Fig 5 Eigenstates L = 3 variational flow TFIM J = 2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from simple_exact_diagonalization_routines.local_matrix_class import *\n",
    "from scipy.linalg import expm\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# System size\n",
    "L = 3\n",
    "\n",
    "# Create objects of Pauli operators\n",
    "X = X_class(L)\n",
    "Y = Y_class(L)\n",
    "Z = Z_class(L)\n",
    "S_plus = S_plus_class(L)\n",
    "S_minus = S_minus_class(L)\n",
    "\n",
    "# Trivial initialization of the right size matrix\n",
    "H_TFIM = 0*np.eye( 2**L ) \n",
    "\n",
    "for x in range(L-1):\n",
    "    H_TFIM = H_TFIM + 2*X.at(x+1).dot(X.at(x+2)) + Z.at(x+1)\n",
    "H_TFIM = H_TFIM + Z.at(L)    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from class_double_bracket_flow import *\n",
    "\n",
    "\n",
    "flow = double_bracket_flow( H_TFIM )\n",
    "\n",
    "flow.flow_step_max =1\n",
    "flow.flow_step_min = 0\n",
    "flow.nmb_search_points_minimizing_s_search = 100\n",
    "flow.please_use_binary_search  =False\n",
    "\n",
    "flow.nmb_flow_steps = 15\n",
    "flow.flow_forwards( states = np.eye(2**L).tolist() )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/marek/Projects/Quantum_decoupling_algorithm/numerical_code/class_double_bracket_flow.py:960: UserWarning:\n",
      "\n",
      "FixedFormatter should only be used together with FixedLocator\n",
      "\n",
      "/home/marek/Projects/Quantum_decoupling_algorithm/numerical_code/class_double_bracket_flow.py:987: UserWarning:\n",
      "\n",
      "FixedFormatter should only be used together with FixedLocator\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x288 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "flow.show_flow_forwards_results_fancy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Flow step  0\n",
      "Found minimum norm =  8.0  for s =  9.5367431640625e-07\n",
      "Flow step output with generator type  single_commutator using string  ZZZ  gave for s =  9.5367431640625e-07   minimal norm  8.0\n",
      "Found minimum norm =  6.928203230275949  for s =  0.9817476272583008\n",
      "Flow step output with generator type  single_commutator using string  ZZ1  gave for s =  0.9817476272583008   minimal norm  6.928203230275949\n",
      "Found minimum norm =  6.693280212485764  for s =  0.4782896041870117\n",
      "Flow step output with generator type  single_commutator using string  Z1Z  gave for s =  0.4782896041870117   minimal norm  6.693280212485764\n",
      "Found minimum norm =  6.17344767382911  for s =  0.5198526382446289\n",
      "Flow step output with generator type  single_commutator using string  Z11  gave for s =  0.5198526382446289   minimal norm  6.17344767382911\n",
      "Found minimum norm =  6.928203230275949  for s =  0.9817476272583008\n",
      "Flow step output with generator type  single_commutator using string  1ZZ  gave for s =  0.9817476272583008   minimal norm  6.928203230275949\n",
      "Found minimum norm =  5.021851319691739  for s =  0.46994876861572266\n",
      "Flow step output with generator type  single_commutator using string  1Z1  gave for s =  0.46994876861572266   minimal norm  5.021851319691739\n",
      "Found minimum norm =  6.17344767382911  for s =  0.5198526382446289\n",
      "Flow step output with generator type  single_commutator using string  11Z  gave for s =  0.5198526382446289   minimal norm  6.17344767382911\n",
      "Found minimum norm =  8.0  for s =  9.5367431640625e-07\n",
      "Flow step output with generator type  single_commutator using string  111  gave for s =  9.5367431640625e-07   minimal norm  8.0\n",
      "Found minimum norm =  5.843251966997657  for s =  0.042220115661621094\n",
      "Found that canonical is worse because  5.843251966997657 5.021851319691739\n",
      "Found minimum norm =  5.021851319691739  for s =  0.46994876861572266\n",
      "Flowing forwards with type  single_commutator\n",
      "[]\n",
      "Flow step  1\n",
      "Found minimum norm =  5.021851319691739  for s =  9.5367431640625e-07\n",
      "Flow step output with generator type  single_commutator using string  ZZZ  gave for s =  9.5367431640625e-07   minimal norm  5.021851319691739\n",
      "Found minimum norm =  4.778344396614585  for s =  0.1195840835571289\n",
      "Flow step output with generator type  single_commutator using string  ZZ1  gave for s =  0.1195840835571289   minimal norm  4.778344396614585\n",
      "Found minimum norm =  5.021851319725252  for s =  9.5367431640625e-07\n",
      "Flow step output with generator type  single_commutator using string  Z1Z  gave for s =  9.5367431640625e-07   minimal norm  5.021851319725252\n",
      "Found minimum norm =  5.021861776155474  for s =  9.5367431640625e-07\n",
      "Flow step output with generator type  single_commutator using string  Z11  gave for s =  9.5367431640625e-07   minimal norm  5.021861776155474\n",
      "Found minimum norm =  4.778344396614585  for s =  0.1195840835571289\n",
      "Flow step output with generator type  single_commutator using string  1ZZ  gave for s =  0.1195840835571289   minimal norm  4.778344396614585\n",
      "Found minimum norm =  4.466429943196894  for s =  0.10052204132080078\n",
      "Flow step output with generator type  single_commutator using string  1Z1  gave for s =  0.10052204132080078   minimal norm  4.466429943196894\n",
      "Found minimum norm =  5.0218617761554745  for s =  9.5367431640625e-07\n",
      "Flow step output with generator type  single_commutator using string  11Z  gave for s =  9.5367431640625e-07   minimal norm  5.0218617761554745\n",
      "Found minimum norm =  5.021851319691739  for s =  9.5367431640625e-07\n",
      "Flow step output with generator type  single_commutator using string  111  gave for s =  9.5367431640625e-07   minimal norm  5.021851319691739\n",
      "Found minimum norm =  4.019426413848646  for s =  0.03959941864013672\n",
      "Found that canonical is better because  4.019426413848646 4.466429943196894\n",
      "Found minimum norm =  4.019426413848646  for s =  0.03959941864013672\n",
      "Flowing forwards with type  canonical\n",
      "[]\n",
      "Flow step  2\n",
      "Found minimum norm =  4.019426413848646  for s =  9.5367431640625e-07\n",
      "Flow step output with generator type  single_commutator using string  ZZZ  gave for s =  9.5367431640625e-07   minimal norm  4.019426413848646\n",
