Update RE application samples to use new features in Q# and azure_qua…

…ntum (#811)

* Use new Q# operation.

* Use new summary table.

* Use constraints and summary table in dynamics sample.

* Summary table.

* Explain logical_depth_factor.

* Add link to paper.

* Address comments.

samples/azure-quantum/resource-estimation/estimation-chemistry.ipynb

@@ -188,7 +188,7 @@
    "source": [
     "## Analyzing the results\n",
     "\n",
-    "Now that the results have been computed, we display them in a summary table. For this purpose we are creating a reusable `dashboard` function that is creating an HTML display from a pandas data frame and the resource estimation tables."
+    "Finally, we are presenting the experimental results using a summary table."
    ]
   },
   {
@@ -199,52 +199,7 @@
    "source": [
     "labels = [\"Gate-based µs, 10⁻³\", \"Gate-based µs, 10⁻⁴\", \"Gate-based ns, 10⁻³\", \"Gate-based ns, 10⁻⁴\", \"Majorana ns, 10⁻⁴\", \"Majorana ns, 10⁻⁶\"]\n",
     "\n",
-    "def dashboard(results):\n",
-    "    def get_row(result):\n",
-    "        # Extract raw data from result dictionary\n",
-    "        logical_qubits = result[\"physicalCounts\"][\"breakdown\"][\"algorithmicLogicalQubits\"]\n",
-    "        logical_depth = result[\"physicalCounts\"][\"breakdown\"][\"logicalDepth\"]\n",
-    "        num_tstates = result[\"physicalCounts\"][\"breakdown\"][\"numTstates\"]\n",
-    "        code_distance = result[\"logicalQubit\"][\"codeDistance\"]\n",
-    "        num_tfactories = result[\"physicalCounts\"][\"breakdown\"][\"numTfactories\"]\n",
-    "        tfactory_fraction = (result[\"physicalCounts\"][\"breakdown\"][\"physicalQubitsForTfactories\"] / result[\"physicalCounts\"][\"physicalQubits\"]) * 100\n",
-    "        physical_qubits = result[\"physicalCounts\"][\"physicalQubits\"]\n",
-    "        runtime = result[\"physicalCounts\"][\"runtime\"]\n",
-    "\n",
-    "        # Format some entries\n",
-    "        logical_depth_formatted = f\"{logical_depth:.1e}\"\n",
-    "        num_tstates_formatted = f\"{num_tstates:.1e}\"\n",
-    "        tfactory_fraction_formatted = f\"{tfactory_fraction:.1f}%\"\n",
-    "        physical_qubits_formatted = f\"{physical_qubits / 1e6:.2f}M\"\n",
-    "\n",
-    "        # Make runtime human readable; we find the largest units for which the\n",
-    "        # runtime has a value that is larger than 1.0.  For that unit we are\n",
-    "        # rounding the value and append the unit suffix.\n",
-    "        units = [(\"nanosecs\", 1), (\"microsecs\", 1000), (\"millisecs\", 1000), (\"secs\", 1000), (\"mins\", 60), (\"hours\", 60), (\"days\", 24), (\"years\", 365)]\n",
-    "        runtime_formatted = runtime\n",
-    "        for idx in range(1, len(units)):\n",
-    "            if runtime_formatted / units[idx][1] < 1.0:\n",
-    "                runtime_formatted = f\"{round(runtime_formatted) % units[idx][1]} {units[idx - 1][0]}\"\n",
-    "                break\n",
-    "            else:\n",
-    "                runtime_formatted = runtime_formatted / units[idx][1]\n",
-    "\n",
-    "        # special case for years\n",
-    "        if isinstance(runtime_formatted, float):\n",
-    "            runtime_formatted = f\"{round(runtime_formatted)} {units[-1][0]}\"\n",
-    "\n",
-    "        # Append all extracted and formatted data to data array\n",
-    "        return (logical_qubits, logical_depth_formatted, num_tstates_formatted, code_distance, num_tfactories, tfactory_fraction_formatted, physical_qubits_formatted, runtime_formatted)\n",
-    "\n",
-    "    data = [get_row(results.data(index)) for index in range(len(results))]\n",
-    "\n",
-    "    # Create data frame with explicit column names and configuration names extracted from array\n",
-    "    import pandas as pd\n",
-    "    df = pd.DataFrame(data, columns=[\"Logical qubits\", \"Logical depth\", \"T states\", \"Code distance\", \"T factories\", \"T factory fraction\", \"Physical qubits\", \"Physical runtime\"], index=labels)\n",
-    "\n",
-    "    return df\n",
-    "\n",
-    "dashboard(results)"
+    "results.summary_data_frame(labels=labels)"
    ]
   },
   {
@@ -303,6 +258,12 @@
    "source": [
     "In this notebook, you've estimated the quantum computing requirements to calculate the energy of a Hamiltonian. Nice job! 👏🏽\n",
     "\n",
+    "The numbers for the XVIII-cas4-fb-64e-56o instance roughly match the numbers in\n",
+    "the paper [Assessing requirements for scaling quantum computers to real-world\n",
+    "impact](https://aka.ms/AQ/RE/Paper), as we incorporated a few improvements in\n",
+    "the implementation of the double-factorized chemistry algorithm as compared to\n",
+    "the version used when the paper was published.\n",
+    "\n",
     "We hope that this notebook was helpful to you.  Here are some suggestions for next steps:\n",
     "* Try to estimate some custom FCIDUMP files\n",
     "* Investigate the details of resource estimation by exploring the detailed resource estimation tables\n",
@@ -327,10 +288,10 @@
   }
  ],
  "metadata": {
-    "language_info": {
-     "name": "python"
-    }
-   },
-   "nbformat": 4,
-   "nbformat_minor": 1
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
 }