-a2195f5a0097",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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+odC/X/+g9+ANhYzMwcqYfbNG5d+kl/9ztsoKVql816caMDK/y+cO5nsQrOsP5fWezCmfgVAJ9vUD6FhzXaUSU7zPJR3qWKCtpVG9e2bIE2irgB+cFg9aFQsmpnQ8U0hnP38pCJ+BpqOWfB9w2nNQck4sEG3xoJWIhwDAGh7b20cV9Hgo1LGvN6FoH/XmdHGyx+ORx2NGoPXr1y/gMiI20TVv3jxt375djzzyiM477zzl5uZKktauXaubbrpJFRVmsdGJEyd6Pf6Pf/yjjhw5oj179uixxx7T+eefr1WrVmnQIN9/sC6XS6mpqQEPtwO8+WCr9Pom79s6ml7mmPuuND0Vqhul+17zv+z4WOmzT99TrAWzcLY1Sx8+Gbrznzf5Jp03+aagnrOwoFCxXe8E2annlklbO+g0EurPwJgh3fTMl+sbhlJTq3TPqc/XsFZQWKjEID5VQ/0euFwuZQ2frrKCVaqrCk4vpGC+B8G+/lBc78mc9hkItmBfP4CO/XmltHGv922hjgWGZiWpeF+R/wcGwGnxoFWxoCTd+6r3tXs7+/lLXf8MXHfJLL34H8SD3jgtFoi2eNAKxEMAYI33t0pLNnnfZkX76Ja1S4PePhrq2NebULSPemNFnByxi0YtXLhQvXr1UnFxscaMGaNx48YpJydH06ZN07Bhw3TOOedI6nh9rpEjR2r69Om6/vrr9cEHH6impkaPPvqolZcAeDWwl31l9+8uS5JcOL3sjmerieiyEZi9W96Tu631lP9vbW7Qvi3vSpJ6DsizulohE23XCyA6DbTxeWxn2TiOeBD+ID4CAEQaO2PSAT1oHw1HEdvPJDs7WytWrNDdd9+tjz76SEVFRcrLy9Ozzz6r2267TcOHD5fUcaKrve7du2vEiBHauXNnqKsNdGpwLykhTmo+9XtKyOV6ny0PFsvNkt7ZYl/ZcJYVf/2hGmoqNWzSZcocOE5xiSmqqSzWjo9f1JHyAo3Ov1mZA8fZXc2gibbrBRCdcmx8HhMLhIfcrI5H+IdSjEsa0cf6ctE1xEcAgEgzOFNKiJWa26wvm3g4PEVsokuSRo8erSVLlpzy/7W1tSoqKlJMTIzGjh3b6XkOHjyoHTt2aPr06aGoJuCXxHhpylDp40Jry3W5pJkjrC0T3g3rLWV1k8qPWlturzRpZOBT5cIms+c/od3r/6nSgpXauXaxmuqPKDGlmzIHjteUS+9R3uxb7K5iUEXb9QKITgN7SoN6SfsqrS23W7I0NtvaMuHd1KFmup4Wixt3xmZL3axfJx1dRHwEAIg0SfHS5KHSJxaPS6F9NHxFdKKrI1u3bpXH41Fubq5SUk6M0m+88UaNGDFCEydOVPfu3VVYWKj/+q//UlxcnH74wx/aVGPgRPk51ie6xgyQeqZZWya8c7mk/FzplbXWlntmjunF6yTZeXN01188p92ns+1ON3jc+Ro87ny7q2GZaLteX/B7AESm/FzpxU+sLXPmCKZpCRcpidLkIdLqXdaWm59rbXnBwHOQ+IjPAABEpvxc6xNdYwdIPVKtLRO+icqvKVu2mDm/vE1bOGPGDL355pv61re+pYsuukiPPfaYZs+erU2bNmnECNK1CA/9e0gTB1lXXoxLOr/zwY+w0NRhZoSVVbol02MFAIBwMmmwGeFtlbREZyY5Itm8MWYxdKsM7yPl9LWuPAAAgNMZ0EOaMNC68mJc0vnM9Bu2onJE1+kSXXfeeafuvPNOq6sE+O3qqVLhAamuKfRlnZtnpsexW8mhQj226Js6Wleh1KRuuvu6FzQka8wp+7316R/1tw8flsft1sQR52jBVc8oLjZeG3cu1R/f/Kkammrlcrk0fdTF+s7XHlZMjMn5/+3DR/Teuj8rLjZBCfFJ+v7lT2rUoGmqOFqqx//+LR2oKlJ8bKIGZOborqv/R93Telv9FnwlMU66YYb09PvWlHfddCk5wZqyAABA5+JiTSzwm3cljwUDEa6dJqUlhb6czhAPHpeZLl0yUXptfejLSvgy9nQ5bHQ/AACIbNdMlXYetKZ9dN4YM4U4whMjugCHSk+Svj7Nv2OqG6Qj9eZPX/XrLl0QJr0Vfrv4Dn1t+u164Z4CXTf3Hj226JZT9ik7vEcvvPNL/df3VujPP92pqtoDemP17yVJ6ck99O/z/6Y/3r1Nz9y1Xlv3fqz31v+vJGnn/k16/eNn9PSCT/Xsjzbp8ll36ul/mKR3bEysbpz3Sz2/cId+/+PP1K/XMP1+yd2WXXdHRvSVzhrp3zGBfAZmDJfyBvhXDgAACL3BmdK8PP+OCSQWmDRYmmDhbAKnQzx4otkjzUgrfwTyGbjsDJNYAwAACCfpydK1U/07JpBYqH+P8J3tquRQoe56epZueSRX3//tVBWVb/V7v+aWRt37whW65ZFc3fHEBN3z+/O0v+L4vJBrtr+p7/1mku54YqJue3ys3l33Z5+Os1JUjuhaunSp3VUAgmLCIOnSidLrm3zb/4m3/Tt/j1Tpjrmmx7DdqmoPqqBknR6+7V1J0uxxV+vp1+7U/oqdGpB5fE69FZ+9opl5l6lnRpYk6ZIZ39VLSx/U5Wd+XyMGnPHVfgnxSRref6IOVBVJklwul1rdLWpsrlNyYppqG48os5tZbb1Hel/1SD8+T8uoQdP1z1VPh/qSfXL5JKmqTtpS4tv+/n4GRvUzvWMAAEB4umiCVFkrbdjr2/7+xgLD+0jXz/C/XqFAPHiqGJf07bOkp9+Tyo76doy/n4G5o81arQAAAOFo4mDpklppySbf9vc3FuqZKt0+JzzaR7051hHsgqm3aPlnr+ixRbfod3et9Xu/r02/XdNGXSSXy6V/rHpaT7x8q379vWXyeDx65KUb9fh3l2lY//EqP1ykbz82Svljr1JcbHyHx1ktKkd0AZHk3DEm2RFsvdOlBedJ3VOCf+5AHDpSrJ4Z/RQba/LzLpdLfXoM0sEj+07Y7+CRferbY/BX/87qOeSUfSTpcHW5Vnz2iqaPvkSSNLz/BF09+4e66aGhuuGBbL26/L905xVPnXJcm7tN/1z1tGaNuTyYlxew2Bjpm/mhWbNtzADTcBKuD3IAAGASHfNnSVOHBv/cuVnmS31CmHSPJB70LjVR+v48KTsEU+mcm2dGczFlIQAACGfzxpjBAMHWJ0P6QRi1j57sWEeweZNulGQ6gh06UnzKqKrO9kuIT9L00V+T68ugb/SgGV91BpMkuVyqbTwiSapvrFZGSi/FxyV2fpyFwuQrC4CumDvaLEa+aI0ZettV04ZJV0ySUhK7fq5wVNdYrV8+f6m+PmehRg6cIslMcbNyy6