      "Found minimum norm =  3.6394255749662174  for s =  0.1249990463256836\n",
      "Flow step output with generator type  single_commutator using string  ZZ1  gave for s =  0.1249990463256836   minimal norm  3.6394255749662174\n",
      "Found minimum norm =  4.019426413921803  for s =  9.5367431640625e-07\n",
      "Flow step output with generator type  single_commutator using string  Z1Z  gave for s =  9.5367431640625e-07   minimal norm  4.019426413921803\n",
      "Found minimum norm =  4.019433170404604  for s =  9.5367431640625e-07\n",
      "Flow step output with generator type  single_commutator using string  Z11  gave for s =  9.5367431640625e-07   minimal norm  4.019433170404604\n",
      "Found minimum norm =  3.63942557496622  for s =  0.1249990463256836\n",
      "Flow step output with generator type  single_commutator using string  1ZZ  gave for s =  0.1249990463256836   minimal norm  3.63942557496622\n",
      "Found minimum norm =  3.7464816128412566  for s =  0.055710792541503906\n",
      "Flow step output with generator type  single_commutator using string  1Z1  gave for s =  0.055710792541503906   minimal norm  3.7464816128412566\n",
      "Found minimum norm =  4.019433170404604  for s =  9.5367431640625e-07\n",
      "Flow step output with generator type  single_commutator using string  11Z  gave for s =  9.5367431640625e-07   minimal norm  4.019433170404604\n",
      "Found minimum norm =  4.019426413848646  for s =  9.5367431640625e-07\n",
      "Flow step output with generator type  single_commutator using string  111  gave for s =  9.5367431640625e-07   minimal norm  4.019426413848646\n",
      "Found minimum norm =  3.6253628777444704  for s =  0.019028663635253906\n",
      "Found that canonical is better because  3.6253628777444704 3.6394255749662174\n",
      "Found minimum norm =  3.6253628777444704  for s =  0.019028663635253906\n",
      "Flowing forwards with type  canonical\n",
      "[]\n",
      "Flow step  3\n",
      "Found minimum norm =  3.6253628777444704  for s =  9.5367431640625e-07\n",
      "Flow step output with generator type  single_commutator using string  ZZZ  gave for s =  9.5367431640625e-07   minimal norm  3.6253628777444704\n",
      "Found minimum norm =  2.330242795804919  for s =  0.19855213165283203\n",
      "Flow step output with generator type  single_commutator using string  ZZ1  gave for s =  0.19855213165283203   minimal norm  2.330242795804919\n",
      "Found minimum norm =  3.6253628777507227  for s =  9.5367431640625e-07\n",
      "Flow step output with generator type  single_commutator using string  Z1Z  gave for s =  9.5367431640625e-07   minimal norm  3.6253628777507227\n",
      "Found minimum norm =  2.9454617488004224  for s =  0.1874990463256836\n",
      "Flow step output with generator type  single_commutator using string  Z11  gave for s =  0.1874990463256836   minimal norm  2.9454617488004224\n",
      "Found minimum norm =  2.3302427958049194  for s =  0.19855213165283203\n",
      "Flow step output with generator type  single_commutator using string  1ZZ  gave for s =  0.19855213165283203   minimal norm  2.3302427958049194\n",
      "Found minimum norm =  3.5878154120076835  for s =  0.0733346939086914\n",
      "Flow step output with generator type  single_commutator using string  1Z1  gave for s =  0.0733346939086914   minimal norm  3.5878154120076835\n",
      "Found minimum norm =  2.9454617488004224  for s =  0.1874990463256836\n",
      "Flow step output with generator type  single_commutator using string  11Z  gave for s =  0.1874990463256836   minimal norm  2.9454617488004224\n",
      "Found minimum norm =  3.6253628777444704  for s =  9.5367431640625e-07\n",
      "Flow step output with generator type  single_commutator using string  111  gave for s =  9.5367431640625e-07   minimal norm  3.6253628777444704\n",
      "Found minimum norm =  3.48374900805577  for s =  0.033112525939941406\n",
      "Found that canonical is worse because  3.48374900805577 2.330242795804919\n",
      "Found minimum norm =  2.330242795804919  for s =  0.19855213165283203\n",
      "Flowing forwards with type  single_commutator\n",
      "[]\n",
      "Flow step  4\n",
      "Found minimum norm =  2.330242795804919  for s =  9.5367431640625e-07\n",
      "Flow step output with generator type  single_commutator using string  ZZZ  gave for s =  9.5367431640625e-07   minimal norm  2.330242795804919\n",
      "Found minimum norm =  2.330248655398932  for s =  9.5367431640625e-07\n",
      "Flow step output with generator type  single_commutator using string  ZZ1  gave for s =  9.5367431640625e-07   minimal norm  2.330248655398932\n",
      "Found minimum norm =  2.327287642764131  for s =  0.007500648498535156\n",
      "Flow step output with generator type  single_commutator using string  Z1Z  gave for s =  0.007500648498535156   minimal norm  2.327287642764131\n",
      "Found minimum norm =  2.3302546015756844  for s =  9.5367431640625e-07\n",
      "Flow step output with generator type  single_commutator using string  Z11  gave for s =  9.5367431640625e-07   minimal norm  2.3302546015756844\n",
      "Found minimum norm =  2.3302560889222637  for s =  9.5367431640625e-07\n",
      "Flow step output with generator type  single_commutator using string  1ZZ  gave for s =  9.5367431640625e-07   minimal norm  2.3302560889222637\n",
      "Found minimum norm =  2.1083531282805654  for s =  0.0627279281616211\n",
      "Flow step output with generator type  single_commutator using string  1Z1  gave for s =  0.0627279281616211   minimal norm  2.1083531282805654\n",
      "Found minimum norm =  2.330255011182207  for s =  9.5367431640625e-07\n",
      "Flow step output with generator type  single_commutator using string  11Z  gave for s =  9.5367431640625e-07   minimal norm  2.330255011182207\n",
      "Found minimum norm =  2.330242795804919  for s =  9.5367431640625e-07\n",
      "Flow step output with generator type  single_commutator using string  111  gave for s =  9.5367431640625e-07   minimal norm  2.330242795804919\n",
      "Found minimum norm =  1.7004250119915698  for s =  0.023499488830566406\n",
      "Found that canonical is better because  1.7004250119915698 2.1083531282805654\n",
      "Found minimum norm =  1.7004250119915698  for s =  0.023499488830566406\n",
      "Flowing forwards with type  canonical\n",
