jupyter_notebooks/Examples/GOpt-AddingNewOptimizations.ipynb

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    "# How to add new gauge-optimizations to GST results\n",
    "This example demonstrates how to take a previously computed `Results` object and add new gauge-optimized version of to one of the estimates.  First, let's \"pre-compute\" a `Results` object using `do_long_sequence_gst`, which contains a single `Estimate` called \"default\":"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from __future__ import print_function\n",
    "import pygsti, pickle\n",
    "from pygsti.construction import std1Q_XYI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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     "text": [
      "--- Circuit Creation ---\n",
      "--- LGST ---\n",
      "--- Iterative MLGST: [##################################################] 100.0%  450 operation sequences ---\n",
      "Iterative MLGST Total Time: 1.0s\n",
      "--- Re-optimizing logl after robust data scaling ---\n"
     ]
    }
   ],
   "source": [
    "#Generate some fake data and run GST on it.\n",
    "target_model = std1Q_XYI.target_model()\n",
    "mdl_datagen  = std1Q_XYI.target_model().depolarize(op_noise=0.1, spam_noise=0.001)\n",
    "listOfExperiments = pygsti.construction.make_lsgst_experiment_list(\n",
    "    target_model, std1Q_XYI.fiducials, std1Q_XYI.fiducials, std1Q_XYI.germs, [1,2,4])\n",
    "ds = pygsti.construction.generate_fake_data(mdl_datagen, listOfExperiments, nSamples=1000,\n",
    "                                            sampleError=\"binomial\", seed=1234)\n",
    "target_model.set_all_parameterizations(\"TP\")\n",
    "results = pygsti.do_long_sequence_gst(\n",
    "    ds, target_model, std1Q_XYI.fiducials, std1Q_XYI.fiducials, std1Q_XYI.germs, [1,2,4],\n",
    "    gaugeOptParams={'itemWeights': {'gates': 1, 'spam': 1}}, verbosity=1)\n",
    "\n",
    "with open(\"example_files/regaugeopt_result.pkl\",\"wb\") as f:\n",
    "    pickle.dump(results, f) # pickle the results, to mimic typical workflow"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, let's load in the pre-computed results and use the `add_gauge_optimization` method of the `pygsti.objects.Estimate` object to add a new gauge-optimized version of the (gauge un-fixed) model estimate stored in `my_results.estimates['default']`.  The first argument of `add_gauge_optimization` is just a dictionary of arguments to `pygsti.gaugeopt_to_target` **except** that you don't need to specify the `Model` to gauge optimize or the target `Model` (just like the `gaugeOptParams` argument of `do_long_sequence_gst`).  The optional \"`label`\" argument defines the key name for the gauge-optimized `Model` and the corresponding parameter dictionary within the `Estimate`'s `.models` and `.goparameters` dictionaries, respectively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_results = pickle.load(open(\"example_files/regaugeopt_result.pkl\",\"rb\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['go0', 'Spam 1e-3']\n",
      "0.039998560434501076\n"
     ]
    }
   ],
   "source": [
    "estimate = my_results.estimates['default']\n",
    "estimate.add_gaugeoptimized( {'itemWeights': {'gates': 1, 'spam': 0.001}}, label=\"Spam 1e-3\" )\n",
    "mdl_gaugeopt = estimate.models['Spam 1e-3']\n",
    "\n",
    "print(list(estimate.goparameters.keys())) # 'go0' is the default gauge-optimization label\n",
    "print(mdl_gaugeopt.frobeniusdist(estimate.models['target']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One can also perform the gauge optimization separately and specify it using the `model` argument (this is useful when you want or need to compute the gauge optimization elsewhere):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['go0', 'Spam 1e-3', 'Spam 1e-3 custom']\n",
      "0.0\n"
     ]
    }
   ],
   "source": [
    "mdl_unfixed = estimate.models['final iteration estimate']\n",
    "mdl_gaugefixed = pygsti.gaugeopt_to_target(mdl_unfixed, estimate.models['target'], {'gates': 1, 'spam': 0.001})\n",
    "estimate.add_gaugeoptimized( {'any': \"dictionary\", \n",
    "                              \"doesn't really\": \"matter\",\n",
    "                              \"but could be useful it you put gaugeopt params\": 'here'},\n",
    "                            model=mdl_gaugefixed, label=\"Spam 1e-3 custom\" )\n",
    "print(list(estimate.goparameters.keys()))\n",
    "print(estimate.models['Spam 1e-3 custom'].frobeniusdist(estimate.models['Spam 1e-3']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can look at the gauge optimization parameters using `.goparameters`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'_gaugeGroupEl': <pygsti.objects.gaugegroup.TPGaugeGroupElement object at 0x11c4c7f60>,\n",
      " 'itemWeights': {'gates': 1, 'spam': 0.001},\n",
      " 'model': <pygsti.objects.explicitmodel.ExplicitOpModel object at 0x11ebf2b38>,\n",
      " 'returnAll': True,\n",
      " 'targetModel': <pygsti.objects.explicitmodel.ExplicitOpModel object at 0x11ec014e0>}\n"
     ]
    }
   ],
   "source": [
    "import pprint\n",
    "pp = pprint.PrettyPrinter()\n",
    "pp.pprint(dict(estimate.goparameters['Spam 1e-3']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, note that if, in the original call to `do_long_sequence_gst`, you set **`gaugeOptParams=False`** then no gauge optimizations are performed (there would be no \"`go0`\" elements) and you start with a blank slate to perform whatever gauge optimizations you want on your own."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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