t64Z6dyuzWX/9Y9bQe+Mt1+s33V351nMfj0VOv/pvSk3voyvy77Kr+KeJipZvzpaE7TO+VlrYuni9Gumi8NGe0SaQBAIDwFhsjfWOmNKS39K8NUlNr1853bKHt88ZEbiwQafFgWpLppLZkk7Rih9TVZdtSE800QBMHd74vAABAODh3jFmCZdEa6agf0xJ2ZPpwM7ggJYzXrD9dR7D2Mx74ut8xr638rWZ+2anL5XLpF/MX6f4/X6WkhFTVNlTp3ptfVXzcqW9M++OsRqILiBCj+0v3XCz9a6O0ZpfkDuDbba806aopZiRPuOndfaAOV5epra1VsbFx8ng8Oli1T326nziUqU/3QSqt3PXVv8sPF52wT31jjX7+hws1a8zluubsH331/ys/W6yh/cYps1t/SdIFU7+l3/3jB2ppbf7qxv27fy7QwaPFuv+b//hqwfJwEeOSzh4l5fWXXl4rFZQHdp7hfcxi81ndgls/AAAQWi6XmV5uVD9p8VppW2lg5xmcadaBHdAjuPULBuLB00uIM7H8hIEmHiz3cSrD9lwu6YxB0pVTzJrAAAAATpI3QPrpJV1rH838sn00HNarX/DUTO2vKPS67b9/uDEkZb74wYMqrdipR+/4QJLU1taqv37wgO795qsaP+ws7Sheq/94/jL9/sdb1C01s8PjrEaiC4ggyQnSddPNaJzVO6WPd3Y+wsvlMsmRM3OkUf1NwiQc9UjroxEDJun9DX/RBVNv0Yoti5XZPfuUXgezx12t//dMvm4+7z71SO+rJav/R3MmXi9Jamiq1c/+cKGmjLxQ8+f94oTjsnoN0zvrnldDU62SE9O0ZtsSZffOPd6o8Y8FKq3Yqftu+YfXHgvhoneG9G/nSmVHpFUF0to9nffqToiTJg8xn4FQTHkDAACs0ytNun2udOCotKrQxAINzac/Jj5WOmOwlJ8rDeplTT0DQTzom+F9TQe4nQdNPLilRGpzn/6Y9CRpxnBp5gipZ5o19QQAAAiFY+2jF37ZPvqJD+2jMS4ziCA/VxrZL3zaR5/8wSen3R4fl+hTRzBfO4y9vOxxrfz8VT16+/tKSjDzNe4s3aTK6lKNH3aWJGnkwKnK7Jatnfs3anLueR0eZzUSXUAEykj+crqZseZGXnxYKjlsGjna3FJ8nOmdkN3T9NYNl3UXOvP/rn5Wjy26RS8tfVApSRm6++vPS5J+/fKtmpl3mWaNuUz9eg3TN8+/X//vd2dKkiYMn6NLZtwhSXp15W+1o/hTNTbXaeWWVyVJZ024VvPP/Xflj71SBcVr9f3fTlF8XKKSElL1s2+8KEn6fM8q/WPVUxrYZ5R+8OR0SVK/nkN13y2vWf0W+Kxfd+maaaYHyoFq8xkoPyI1t5qpbBLizKitgT2lvt0id1oiAACiVd9uJg64YpJ0qMbEAmVHpKaW47FA3y9jgSwHxQLEg75xuaScvubV0mZ+9sWV0sEa8+8Yl5QcL/XvYT4DvdJYhwsAAESWbsnSBeOk88dKVXUmHt5f5fz20fZ87Qjmy36vfPSEPtz0kh65/X2lJXf/6v/7dB+owzVl2ntguwb3Ha39FTtVVrlLA3uPPO1xVnN5PJ6uTt8NAEHX1ix9+KTdtfDP3AVSbPh27nWUplbpnkV218I/j1wnJQYxKIr29yDar19y3nsQ7OsHAKf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\n",
      "text/plain": [
       "<Figure size 2210.55x1622.06 with 1 Axes>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uccsd.get_circuit(trotter=True).draw(output=\"mpl\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "713ced03-6ef7-42a1-9b16-d26b04cd3dbd",
   "metadata": {},
   "source": [
    "## Useful Attributes\n",
    "TenCirChem chooses to expose its internal data whenever possible and in the simplest way.\n",
    "The intention is to make TenCirChem easy to hack and useful as a handy toolbox."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b7db036d-ae52-4538-9971-ca9fee291e60",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4, 2)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uccsd.n_qubits, uccsd.n_elec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8728730d-c9d9-4d17-a704-4827abc464c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<pyscf.gto.mole.Mole at 0x7f13232ed950>,\n",
       " <pyscf.scf.hf_symm.SymAdaptedRHF at 0x7f132bafc850>)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# PySCF objects\n",
    "uccsd.mol, uccsd.hf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "773edc08-f2f8-4bd5-8f60-e686f7213e33",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-1.116706137236105,\n",
       " -1.1298675557838804,\n",
       " -1.1372745709766439,\n",
       " -1.1372744055294384,\n",
       " -1.1372744055294384,\n",
       " 0.7141392859919029)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uccsd.e_hf, uccsd.e_mp2, uccsd.e_ccsd, uccsd.e_ucc, uccsd.e_fci, uccsd.e_nuc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "cb778365-b0cb-4e84-aaa8-a19781550ec3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((2, 2), (2, 2, 2, 2))"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# one and two electron integral in molecular orbital basis\n",