      "[]\n",
      "Flow step  5\n",
      "Found minimum norm =  1.7004250119915698  for s =  9.5367431640625e-07\n",
      "Flow step output with generator type  single_commutator using string  ZZZ  gave for s =  9.5367431640625e-07   minimal norm  1.7004250119915698\n",
      "Found minimum norm =  1.700429642788085  for s =  9.5367431640625e-07\n",
      "Flow step output with generator type  single_commutator using string  ZZ1  gave for s =  9.5367431640625e-07   minimal norm  1.700429642788085\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found minimum norm =  1.6996920182009778  for s =  0.003912925720214844\n",
      "Flow step output with generator type  single_commutator using string  Z1Z  gave for s =  0.003912925720214844   minimal norm  1.6996920182009778\n",
      "Found minimum norm =  1.700435881189601  for s =  9.5367431640625e-07\n",
      "Flow step output with generator type  single_commutator using string  Z11  gave for s =  9.5367431640625e-07   minimal norm  1.700435881189601\n",
      "Found minimum norm =  1.7004367997972687  for s =  9.5367431640625e-07\n",
      "Flow step output with generator type  single_commutator using string  1ZZ  gave for s =  9.5367431640625e-07   minimal norm  1.7004367997972687\n",
      "Found minimum norm =  1.5372905218148967  for s =  0.05665302276611328\n",
      "Flow step output with generator type  single_commutator using string  1Z1  gave for s =  0.05665302276611328   minimal norm  1.5372905218148967\n",
      "Found minimum norm =  1.7004353837253503  for s =  9.5367431640625e-07\n",
      "Flow step output with generator type  single_commutator using string  11Z  gave for s =  9.5367431640625e-07   minimal norm  1.7004353837253503\n",
      "Found minimum norm =  1.7004250119915698  for s =  9.5367431640625e-07\n",
      "Flow step output with generator type  single_commutator using string  111  gave for s =  9.5367431640625e-07   minimal norm  1.7004250119915698\n",
      "Found minimum norm =  1.2062789940898502  for s =  0.019911766052246094\n",
      "Found that canonical is better because  1.2062789940898502 1.5372905218148967\n",
      "Found minimum norm =  1.2062789940898502  for s =  0.019911766052246094\n",
      "Flowing forwards with type  canonical\n",
      "[]\n",
      "Flow step  6\n",
      "Found minimum norm =  1.2062789940898502  for s =  9.5367431640625e-07\n",
      "Flow step output with generator type  single_commutator using string  ZZZ  gave for s =  9.5367431640625e-07   minimal norm  1.2062789940898502\n",
      "Found minimum norm =  1.206282826810033  for s =  9.5367431640625e-07\n",
      "Flow step output with generator type  single_commutator using string  ZZ1  gave for s =  9.5367431640625e-07   minimal norm  1.206282826810033\n",
      "Found minimum norm =  1.2062679846640487  for s =  0.0006303787231445312\n",
      "Flow step output with generator type  single_commutator using string  Z1Z  gave for s =  0.0006303787231445312   minimal norm  1.2062679846640487\n",
      "Found minimum norm =  1.2062869084243293  for s =  9.5367431640625e-07\n",
      "Flow step output with generator type  single_commutator using string  Z11  gave for s =  9.5367431640625e-07   minimal norm  1.2062869084243293\n",
      "Found minimum norm =  1.2062874958817673  for s =  9.5367431640625e-07\n",
      "Flow step output with generator type  single_commutator using string  1ZZ  gave for s =  9.5367431640625e-07   minimal norm  1.2062874958817673\n",
      "Found minimum norm =  1.1144329490471194  for s =  0.056305885314941406\n",
      "Flow step output with generator type  single_commutator using string  1Z1  gave for s =  0.056305885314941406   minimal norm  1.1144329490471194\n",
      "Found minimum norm =  1.2062868213105125  for s =  9.5367431640625e-07\n",
      "Flow step output with generator type  single_commutator using string  11Z  gave for s =  9.5367431640625e-07   minimal norm  1.2062868213105125\n",
      "Found minimum norm =  1.2062789940898502  for s =  9.5367431640625e-07\n",
      "Flow step output with generator type  single_commutator using string  111  gave for s =  9.5367431640625e-07   minimal norm  1.2062789940898502\n",
      "Found minimum norm =  0.8374581083835199  for s =  0.02138996124267578\n",
      "Found that canonical is better because  0.8374581083835199 1.1144329490471194\n",
      "Found minimum norm =  0.8374581083835199  for s =  0.02138996124267578\n",
      "Flowing forwards with type  canonical\n",
      "[]\n",
      "Flow step  7\n",
      "Found minimum norm =  0.8374581083835199  for s =  9.5367431640625e-07\n",
      "Flow step output with generator type  single_commutator using string  ZZZ  gave for s =  9.5367431640625e-07   minimal norm  0.8374581083835199\n",
      "Found minimum norm =  0.8374601106784377  for s =  9.5367431640625e-07\n",
      "Flow step output with generator type  single_commutator using string  ZZ1  gave for s =  9.5367431640625e-07   minimal norm  0.8374601106784377\n",
      "Found minimum norm =  0.8374581626098566  for s =  9.5367431640625e-07\n",
      "Flow step output with generator type  single_commutator using string  Z1Z  gave for s =  9.5367431640625e-07   minimal norm  0.8374581626098566\n",
      "Found minimum norm =  0.8374636080212998  for s =  9.5367431640625e-07\n",
      "Flow step output with generator type  single_commutator using string  Z11  gave for s =  9.5367431640625e-07   minimal norm  0.8374636080212998\n",
      "Found minimum norm =  0.8374641661254961  for s =  9.5367431640625e-07\n",
      "Flow step output with generator type  single_commutator using string  1ZZ  gave for s =  9.5367431640625e-07   minimal norm  0.8374641661254961\n",
      "Found minimum norm =  0.7550264225858767  for s =  0.05581378936767578\n",
      "Flow step output with generator type  single_commutator using string  1Z1  gave for s =  0.05581378936767578   minimal norm  0.7550264225858767\n",
      "Found minimum norm =  0.8374635440200163  for s =  9.5367431640625e-07\n",
      "Flow step output with generator type  single_commutator using string  11Z  gave for s =  9.5367431640625e-07   minimal norm  0.8374635440200163\n",
      "Found minimum norm =  0.8374581083835199  for s =  9.5367431640625e-07\n",
      "Flow step output with generator type  single_commutator using string  111  gave for s =  9.5367431640625e-07   minimal norm  0.8374581083835199\n",
