jupyter_notebooks/Examples/2QGST-ErrorBars.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# How to generate error bars for 2Q-GST\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Memory limit was =  3221225472\n",
      "Memory limit is now =  2684354560.0\n"
     ]
    }
   ],
   "source": [
    "import pygsti\n",
    "import pickle\n",
    "import time\n",
    "\n",
    "#If we were using MPI\n",
    "# from mpi4py import MPI\n",
    "# comm = MPI.COMM_WORLD\n",
    "comm = None\n",
    "\n",
    "#Load the 2-qubit results (if you don't have this file, run the 2Q-GST example)\n",
    "with open(\"example_files/easy_2q_results.pkl\",\"rb\") as f:\n",
    "    results = pickle.load(f)\n",
    "\n",
    "#Set a memory limit\n",
    "print(\"Memory limit was = \", results.estimates['default'].parameters.get('memLimit',\"none given\"))\n",
    "results.estimates['default'].parameters['memLimit'] = 2.5*(1024.0)**3 # 2.5GB\n",
    "print(\"Memory limit is now = \", results.estimates['default'].parameters['memLimit'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaltree generation (deriv) w/mem limit = 2.48GB\n",
      " mem(1 subtrees, 1,1 param-grps, 1 proc-grps) in 0s = 6773.17GB (6773.17GB fc)\n",
      "Created evaluation tree with 1 subtrees.  Will divide 1 procs into 1 (subtree-processing)\n",
      " groups of ~1 procs each, to distribute over (1616,1616) params (taken as 1616,4 param groups of ~1,404 params).\n",
      " Memory estimate = 2.08GB (cache=1317, wrtLen1=1, wrtLen2=404, subsPerProc=1).\n",
      "rank 0: 29.3325s: block 0/4043, sub-tree 0/1, sub-tree-len = 1317\n",
      "rank 0: 58.4175s: block 1/4043, sub-tree 0/1, sub-tree-len = 1317\n",
      "rank 0: 87.3489s: block 2/4043, sub-tree 0/1, sub-tree-len = 1317\n",
      "rank 0: 116.28s: block 3/4043, sub-tree 0/1, sub-tree-len = 1317\n",
      "rank 0: 146.016s: block 4/4043, sub-tree 0/1, sub-tree-len = 1317\n",
      "rank 0: 175.125s: block 5/4043, sub-tree 0/1, sub-tree-len = 1317\n",
      "rank 0: 204.208s: block 6/4043, sub-tree 0/1, sub-tree-len = 1317\n",
      "rank 0: 233.866s: block 7/4043, sub-tree 0/1, sub-tree-len = 1317\n",
      "rank 0: 262.991s: block 8/4043, sub-tree 0/1, sub-tree-len = 1317\n",
      "rank 0: 292.132s: block 9/4043, sub-tree 0/1, sub-tree-len = 1317\n",
      "rank 0: 321.038s: block 10/4043, sub-tree 0/1, sub-tree-len = 1317\n",
      "rank 0: 349.834s: block 11/4043, sub-tree 0/1, sub-tree-len = 1317\n",
      "rank 0: 378.618s: block 12/4043, sub-tree 0/1, sub-tree-len = 1317\n",
      "rank 0: 407.43s: block 13/4043, sub-tree 0/1, sub-tree-len = 1317\n",
      "rank 0: 436.11s: block 14/4043, sub-tree 0/1, sub-tree-len = 1317\n",
      "rank 0: 464.901s: block 15/4043, sub-tree 0/1, sub-tree-len = 1317\n",
      "rank 0: 493.786s: block 16/4043, sub-tree 0/1, sub-tree-len = 1317\n",
      "rank 0: 522.456s: block 17/4043, sub-tree 0/1, sub-tree-len = 1317\n",
      "rank 0: 551.266s: block 18/4043, sub-tree 0/1, sub-tree-len = 1317\n",
      "rank 0: 579.995s: block 19/4043, sub-tree 0/1, sub-tree-len = 1317\n",
      "rank 0: 608.664s: block 20/4043, sub-tree 0/1, sub-tree-len = 1317\n",
      "rank 0: 637.449s: block 21/4043, sub-tree 0/1, sub-tree-len = 1317\n",
      "rank 0: 666.115s: block 22/4043, sub-tree 0/1, sub-tree-len = 1317\n",
      "rank 0: 694.955s: block 23/4043, sub-tree 0/1, sub-tree-len = 1317\n",
      "rank 0: 723.691s: block 24/4043, sub-tree 0/1, sub-tree-len = 1317\n",
      "rank 0: 752.808s: block 25/4043, sub-tree 0/1, sub-tree-len = 1317\n",
      "rank 0: 781.978s: block 26/4043, sub-tree 0/1, sub-tree-len = 1317\n",
      "rank 0: 811.188s: block 27/4043, sub-tree 0/1, sub-tree-len = 1317\n",
      "rank 0: 840.21s: block 28/4043, sub-tree 0/1, sub-tree-len = 1317\n",
      "rank 0: 869.157s: block 29/4043, sub-tree 0/1, sub-tree-len = 1317\n",
      "rank 0: 898.371s: block 30/4043, sub-tree 0/1, sub-tree-len = 1317\n",
      "rank 0: 927.436s: block 31/4043, sub-tree 0/1, sub-tree-len = 1317\n",
      "rank 0: 957s: block 32/4043, sub-tree 0/1, sub-tree-len = 1317\n",
      "rank 0: 986.078s: block 33/4043, sub-tree 0/1, sub-tree-len = 1317\n",
      "rank 0: 1015.71s: block 34/4043, sub-tree 0/1, sub-tree-len = 1317\n",
      "rank 0: 1044.65s: block 35/4043, sub-tree 0/1, sub-tree-len = 1317\n",
      "rank 0: 1073.76s: block 36/4043, sub-tree 0/1, sub-tree-len = 1317\n",
      "rank 0: 1102.93s: block 37/4043, sub-tree 0/1, sub-tree-len = 1317\n",
      "rank 0: 1132.23s: block 38/4043, sub-tree 0/1, sub-tree-len = 1317\n",
      "rank 0: 1161.42s: block 39/4043, sub-tree 0/1, sub-tree-len = 1317\n",
      "rank 0: 1191.09s: block 40/4043, sub-tree 0/1, sub-tree-len = 1317\n",
      "rank 0: 1220.73s: block 41/4043, sub-tree 0/1, sub-tree-len = 1317\n",
      "rank 0: 1250.07s: block 42/4043, sub-tree 0/1, sub-tree-len = 1317\n",
      "rank 0: 1279.96s: block 43/4043, sub-tree 0/1, sub-tree-len = 1317\n",
      "rank 0: 1309.22s: block 44/4043, sub-tree 0/1, sub-tree-len = 1317\n",
      "rank 0: 1339.77s: block 45/4043, sub-tree 0/1, sub-tree-len = 1317\n",