    "uccsd.int1e.shape, uccsd.int2e.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "2e0e3065-66f5-4d0e-82d1-6b3b28539352",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7141392859919029 [] +\n",
       "-1.2527052599711868 [0^ 0] +\n",
       "-0.48227117798977825 [0^ 1^ 0 1] +\n",
       "-0.6745650967143663 [0^ 2^ 0 2] +\n",
       "-0.18126641677772592 [0^ 2^ 1 3] +\n",
       "-0.6635375947675042 [0^ 3^ 0 3] +\n",
       "-0.18126641677772592 [0^ 3^ 1 2] +\n",
       "-0.47569770336145906 [1^ 1] +\n",
       "-0.18126641677772592 [1^ 2^ 0 3] +\n",
       "-0.6635375947675038 [1^ 2^ 1 2] +\n",
       "-0.18126641677772592 [1^ 3^ 0 2] +\n",
       "-0.6974673850129379 [1^ 3^ 1 3] +\n",
       "-1.2527052599711868 [2^ 2] +\n",
       "-0.48227117798977825 [2^ 3^ 2 3] +\n",
       "-0.47569770336145906 [3^ 3]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Hamiltonian as openfermion FermionOperator\n",
    "uccsd.h_fermion_op"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "5ab0106b-b259-4fb4-85e6-33eae4ace027",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.09835117053027564+0j) [] +\n",
       "(0.04531660419443148+0j) [X0 X1 X2 X3] +\n",
       "(0.04531660419443148+0j) [X0 X1 Y2 Y3] +\n",
       "(0.04531660419443148+0j) [Y0 Y1 X2 X3] +\n",
       "(0.04531660419443148+0j) [Y0 Y1 Y2 Y3] +\n",
       "(-0.22297018776182556+0j) [Z0] +\n",
       "(0.12056779449744456+0j) [Z0 Z1] +\n",
       "(0.17436684625323448+0j) [Z0 Z2] +\n",
       "(0.16588439869187604+0j) [Z0 Z3] +\n",
       "(0.1712591626176813+0j) [Z1] +\n",
       "(0.16588439869187596+0j) [Z1 Z2] +\n",
       "(0.16864127417859157+0j) [Z1 Z3] +\n",
       "(-0.22297018776182548+0j) [Z2] +\n",
       "(0.12056779449744456+0j) [Z2 Z3] +\n",
       "(0.1712591626176812+0j) [Z3]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Hamiltonian as openfermion QubitOperator\n",
    "uccsd.h_qubit_op"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6c3556dc-a36b-4d37-b3b4-5299508a2ff2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(None, array([[[[-0.07260815]]]]))"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# t1, t2 amplitude based on MP2 or CCSD (determined by the `init_method` argument)\n",
    "uccsd.t1, uccsd.t2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "9b7cab6f-60fb-41ff-b73d-1214129a4b53",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([(3, 2), (1, 0), (1, 3, 2, 0)], [0, 0, 1], [0.0, -0.07260814651571333])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# `param_ids` maps excitation operators to parameters.\n",
    "# Some excitation operators share the same parameter due to symmetry\n",
    "# refer to the documentation for the convention of excitation operators\n",
    "uccsd.ex_ops, uccsd.param_ids, uccsd.init_guess"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "cdca2558-d429-4760-9138-ffa02f99ee73",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "            e: -1.1372744055294384\n",
       "          fun: array(-1.13727441)\n",
       "     hess_inv: <2x2 LbfgsInvHessProduct with dtype=float64>\n",
       "   init_guess: [0.0, -0.07260814651571333]\n",
       "          jac: array([-9.60813938e-19, -1.11022302e-16])\n",
       "      message: 'CONVERGENCE: NORM_OF_PROJECTED_GRADIENT_<=_PGTOL'\n",