      "Found minimum norm =  0.5790360184205285  for s =  0.01897907257080078\n",
      "Found that canonical is better because  0.5790360184205285 0.7550264225858767\n",
      "Found minimum norm =  0.5790360184205285  for s =  0.01897907257080078\n",
      "Flowing forwards with type  canonical\n",
      "[]\n",
      "Flow step  8\n",
      "Found minimum norm =  0.5790360184205285  for s =  9.5367431640625e-07\n",
      "Flow step output with generator type  single_commutator using string  ZZZ  gave for s =  9.5367431640625e-07   minimal norm  0.5790360184205285\n",
      "Found minimum norm =  0.579037436970236  for s =  9.5367431640625e-07\n",
      "Flow step output with generator type  single_commutator using string  ZZ1  gave for s =  9.5367431640625e-07   minimal norm  0.579037436970236\n",
      "Found minimum norm =  0.5790361024994379  for s =  9.5367431640625e-07\n",
      "Flow step output with generator type  single_commutator using string  Z1Z  gave for s =  9.5367431640625e-07   minimal norm  0.5790361024994379\n",
      "Found minimum norm =  0.5790395523715071  for s =  9.5367431640625e-07\n",
      "Flow step output with generator type  single_commutator using string  Z11  gave for s =  9.5367431640625e-07   minimal norm  0.5790395523715071\n",
      "Found minimum norm =  0.579040067717642  for s =  9.5367431640625e-07\n",
      "Flow step output with generator type  single_commutator using string  1ZZ  gave for s =  9.5367431640625e-07   minimal norm  0.579040067717642\n",
      "Found minimum norm =  0.5329312060535291  for s =  0.057572364807128906\n",
      "Flow step output with generator type  single_commutator using string  1Z1  gave for s =  0.057572364807128906   minimal norm  0.5329312060535291\n",
      "Found minimum norm =  0.5790395054254817  for s =  9.5367431640625e-07\n",
      "Flow step output with generator type  single_commutator using string  11Z  gave for s =  9.5367431640625e-07   minimal norm  0.5790395054254817\n",
      "Found minimum norm =  0.5790360184205285  for s =  9.5367431640625e-07\n",
      "Flow step output with generator type  single_commutator using string  111  gave for s =  9.5367431640625e-07   minimal norm  0.5790360184205285\n",
      "Found minimum norm =  0.4048460028282807  for s =  0.020880699157714844\n",
      "Found that canonical is better because  0.4048460028282807 0.5329312060535291\n",
      "Found minimum norm =  0.4048460028282807  for s =  0.020880699157714844\n",
      "Flowing forwards with type  canonical\n",
      "[]\n",
      "Flow step  9\n",
      "Found minimum norm =  0.4048460028282807  for s =  9.5367431640625e-07\n",
      "Flow step output with generator type  single_commutator using string  ZZZ  gave for s =  9.5367431640625e-07   minimal norm  0.4048460028282807\n",
      "Found minimum norm =  0.4048465544506124  for s =  9.5367431640625e-07\n",
      "Flow step output with generator type  single_commutator using string  ZZ1  gave for s =  9.5367431640625e-07   minimal norm  0.4048465544506124\n",
      "Found minimum norm =  0.4048461035349571  for s =  9.5367431640625e-07\n",
      "Flow step output with generator type  single_commutator using string  Z1Z  gave for s =  9.5367431640625e-07   minimal norm  0.4048461035349571\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found minimum norm =  0.40484824823533244  for s =  9.5367431640625e-07\n",
      "Flow step output with generator type  single_commutator using string  Z11  gave for s =  9.5367431640625e-07   minimal norm  0.40484824823533244\n",
      "Found minimum norm =  0.40484870965735237  for s =  9.5367431640625e-07\n",
      "Flow step output with generator type  single_commutator using string  1ZZ  gave for s =  9.5367431640625e-07   minimal norm  0.40484870965735237\n",
      "Found minimum norm =  0.36310065142797504  for s =  0.058852195739746094\n",
      "Flow step output with generator type  single_commutator using string  1Z1  gave for s =  0.058852195739746094   minimal norm  0.36310065142797504\n",
      "Found minimum norm =  0.4048481876867731  for s =  9.5367431640625e-07\n",
      "Flow step output with generator type  single_commutator using string  11Z  gave for s =  9.5367431640625e-07   minimal norm  0.4048481876867731\n",
      "Found minimum norm =  0.4048460028282807  for s =  9.5367431640625e-07\n",
      "Flow step output with generator type  single_commutator using string  111  gave for s =  9.5367431640625e-07   minimal norm  0.4048460028282807\n",
      "Found minimum norm =  0.2900359631810606  for s =  0.01889514923095703\n",
      "Found that canonical is better because  0.2900359631810606 0.36310065142797504\n",
      "Found minimum norm =  0.2900359631810606  for s =  0.01889514923095703\n",
      "Flowing forwards with type  canonical\n",
      "[]\n",
      "Flow step  10\n",
      "Found minimum norm =  0.2900359631810606  for s =  9.5367431640625e-07\n",
      "Flow step output with generator type  single_commutator using string  ZZZ  gave for s =  9.5367431640625e-07   minimal norm  0.2900359631810606\n",
      "Found minimum norm =  0.29003631601475954  for s =  9.5367431640625e-07\n",
      "Flow step output with generator type  single_commutator using string  ZZ1  gave for s =  9.5367431640625e-07   minimal norm  0.29003631601475954\n",
      "Found minimum norm =  0.29003607388825503  for s =  9.5367431640625e-07\n",
      "Flow step output with generator type  single_commutator using string  Z1Z  gave for s =  9.5367431640625e-07   minimal norm  0.29003607388825503\n",
      "Found minimum norm =  0.29003729611650975  for s =  9.5367431640625e-07\n",
      "Flow step output with generator type  single_commutator using string  Z11  gave for s =  9.5367431640625e-07   minimal norm  0.29003729611650975\n",
      "Found minimum norm =  0.29003770657469435  for s =  9.5367431640625e-07\n",
      "Flow step output with generator type  single_commutator using string  1ZZ  gave for s =  9.5367431640625e-07   minimal norm  0.29003770657469435\n",
      "Found minimum norm =  0.26326912633344446  for s =  0.06524944305419922\n",
      "Flow step output with generator type  single_commutator using string  1Z1  gave for s =  0.06524944305419922   minimal norm  0.26326912633344446\n",
      "Found minimum norm =  0.29003720749261347  for s =  9.5367431640625e-07\n",