      "rank 0: 1368.74s: block 46/4043, sub-tree 0/1, sub-tree-len = 1317\n",
      "rank 0: 1397.34s: block 47/4043, sub-tree 0/1, sub-tree-len = 1317\n",
      "rank 0: 1426.26s: block 48/4043, sub-tree 0/1, sub-tree-len = 1317\n",
      "rank 0: 1455.08s: block 49/4043, sub-tree 0/1, sub-tree-len = 1317\n",
      "rank 0: 1483.91s: block 50/4043, sub-tree 0/1, sub-tree-len = 1317\n",
      "rank 0: 1512.55s: block 51/4043, sub-tree 0/1, sub-tree-len = 1317\n",
      "rank 0: 1541.71s: block 52/4043, sub-tree 0/1, sub-tree-len = 1317\n",
      "rank 0: 1571.52s: block 53/4043, sub-tree 0/1, sub-tree-len = 1317\n",
      "rank 0: 1601.31s: block 54/4043, sub-tree 0/1, sub-tree-len = 1317\n",
      "rank 0: 1631.02s: block 55/4043, sub-tree 0/1, sub-tree-len = 1317\n",
      "rank 0: 1660.61s: block 56/4043, sub-tree 0/1, sub-tree-len = 1317\n",
      "rank 0: 1690.12s: block 57/4043, sub-tree 0/1, sub-tree-len = 1317\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-2-7208f561b6ac>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;31m# initialize a factory for the 'go0' gauge optimization within the 'default' estimate\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0mcrfact\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mresults\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mestimates\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'default'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_confidence_region_factory\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'go0'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'final'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m \u001b[0mcrfact\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompute_hessian\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcomm\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcomm\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m#optionally use multiple processors\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     10\u001b[0m \u001b[0mcrfact\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mproject_hessian\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'intrinsic error'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/research/pyGSTi/packages/pygsti/objects/confidenceregionfactory.py\u001b[0m in \u001b[0;36mcompute_hessian\u001b[0;34m(self, comm, memLimit, approximate)\u001b[0m\n\u001b[1;32m    239\u001b[0m                                  \u001b[0mminProbClip\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprobClipInterval\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mradius\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    240\u001b[0m                                  \u001b[0mcomm\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcomm\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemLimit\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmemLimit\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbosity\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvb\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 241\u001b[0;31m                                  opLabelAliases=aliases)\n\u001b[0m\u001b[1;32m    242\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    243\u001b[0m             nonMarkRadiusSq = max( 2*(_tools.logl_max(model, dataset)\n",
      "\u001b[0;32m~/research/pyGSTi/packages/pygsti/tools/likelihoodfns.py\u001b[0m in \u001b[0;36mlogl_hessian\u001b[0;34m(model, dataset, circuit_list, minProbClip, probClipInterval, radius, poissonPicture, check, comm, memLimit, opLabelAliases, smartc, verbosity)\u001b[0m\n\u001b[1;32m    733\u001b[0m         \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mkmax\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmySliceTupList\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    734\u001b[0m         for (slice1,slice2,hprobs,dprobs12) in model.bulk_hprobs_by_block(\n\u001b[0;32m--> 735\u001b[0;31m             evalSubTree, mySliceTupList, True, blkComm):\n\u001b[0m\u001b[1;32m    736\u001b[0m             \u001b[0mrank\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcomm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mGet_rank\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mcomm\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    737\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/research/pyGSTi/packages/pygsti/objects/matrixforwardsim.py\u001b[0m in \u001b[0;36mbulk_hprobs_by_block\u001b[0;34m(self, evalTree, wrtSlicesList, bReturnDProbs12, comm)\u001b[0m\n\u001b[1;32m   2705\u001b[0m             \u001b[0;31m#Fill arrays\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2706\u001b[0m             self._fill_result_tuple((None, dprobs1, dprobs2, hprobs), evalTree,\n\u001b[0;32m-> 2707\u001b[0;31m                                     slice(None), slice(None), calc_and_fill)\n\u001b[0m\u001b[1;32m   2708\u001b[0m             \u001b[0mhProdCache\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhGs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdProdCache2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdGs2\u001b[0m \u001b[0;34m=\u001b[0m  \u001b[0;32mNone\u001b[0m \u001b[0;31m# free mem\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2709\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mbReturnDProbs12\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/research/pyGSTi/packages/pygsti/objects/forwardsim.py\u001b[0m in \u001b[0;36m_fill_result_tuple\u001b[0;34m(self, result_tup, evalTree, param_slice1, param_slice2, calc_and_fill_fn)\u001b[0m\n\u001b[1;32m    524\u001b[0m             \u001b[0;31m#          all of the raw operation sequences which need to be computed\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    525\u001b[0m             \u001b[0;31m#          for the current spamTuple (this list has the SAME length as fInds).