       "         nfev: 6\n",
       "          nit: 4\n",
       "         njev: 6\n",
       "     opt_time: 0.010929584503173828\n",
       " staging_time: 3.337860107421875e-06\n",
       "       status: 0\n",
       "      success: True\n",
       "            x: array([ 1.08284918e-16, -1.12986561e-01])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Scipy `OptimizeResult` object\n",
    "uccsd.opt_res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "9bbd78fd-8cf4-4a0a-8187-ca4896d32ee8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.08284918e-16, -1.12986561e-01])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# the optimized parameters\n",
    "uccsd.params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ec0d9452-36cb-464d-97a1-26539b787c8d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
       "        0.00000000e+00,  9.93623806e-01,  1.08284918e-16,  0.00000000e+00,\n",
       "        0.00000000e+00,  1.08284918e-16, -1.12746318e-01,  0.00000000e+00,\n",
       "        0.00000000e+00,  0.00000000e+00,  0.00000000e+00,  0.00000000e+00])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# optimized circuit statevector\n",
    "uccsd.statevector()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "3e628f4a-abe5-4cf8-8230-591c68878948",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 9.93623806e-01,  1.08284918e-16,  1.08284918e-16, -1.12746318e-01])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# configuration interaction vector\n",
    "uccsd.civector()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "1a06c242-7b28-41f2-8077-a90997861687",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 5,  6,  9, 10], dtype=uint64)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# bitstring for each element in the CI vector\n",
    "uccsd.get_ci_strings()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf3b8b0c-9dee-444c-9a8e-7c628ed4e4ab",
   "metadata": {},
   "source": [
    "## Active Space Approximation\n",
    "\n",
    "TenCirChem offers the simplest API for active space approximation among available packages."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "85c17b8f-e225-44df-88e0-0298072b7630",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "################################ Ansatz ###############################\n",
      " #qubits  #params  #excitations initial condition\n",
      "       4        2             3               RHF\n",
      "############################### Energy ################################\n",
      "       energy (Hartree)    error (mH) correlation energy (%)\n",
      "HF            -4.149619  1.343542e+01                 -0.000\n",
      "MP2           -4.157573  5.480558e+00                 59.208\n",
      "CCSD          -4.163054 -1.932076e-04                100.001\n",
      "UCCSD         -4.163054 -8.881784e-13                100.000\n",
      "FCI           -4.163054  0.000000e+00                100.000\n",
      "############################# Excitations #############################\n",
      "     excitation configuration     parameter  initial guess\n",
      "0        (3, 2)          1001  1.112312e-15       0.000000\n",
      "1        (1, 0)          0110  1.112312e-15       0.000000\n",
      "2  (1, 3, 2, 0)          1010 -1.377274e-01      -0.082065\n",
      "######################### Optimization Result #########################\n",
      "            e: -4.163053957291894\n",