      "Flow step output with generator type  single_commutator using string  11Z  gave for s =  9.5367431640625e-07   minimal norm  0.29003720749261347\n",
      "Found minimum norm =  0.2900359631810606  for s =  9.5367431640625e-07\n",
      "Flow step output with generator type  single_commutator using string  111  gave for s =  9.5367431640625e-07   minimal norm  0.2900359631810606\n",
      "Found minimum norm =  0.21539897201038177  for s =  0.020995140075683594\n",
      "Found that canonical is better because  0.21539897201038177 0.26326912633344446\n",
      "Found minimum norm =  0.21539897201038177  for s =  0.020995140075683594\n",
      "Flowing forwards with type  canonical\n",
      "[]\n",
      "Flow step  11\n",
      "Found minimum norm =  0.21539897201038177  for s =  9.5367431640625e-07\n",
      "Flow step output with generator type  single_commutator using string  ZZZ  gave for s =  9.5367431640625e-07   minimal norm  0.21539897201038177\n",
      "Found minimum norm =  0.21539904746409716  for s =  9.5367431640625e-07\n",
      "Flow step output with generator type  single_commutator using string  ZZ1  gave for s =  9.5367431640625e-07   minimal norm  0.21539904746409716\n",
      "Found minimum norm =  0.21539909993199505  for s =  9.5367431640625e-07\n",
      "Flow step output with generator type  single_commutator using string  Z1Z  gave for s =  9.5367431640625e-07   minimal norm  0.21539909993199505\n",
      "Found minimum norm =  0.21539975657372473  for s =  9.5367431640625e-07\n",
      "Flow step output with generator type  single_commutator using string  Z11  gave for s =  9.5367431640625e-07   minimal norm  0.21539975657372473\n",
      "Found minimum norm =  0.21540008579828904  for s =  9.5367431640625e-07\n",
      "Flow step output with generator type  single_commutator using string  1ZZ  gave for s =  9.5367431640625e-07   minimal norm  0.21540008579828904\n",
      "Found minimum norm =  0.18906229025711366  for s =  0.0697927474975586\n",
      "Flow step output with generator type  single_commutator using string  1Z1  gave for s =  0.0697927474975586   minimal norm  0.18906229025711366\n",
      "Found minimum norm =  0.21539964731190706  for s =  9.5367431640625e-07\n",
      "Flow step output with generator type  single_commutator using string  11Z  gave for s =  9.5367431640625e-07   minimal norm  0.21539964731190706\n",
      "Found minimum norm =  0.21539897201038177  for s =  9.5367431640625e-07\n",
      "Flow step output with generator type  single_commutator using string  111  gave for s =  9.5367431640625e-07   minimal norm  0.21539897201038177\n",
      "Found minimum norm =  0.16745749511849983  for s =  0.01913166046142578\n",
      "Found that canonical is better because  0.16745749511849983 0.18906229025711366\n",
      "Found minimum norm =  0.16745749511849983  for s =  0.01913166046142578\n",
      "Flowing forwards with type  canonical\n",
      "[]\n",
      "Flow step  12\n",
      "Found minimum norm =  0.16745749511849983  for s =  9.5367431640625e-07\n",
      "Flow step output with generator type  single_commutator using string  ZZZ  gave for s =  9.5367431640625e-07   minimal norm  0.16745749511849983\n",
      "Found minimum norm =  0.16745756598179462  for s =  9.5367431640625e-07\n",
      "Flow step output with generator type  single_commutator using string  ZZ1  gave for s =  9.5367431640625e-07   minimal norm  0.16745756598179462\n",
      "Found minimum norm =  0.16745763666805769  for s =  9.5367431640625e-07\n",
      "Flow step output with generator type  single_commutator using string  Z1Z  gave for s =  9.5367431640625e-07   minimal norm  0.16745763666805769\n",
      "Found minimum norm =  0.16745790652727385  for s =  9.5367431640625e-07\n",
      "Flow step output with generator type  single_commutator using string  Z11  gave for s =  9.5367431640625e-07   minimal norm  0.16745790652727385\n",
      "Found minimum norm =  0.16745816864859356  for s =  9.5367431640625e-07\n",
      "Flow step output with generator type  single_commutator using string  1ZZ  gave for s =  9.5367431640625e-07   minimal norm  0.16745816864859356\n",
      "Found minimum norm =  0.14477196131678385  for s =  0.08735942840576172\n",
      "Flow step output with generator type  single_commutator using string  1Z1  gave for s =  0.08735942840576172   minimal norm  0.14477196131678385\n",
      "Found minimum norm =  0.16745777367772494  for s =  9.5367431640625e-07\n",
      "Flow step output with generator type  single_commutator using string  11Z  gave for s =  9.5367431640625e-07   minimal norm  0.16745777367772494\n",
      "Found minimum norm =  0.16745749511849983  for s =  9.5367431640625e-07\n",
      "Flow step output with generator type  single_commutator using string  111  gave for s =  9.5367431640625e-07   minimal norm  0.16745749511849983\n",
      "Found minimum norm =  0.13662397350176428  for s =  0.021454811096191406\n",
      "Found that canonical is better because  0.13662397350176428 0.14477196131678385\n",
      "Found minimum norm =  0.13662397350176428  for s =  0.021454811096191406\n",
      "Flowing forwards with type  canonical\n",
      "[]\n",
      "Flow step  13\n",
      "Found minimum norm =  0.13662397350176428  for s =  9.5367431640625e-07\n",
      "Flow step output with generator type  single_commutator using string  ZZZ  gave for s =  9.5367431640625e-07   minimal norm  0.13662397350176428\n",
      "Found minimum norm =  0.13662400874352829  for s =  9.5367431640625e-07\n",
      "Flow step output with generator type  single_commutator using string  ZZ1  gave for s =  9.5367431640625e-07   minimal norm  0.13662400874352829\n",
      "Found minimum norm =  0.1366241267777186  for s =  9.5367431640625e-07\n",
      "Flow step output with generator type  single_commutator using string  Z1Z  gave for s =  9.5367431640625e-07   minimal norm  0.1366241267777186\n",
      "Found minimum norm =  0.13662418978070034  for s =  9.5367431640625e-07\n",
      "Flow step output with generator type  single_commutator using string  Z11  gave for s =  9.5367431640625e-07   minimal norm  0.13662418978070034\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found minimum norm =  0.13662437420736348  for s =  9.5367431640625e-07\n",
      "Flow step output with generator type  single_commutator using string  1ZZ  gave for s =  9.5367431640625e-07   minimal norm  0.13662437420736348\n",