\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 526\u001b[0;31m             \u001b[0mcalc_and_fill_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mspamTuple\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mfInds\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mgInds\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mpslc1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mpslc2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m#TODO: remove SumInto == True cases\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    527\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    528\u001b[0m         \u001b[0;32mreturn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/research/pyGSTi/packages/pygsti/objects/matrixforwardsim.py\u001b[0m in \u001b[0;36mcalc_and_fill\u001b[0;34m(spamTuple, fInds, gInds, pslc1, pslc2, sumInto)\u001b[0m\n\u001b[1;32m   2652\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mderiv2MxToFill\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2653\u001b[0m                 _fas(deriv2MxToFill, [fInds,pslc2], self._dprobs_from_rhoE( \n\u001b[0;32m-> 2654\u001b[0;31m                     spamTuple, rho, E, Gs[gInds], dGs2[gInds], scaleVals[gInds], wrtSlice2), add=sumInto)\n\u001b[0m\u001b[1;32m   2655\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2656\u001b[0m             _fas(mxToFill, [fInds,pslc1,pslc2], self._hprobs_from_rhoE( \n",
      "\u001b[0;32m~/research/pyGSTi/packages/pygsti/objects/matrixforwardsim.py\u001b[0m in \u001b[0;36m_dprobs_from_rhoE\u001b[0;34m(self, spamTuple, rho, E, Gs, dGs, scaleVals, wrtSlice)\u001b[0m\n\u001b[1;32m   1677\u001b[0m         \u001b[0;31m# dp_dOps      = squeeze( dot( E, dot( dGs, rho ) ), axis=(0,3))\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1678\u001b[0m         \u001b[0mold_err2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_np\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mseterr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minvalid\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'ignore'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mover\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'ignore'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1679\u001b[0;31m         \u001b[0mdp_dOps\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_np\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msqueeze\u001b[0m\u001b[0;34m(\u001b[0m \u001b[0m_np\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m \u001b[0mE\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_np\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m \u001b[0mdGs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrho\u001b[0m \u001b[0;34m)\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mscaleVals\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1680\u001b[0m         \u001b[0m_np\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mseterr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mold_err2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1681\u001b[0m            \u001b[0;31m# may overflow, but OK ; shape == (len(circuit_list), nDerivCols)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "# error bars in reports require the presence of a fully-initialized\n",
    "# \"confidence region factory\" within the relevant Estimate object.\n",
    "# In most cases \"fully-initialized\" means that a Hessian has been \n",
    "# computed and projected onto the non-gauge space.\n",
    "start = time.time()\n",
    "\n",
    "# initialize a factory for the 'go0' gauge optimization within the 'default' estimate\n",
    "crfact = results.estimates['default'].add_confidence_region_factory('go0', 'final')\n",
    "crfact.compute_hessian(comm=comm) #optionally use multiple processors\n",
    "crfact.project_hessian('intrinsic error')\n",
    "\n",
    "end = time.time()\n",
    "print(\"Total time=%f hours\" % ((end - start) / 3600.0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note above cell was executed for demonstration purposes, and was **keyboard-interrupted intentionally** since it would have taken forever on a single processor."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#write results back to file\n",
    "with open(\"example_files/easy_2q_results_withCI.pkl\",\"wb\") as f:\n",
    "    pickle.dump(results, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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