      "          fun: array(-4.16305396)\n",
      "     hess_inv: <2x2 LbfgsInvHessProduct with dtype=float64>\n",
      "   init_guess: [0.0, -0.08206541779079227]\n",
      "          jac: array([-1.21074365e-17, -5.55111512e-17])\n",
      "      message: 'CONVERGENCE: NORM_OF_PROJECTED_GRADIENT_<=_PGTOL'\n",
      "         nfev: 6\n",
      "          nit: 4\n",
      "         njev: 6\n",
      "     opt_time: 0.003999471664428711\n",
      " staging_time: 1.6689300537109375e-06\n",
      "       status: 0\n",
      "      success: True\n",
      "            x: array([ 1.11231160e-15, -1.37727434e-01])\n"
     ]
    }
   ],
   "source": [
    "from tencirchem.molecule import h8\n",
    "\n",
    "# (2e, 2o) active space\n",
    "uccsd = UCCSD(h8, active_space=(2, 2))\n",
    "uccsd.kernel()\n",
    "uccsd.print_summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "105b9dfb-9222-42ab-be07-8b0b23c8c6bd",
   "metadata": {},
   "source": [
    "The reference energies including MP2, CCSD nad FCI are also based on the active space."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97009d72-e8c7-4760-9613-6b9e77be0090",
   "metadata": {},
   "source": [
    "## Starting from the Integrals\n",
    "One may start the UCC calculation from custom integrals without the need to define a molecule."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "5620f42b-aa6b-4900-95ab-98c20f08097b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-4.163053957291894 -4.163053957291894\n"
     ]
    }
   ],
   "source": [
    "uccsd_from_integral = UCCSD.from_integral(uccsd.int1e, uccsd.int2e, uccsd.n_elec, uccsd.e_core)\n",
    "# the energy is the same with the last calculation\n",
    "print(uccsd_from_integral.kernel(), uccsd.e_ucc)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8ab3f9d-94a3-4b20-a3b7-42846329a082",
   "metadata": {},
   "source": [
    "## Feeding in Custom Parameters\n",
    "\n",
    "In TenCirChem, it is also fairly easy to feed in customized parameters to get desired properties"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "88ffc492-8e6e-4ee2-ad86-417199683eb4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "params = np.zeros(uccsd.n_params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "4bd914bd-cd38-490d-b496-c093b32c5996",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-4.149618533807672, -4.1496185338076685)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uccsd.energy(params), uccsd.e_hf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "1b06a3bc-c88e-428b-9f1c-5d025a4e1579",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-4.149618533807672, array([-2.27679890e-15,  1.93866451e-01]))"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# energy and gradient, can be used in optimization\n",
    "# one caveat is that TenCirChem by default uses float32, while SciPy assumes float64\n",
    "uccsd.energy_and_grad(params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "7b38c34c-fe1e-41dd-b05e-19b44f5a88e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uccsd.statevector(params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "450d9fc8-40cc-49ce-aa77-f21865614aa7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1., 0., 0., 0.])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Configuration Interaction vector. Compatible with PySCF.\n",
    "civector = uccsd.civector(params)\n",