      "Found minimum norm =  0.11232057521073877  for s =  0.0959157943725586\n",
      "Flow step output with generator type  single_commutator using string  1Z1  gave for s =  0.0959157943725586   minimal norm  0.11232057521073877\n",
      "Found minimum norm =  0.13662404398272726  for s =  9.5367431640625e-07\n",
      "Flow step output with generator type  single_commutator using string  11Z  gave for s =  9.5367431640625e-07   minimal norm  0.13662404398272726\n",
      "Found minimum norm =  0.13662397350176428  for s =  9.5367431640625e-07\n",
      "Flow step output with generator type  single_commutator using string  111  gave for s =  9.5367431640625e-07   minimal norm  0.13662397350176428\n",
      "Found minimum norm =  0.1165471710253825  for s =  0.019652366638183594\n",
      "Found that canonical is worse because  0.1165471710253825 0.11232057521073877\n",
      "Found minimum norm =  0.11232057521073877  for s =  0.0959157943725586\n",
      "Flowing forwards with type  single_commutator\n",
      "[]\n",
      "Flow step  14\n",
      "Found minimum norm =  0.11232057521073877  for s =  9.5367431640625e-07\n",
      "Flow step output with generator type  single_commutator using string  ZZZ  gave for s =  9.5367431640625e-07   minimal norm  0.11232057521073877\n",
      "Found minimum norm =  0.11225453042989972  for s =  0.005034446716308594\n",
      "Flow step output with generator type  single_commutator using string  ZZ1  gave for s =  0.005034446716308594   minimal norm  0.11225453042989972\n",
      "Found minimum norm =  0.1123206651113644  for s =  9.5367431640625e-07\n",
      "Flow step output with generator type  single_commutator using string  Z1Z  gave for s =  9.5367431640625e-07   minimal norm  0.1123206651113644\n",
      "Found minimum norm =  0.11232081427270528  for s =  9.5367431640625e-07\n",
      "Flow step output with generator type  single_commutator using string  Z11  gave for s =  9.5367431640625e-07   minimal norm  0.11232081427270528\n",
      "Found minimum norm =  0.11232103846436697  for s =  9.5367431640625e-07\n",
      "Flow step output with generator type  single_commutator using string  1ZZ  gave for s =  9.5367431640625e-07   minimal norm  0.11232103846436697\n",
      "Found minimum norm =  0.09812429837928681  for s =  0.08897113800048828\n",
      "Flow step output with generator type  single_commutator using string  1Z1  gave for s =  0.08897113800048828   minimal norm  0.09812429837928681\n",
      "Found minimum norm =  0.11232072882787776  for s =  9.5367431640625e-07\n",
      "Flow step output with generator type  single_commutator using string  11Z  gave for s =  9.5367431640625e-07   minimal norm  0.11232072882787776\n",
      "Found minimum norm =  0.11232057521073877  for s =  9.5367431640625e-07\n",
      "Flow step output with generator type  single_commutator using string  111  gave for s =  9.5367431640625e-07   minimal norm  0.11232057521073877\n",
      "Found minimum norm =  0.0914133120023546  for s =  0.02258586883544922\n",
      "Found that canonical is better because  0.0914133120023546 0.09812429837928681\n",
      "Found minimum norm =  0.0914133120023546  for s =  0.02258586883544922\n",
      "Flowing forwards with type  canonical\n",
      "[]\n"
     ]
    }
   ],
   "source": [
    "flow2 = double_bracket_flow( H_TFIM )\n",
    "flow2.please_be_exhaustively_verbose = False\n",
    "flow2.please_be_verbose = True\n",
    "ini_state = np.kron( np.kron([0,1],[1,0]), [0,1])\n",
    "flow2.compute_observables_during_flow( initial_state=ini_state )\n",
    "flow2.flow_step_max =1\n",
    "flow2.flow_step_min = 0\n",
    "flow2.nmb_search_points_minimizing_s_search = 20\n",
    "flow2.please_use_binary_search  = True\n",
    "flow2.nmb_flow_steps = 15\n",
    "flow2.flow_via_best_Z_search( states = np.eye(2**L).tolist(), please_also_check_canonical_bracket=True )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x288 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/marek/Projects/Quantum_decoupling_algorithm/numerical_code/class_double_bracket_flow.py:960: UserWarning:\n",
      "\n",
      "FixedFormatter should only be used together with FixedLocator\n",
      "\n",
      "/home/marek/Projects/Quantum_decoupling_algorithm/numerical_code/class_double_bracket_flow.py:987: UserWarning:\n",
      "\n",
      "FixedFormatter should only be used together with FixedLocator\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x288 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "flow2.show_flow_forwards_results()\n",
    "flow2.show_flow_forwards_results_fancy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['1Z1',\n",
       " 'Canonical',\n",
       " 'Canonical',\n",
       " 'ZZ1',\n",
       " 'Canonical',\n",
       " 'Canonical',\n",
       " 'Canonical',\n",
       " 'Canonical',\n",
       " 'Canonical',\n",
       " 'Canonical',\n",
       " 'Canonical',\n",
       " 'Canonical',\n",
       " 'Canonical',\n",
       " '1Z1',\n",
       " 'Canonical']"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flow2.flow_outputs['strs_of_minimizer_Z']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "f = plt.figure(figsize = (12,5))\n",
    "\n",
    "f.add_subplot(121)\n",
    "for i, k in enumerate([0,1,2,3,4,5,6,7]):\n",
    "    e = flow.observable_over_steps( k, flow.flow_outputs['expected_energy'] )\n",
    "    ef = np.sqrt(\n",
    "        flow.observable_over_steps( k, flow.flow_outputs['expected_energy_fluctuation'] ))\n",
    "    plt.errorbar( np.linspace( i * 0.02, flow.nmb_flow_steps + i * 0.02, \n",
    "                               flow.nmb_flow_steps), e, yerr = ef ) \n",
    "plt.ylim( [-7.5,7.5])\n",
    "\n",
    "#plt.yticks(np.round([max(last_e),min(last_e),0],1))\n",
    "plt.xlabel(r'Flow step $n$')\n",
    "plt.ylabel(r'Average energy $\\langle \\hat H_n \\rangle$')\n",
    "plt.title(r'Wegner flow')\n",
    "    \n",
    "\n",
    " \n",
    "f.add_subplot(122)\n",
    "for i, k in enumerate([0,1,2,3,4,5,6,7]):\n",
    "    e = flow2.observable_over_steps( k, flow2.flow_outputs['expected_energy'] )\n",
    "    ef = np.sqrt(\n",
    "        flow2.observable_over_steps( k, flow2.flow_outputs['expected_energy_fluctuation'] ))\n",
    "    label = r'$\\vert%d\\rangle$' %k\n",
    "\n",
    "    plt.errorbar( np.linspace( i * 0.05, flow.nmb_flow_steps + i * 0.02, \n",
    "                              flow.nmb_flow_steps), e, yerr = ef , \n",
    "                 label= label) \n",
    "plt.ylim( [-7.5,7.5])\n",
    "#plt.yticks(np.round([max(last_e),min(last_e),0],1), labels=[])\n",
    "plt.xlabel(r'Flow step $n$')\n",
    "plt.title(r'Wegner and $Z$-product flow')\n",
    "plt.grid()\n",
    "plt.legend(bbox_to_anchor=(1, 1.05))\n",
    "plt.savefig( 'figs/eigenstates_L_%d.pdf'%L)\n",
    "plt.show()\n"
   ]
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     "metadata": {},
     "output_type": "display_data"
    },
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['1Z1', 'Canonical', 'Canonical', 'ZZ1', 'Canonical', 'Canonical', 'Canonical', 'Canonical', 'Canonical', 'Canonical', 'Canonical', 'Canonical', 'Canonical', '1Z1', 'Canonical']\n"
     ]
    }
   ],
   "source": [
    "from plotly.subplots import make_subplots\n",
    "import plotly.graph_objects as go\n",
    "\n",
    "import plotly.express as px\n",
    "color_discrete_sequence=px.colors.qualitative.G10\n",
    "\n",
    "fig = make_subplots(rows=1, cols=3, subplot_titles=(\"Plot 1\", \"Plot 2\", \"plot 3\"))\n",
    "\n",
    "fig.layout.annotations[0].update(font=dict(\n",
    "            family='Courier New, monospace',\n",
    "            size=14\n",
    "        ),x=0.15, y=1.01, text=r'$\\text{Norm off-diagonal } \\|\\sigma(\\hat H_k)\\|_{\\text{HS}}$')\n",
    "fig.layout.annotations[1].update(font=dict(\n",
    "            family='Courier New, monospace',\n",
    "            size=14\n",
    "        ),x=0.50, y=1.01,text=r'$\\text{Expected energy value } E^{(k)}_\\mu$')\n",
    "fig.layout.annotations[2].update(font=dict(\n",
    "            family='Courier New, monospace',\n",
    "            size=14\n",
    "        ),x=0.855, y=1.01,text=r'$\\text{Expected energy value } E^{(k)}_\\mu$')\n",
    "\n",
    "x = [ sum(flow.flow_outputs['minimizing_flow_step'][:i]) for i in \n",
    "    range(1,1+len(flow.flow_outputs['minimizing_flow_step']))]\n",
    "\n",
    "norms = flow.flow_outputs['minimal_norm_sigma_H_s']\n",
    "norms2 = flow2.flow_outputs['minimal_norm_sigma_H_s']\n",
    "\n",
    "fig.add_trace(go.Scatter(\n",
    "    x=x,\n",
    "    y=norms,\n",
    "    mode = 'lines+markers',\n",
    "        name = \"GWW\",\n",
    "        showlegend=False\n",
    "),  row=1, col=1)\n",
    " \n",
    "fig.add_trace( go.Scatter(\n",
    "        name = \"Variational\",\n",
    "    x=x,\n",
    "    y=norms2\n",
    ",mode = 'lines+markers',\n",
    "        showlegend=False\n",
    "),  row=1, col=1)\n",
    "x_selection = [1,2,-1]\n",
    "y_selection = [0,1,-1]\n",
    "y_selection2 = [1,2,5,-1]\n",
    "fig.update_layout(\n",
    "    xaxis1=dict(\n",
    "    title=dict(text=r'', standoff=8),\n",
    "        titlefont=dict(\n",
    "            family='Courier New, monospace',\n",
    "            size=14\n",
    "        ),\n",
    "        tickmode = 'array',\n",
    "        tickvals = [x[s] for s in x_selection],\n",
    "        ticktext = [round(x[s],2) for s in x_selection]\n",
    "    ),\n",
    "    yaxis1=dict(\n",
    "        title=dict(text=r'', standoff=8),\n",
    "        titlefont=dict(\n",
    "            family='Courier New, monospace',\n",
    "            size=14\n",
    "        ),\n",
    "        tickmode = 'array',\n",
    "        tickvals = [norms[s] for s in y_selection]+[norms2[s] for s in y_selection2],\n",
    "        ticktext = [round(norms[s],1) for s in y_selection]+[round(norms2[s],1) for s in y_selection2]\n",
    "    ))\n",
    "\n",
    "#fig_norm.update_yaxes(range=[min(norms)-5,max(norms)+6])\n",
    "\n",
    "y_selection = [0,1,2,3,7,13,-1]\n",
    "y_labels_rounded = [ round(y, 1 ) for y in norms ]\n",
    "y_vals = [norms[i] for i in y_selection]\n",
    "y_labels = [ y_labels_rounded[i] for i in y_selection]\n",
    "x_selection = [0,1,3,7,13,-2]\n",
    "x_vals = [x[i] for i in x_selection]\n",
    "x_labels = [ round(x[i],2)for i in x_selection]\n",
    "\n",
    "\n",
    "\n",
    "x = [x+1 for x in range((flow.nmb_flow_steps))]\n",
    "\n",
    "#Canonical flow\n",
    "\n",
    "for i, k in enumerate(range(2**L)):\n",
    "    e = flow.observable_over_steps( k, flow.flow_outputs['expected_energy'] )\n",
    "    ef = np.sqrt(\n",
    "        flow.observable_over_steps( k,\n",
    "        flow.flow_outputs['expected_energy_fluctuation'] )\n",
    "    )\n",
    "    label = r'$\\vert%d\\rangle$' %k\n",
    "    label = r'$|'+str(flow.binary_label_from_state_number(k)) +r'\\rangle$'\n",
    "\n",
    "    fig.add_trace(\n",
    "        go.Scatter(x=x, y=e, line=dict(color=color_discrete_sequence[i])\n",
    "                  , name = label),\n",
    "        row=1, col=2\n",
    "    )\n",
    "    y = e\n",
    "    y_upper = [ E+EF for E,EF in zip( e, ef)]\n",
    "    y_lower = [ E-EF for E,EF in zip( e, ef)]\n",
    "\n",
    "    fig.add_trace(\n",
    "        go.Scatter(\n",
    "        x=x+x[::-1], # x, then x reversed\n",
    "        y=y_upper+y_lower[::-1], # upper, then lower reversed\n",
    "        fill='toself',\n",
    "        fillcolor=color_discrete_sequence[i],\n",
    "        line=dict(color='rgba(0,0,0,0)'),\n",
    "            opacity=0.2,\n",
    "        hoverinfo=\"skip\",\n",
    "        showlegend=False),\n",
    "        row=1, col=2\n",
    "    )   \n",
    "\n",
    "\n",
    "for i, k in enumerate(range(2**L)):\n",
    "    e = flow2.observable_over_steps( k, flow2.flow_outputs['expected_energy'] )\n",
    "    ef = np.sqrt(\n",
    "        flow2.observable_over_steps( k,\n",
    "            flow2.flow_outputs['expected_energy_fluctuation'] ))\n",
    "    label = r'$\\vert%d\\rangle$' %k\n",
    "\n",
    "    fig.add_trace(\n",
    "        go.Scatter(x=x, y=e,line=dict(color=color_discrete_sequence[i]), name = label,showlegend=False),\n",
    "        row=1, col=3\n",
    "    )\n",
    "    y = e\n",
    "    y_upper = [ E+EF for E,EF in zip( e, ef)]\n",
    "    y_lower = [ E-EF for E,EF in zip( e, ef)]\n",
    "\n",
    "    fig.add_trace(\n",
    "        go.Scatter(\n",
    "        x=x+x[::-1], # x, then x reversed\n",
    "        y=y_upper+y_lower[::-1], # upper, then lower reversed\n",
    "        fill='toself',\n",
    "        fillcolor=color_discrete_sequence[i],\n",
    "        line=dict(color='rgba(0,0,0,0)'),\n",
    "            opacity=0.2,\n",