    "civector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "a2e9b325-4544-443c-b0a7-324db77da8c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-4.149618533807672"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calculate the energy by hand\n",
    "civector @ uccsd.hamiltonian(civector) + uccsd.e_core"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "a19b0abb-c4c0-47b2-b5c3-267a008b9777",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "################################ Ansatz ###############################\n",
      " #qubits  #params  #excitations initial condition\n",
      "       4        2             3               RHF\n",
      "############################### Energy ################################\n",
      "       energy (Hartree)  error (mH) correlation energy (%)\n",
      "HF            -4.149619   13.435423                 -0.000\n",
      "MP2           -4.157573    5.480558                 59.208\n",
      "CCSD          -4.163054   -0.000193                100.001\n",
      "UCCSD         -4.149619   13.435423                  0.000\n",
      "FCI           -4.163054    0.000000                100.000\n",
      "############################# Excitations #############################\n",
      "     excitation configuration  parameter  initial guess\n",
      "0        (3, 2)          1001        0.0       0.000000\n",
      "1        (1, 0)          0110        0.0       0.000000\n",
      "2  (1, 3, 2, 0)          1010        0.0      -0.082065\n",
      "######################### Optimization Result #########################\n",
      "            e: -4.163053957291894\n",
      "          fun: array(-4.16305396)\n",
      "     hess_inv: <2x2 LbfgsInvHessProduct with dtype=float64>\n",
      "   init_guess: [0.0, -0.08206541779079227]\n",
      "          jac: array([-1.21074365e-17, -5.55111512e-17])\n",
      "      message: 'CONVERGENCE: NORM_OF_PROJECTED_GRADIENT_<=_PGTOL'\n",
      "         nfev: 6\n",
      "          nit: 4\n",
      "         njev: 6\n",
      "     opt_time: 0.003999471664428711\n",
      " staging_time: 1.6689300537109375e-06\n",
      "       status: 0\n",
      "      success: True\n",
      "            x: array([ 1.11231160e-15, -1.37727434e-01])\n"
     ]
    }
   ],
   "source": [
    "# set the parameters, and use the class as usual\n",
    "uccsd.params = params\n",
    "uccsd.print_summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40212b40-6294-4064-8c5c-ee2963e782ca",
   "metadata": {},
   "source": [
    "## Reduced Density Matrix\n",
    "Calculate reduced density matrix in atomic orbital or molecule orbital basis "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "1d97185e-809b-486c-b343-bbd4a7b372aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((8, 8), (8, 8, 8, 8))"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uccsd.make_rdm1().shape, uccsd.make_rdm2().shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "11b0b0f1-557f-4b87-9277-1be700fbe7a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((8, 8), (8, 8, 8, 8))"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uccsd.make_rdm1(basis=\"MO\").shape, uccsd.make_rdm2(basis=\"MO\").shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23bf9027-bee0-4e7c-b699-efcebf9e235d",
   "metadata": {},
   "source": [
    "Note that the RDM is for the whole space, beyond the active space."
   ]
  }
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