    "        hoverinfo=\"skip\",\n",
    "        showlegend=False),\n",
    "        row=1, col=3\n",
    "    )   \n",
    "fig.add_annotation(x=-.045, y=1.1,\n",
    "            text='a)',\n",
    "            xref=\"paper\",\n",
    "    yref=\"paper\",\n",
    "            showarrow=False,font=dict(\n",
    "            family='Courier New, monospace',\n",
    "            size=14\n",
    "        )\n",
    ")  \n",
    "fig.add_annotation(x=.315, y=1.1,\n",
    "            text='b)',\n",
    "            xref=\"paper\",\n",
    "    yref=\"paper\",\n",
    "            showarrow=False,font=dict(\n",
    "            family='Courier New, monospace',\n",
    "            size=14\n",
    "        )\n",
    ") \n",
    "fig.add_annotation(x=.70, y=1.1,\n",
    "            text='c)',\n",
    "            xref=\"paper\",\n",
    "    yref=\"paper\",\n",
    "            showarrow=False,font=dict(\n",
    "            family='Courier New, monospace',\n",
    "            size=14\n",
    "        )\n",
    ") \n",
    "\n",
    "# fig.add_annotation(x=.8, y=8,\n",
    "#             text=\"GWW flow\",\n",
    "#             showarrow=True,\n",
    "#             arrowhead=1)\n",
    "\n",
    "x = [ sum(flow.flow_outputs['minimizing_flow_step'][:i]) for i in \n",
    "    range(1,1+len(flow.flow_outputs['minimizing_flow_step']))]\n",
    "fig.add_annotation(dict(\n",
    "                x= x[1],\n",
    "                y= norms[1]+.3,\n",
    "                xref=\"x\", yref=\"y\",\n",
    "                text=\"GWW flow\",\n",
    "                showarrow=True,\n",
    "                axref = \"x\", ayref='y',\n",
    "                ax= .8,\n",
    "                ay= 8,\n",
    "                arrowhead = 3,\n",
    "                arrowwidth=1.5,\n",
    "                )\n",
    "            )\n",
    "fig.add_annotation(dict(\n",
    "                x= x[2],\n",
    "                y= norms[2]+.4,\n",
    "                xref=\"x\", yref=\"y\",\n",
    "                text=\" \",\n",
    "                showarrow=True,\n",
    "                axref = \"x\", ayref='y',\n",
    "                ax= .8,\n",
    "                ay= 7.8,\n",
    "                arrowhead = 3,\n",
    "                arrowwidth=1.5,\n",
    "        yshift=0.2\n",
    "                )\n",
    "            )\n",
    "x2 = [ sum(flow.flow_outputs['minimizing_flow_step'][:i]) for i in \n",
    "    range(1,1+len(flow.flow_outputs['minimizing_flow_step']))]\n",
    "if 0:\n",
    "    fig.add_annotation(dict(\n",
    "                x= x[5]-.3,\n",
    "                y= norms[5]+.5,\n",
    "                xref=\"x\", yref=\"y\",\n",
    "                text=\" \",\n",
    "                showarrow=True,\n",
    "                axref = \"x\", ayref='y',\n",
    "                ax= .8,\n",
    "                ay= 7.8,\n",
    "                arrowhead = 3,\n",
    "                arrowwidth=1.5,\n",
    "                )\n",
    "            )\n",
    "for i, Z_name in enumerate(flow2.flow_outputs['strs_of_minimizer_Z']):\n",
    "     if i<7 or Z_name is not 'Canonical':\n",
    "            fig.add_annotation(dict(     x=x[i+1], y=norms2[i+1],\n",
    "                    text=Z_name,\n",
    "                             font=dict(\n",
    "            family='Courier New, monospace',\n",
    "            size=12\n",
    "        ),\n",
    "                    xref=\"x\", yref=\"y\",\n",
    "                    showarrow=True,\n",
    "                    axref = \"x\", ayref='y',\n",
    "                    ax= x[i+1]-(.9 if Z_name=='Canonical' else .6),\n",
    "                    ay= norms2[i+1],\n",
    "                    arrowhead = 3,\n",
    "                    arrowwidth=1.5,\n",
    "                    )\n",
    "                )\n",
    "i=7\n",
    "fig.add_annotation(dict(     x=x[i+1]-.5, y=norms2[i+1],\n",
    "text='...',\n",
    "     font=dict(\n",
    "family='Courier New, monospace',\n",
    "size=12\n",
    "),\n",
    "xref=\"x\", yref=\"y\",\n",
    "showarrow=False\n",
    ")\n",
    ")\n",
    "fig.update_xaxes(title_text=r'$\\text{Total flow duration } \\ell$', row =1, col = 1)\n",
    "fig.update_xaxes(title_text=r'$\\text{Flow step } k$', row =1, col = 2)\n",
    "fig.update_xaxes(title_text=r'$\\text{Flow step } k$', row =1, col = 3)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "fig.update_xaxes(showline=True, linewidth=1, linecolor='black', mirror=True)\n",
    "fig.update_yaxes(showline=True, linewidth=1, linecolor='black', mirror=True)\n",
    "fig.update_layout(margin=dict(l=56, r=0, b=0, t=26))\n",
    "fig.update_layout(\n",
    "    autosize=False,\n",
    "    width=800,\n",
    "    height=300)\n",
    "\n",
    "fig.show()\n",
    "print(flow2.flow_outputs['strs_of_minimizer_Z'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig.write_image( 'figs/simple_eigenstates_comparison_TFIM_J=2.pdf' )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Checks \n",
    "The landscape for 3 qubits is a bit unusual and exploring large flow steps can speed things up.  Above the brute force check found fairly large steps while binary search may get stuck in local minima, cf this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x288 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/marek/Projects/Quantum_decoupling_algorithm/numerical_code/class_double_bracket_flow.py:960: UserWarning:\n",
      "\n",
      "FixedFormatter should only be used together with FixedLocator\n",
      "\n",
      "/home/marek/Projects/Quantum_decoupling_algorithm/numerical_code/class_double_bracket_flow.py:987: UserWarning:\n",
      "\n",
      "FixedFormatter should only be used together with FixedLocator\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x288 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from class_double_bracket_flow import *\n",
    "\n",
    "\n",
    "flow3 = double_bracket_flow( H_TFIM )\n",
    "\n",
    "\n",
    "flow3.flow_step_max =1\n",
    "flow3.flow_step_min = 0\n",
    "flow3.nmb_search_points_minimizing_s_search = 20\n",
    "flow3.please_use_binary_search  =True #by default binary search is used\n",
    "\n",
    "flow3.nmb_flow_steps = 15\n",
    "flow3.flow_forwards( states = np.eye(2**L).tolist() )\n",
    "flow3.show_flow_forwards_results()\n",
    "flow3.show_flow_forwards_results_fancy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}