From d2fc746c329bb3f9dfdf67f52092435586becc95 Mon Sep 17 00:00:00 2001
From: nick bronn <ntbronn@us.ibm.com>
Date: Wed, 23 Aug 2023 09:48:33 -0400
Subject: [PATCH 1/2] added ep06 coding with qiskit runtime notebook

---
 .../ep06-notebook.ipynb                       | 641 ++++++++++++++++++
 1 file changed, 641 insertions(+)
 create mode 100644 video-companions/coding-with-qiskit-runtime/ep06-notebook.ipynb

diff --git a/video-companions/coding-with-qiskit-runtime/ep06-notebook.ipynb b/video-companions/coding-with-qiskit-runtime/ep06-notebook.ipynb
new file mode 100644
index 0000000..a5486e7
--- /dev/null
+++ b/video-companions/coding-with-qiskit-runtime/ep06-notebook.ipynb
@@ -0,0 +1,641 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "89bc62cb",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from friends import retrieve_observable_and_ansatz\n",
+    "obsv, qc, params = retrieve_observable_and_ansatz()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "ecedcc78",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "PauliSumOp(SparsePauliOp(['IIII', 'XXXX', 'YXYX', 'IZXX', 'ZZXX', 'IIXX', 'ZIXX', 'XYXY', 'YYYY', 'IIIZ', 'XXZZ', 'XXIZ', 'IZIZ', 'IZZZ', 'ZZZZ', 'ZZIZ', 'IIZZ', 'ZIZZ', 'ZIIZ', 'XXZI', 'XXII', 'IZII', 'IZZI', 'ZZZI', 'ZZII', 'IIZI', 'ZIZI', 'ZIII'],\n",
+       "              coeffs=[-1.40343802e+01+0.j,  1.89666179e-02+0.j, -1.89666179e-02+0.j,\n",
+       " -1.89666179e-02+0.j,  1.89666179e-02+0.j,  1.89666179e-02+0.j,\n",
+       " -1.89666179e-02+0.j, -1.89666179e-02+0.j,  1.89666179e-02+0.j,\n",
+       " -1.60527381e-01+0.j,  5.30165769e-03+0.j,  5.30165769e-03+0.j,\n",
+       " -2.41584122e-01+0.j,  8.88184955e-02+0.j,  9.94831530e-02+0.j,\n",
+       " -8.88798376e-02+0.j,  8.58722654e-02+0.j,  8.88184955e-02+0.j,\n",
+       " -8.88184955e-02+0.j,  5.30165769e-03+0.j,  5.30165769e-03+0.j,\n",
+       "  2.41584122e-01+0.j, -8.88184955e-02+0.j, -8.88798376e-02+0.j,\n",
+       "  9.94831530e-02+0.j, -1.60527381e-01+0.j, -2.41584122e-01+0.j,\n",
+       "  2.41584122e-01+0.j]), coeff=1.0)"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "obsv"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "f1929ae7",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2294.16x367.889 with 1 Axes>"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "qc.draw('mpl', fold=-1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "8e3eba2f",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[0.19535462771567846, 12.167984734279953, 0.19535462771567846]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "params"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "e3834c1c",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from qiskit.primitives import Estimator\n",
+    "estimator = Estimator()\n",
+    "job = estimator.run(circuits=qc, observables=obsv, parameter_values=params)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "16c2e405",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "EstimatorResult(values=array([-14.60295835]), metadata=[{}])"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "job.result()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "e24eaf2b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results = []\n",
+    "results.append(job.result().values[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "9192b1dd",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from qiskit_ibm_runtime import Options\n",
+    "\n",
+    "options = Options()\n",
+    "options.optimization_level = 0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "362b71b6",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from qiskit_ibm_runtime import QiskitRuntimeService, Session, Estimator\n",
+    "\n",
+    "service = QiskitRuntimeService()\n",
+    "backend = service.get_backend('ibm_lagos')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "52fee343",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "with Session(service=service, backend=backend) as session:\n",
+    "    estimator = Estimator(options=options)\n",
+    "    job = estimator.run(circuits=qc, observables=obsv, parameter_values=params)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "279b27c6",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "EstimatorResult(values=array([-14.21913395]), metadata=[{'variance': 0.3198837586455653, 'shots': 4000}])"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "job.result()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "2942e3ef",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "results.append(job.result().values[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "429835b1",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "options.optimization_level = 2\n",
+    "options.transpilation.layout_method = \"sabre\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "60f72534",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Options(optimization_level=2, resilience_level=None, max_execution_time=None, transpilation=TranspilationOptions(skip_transpilation=False, initial_layout=None, layout_method='sabre', routing_method=None, approximation_degree=None), resilience=ResilienceOptions(noise_amplifier='TwoQubitAmplifier', noise_factors=(1, 3, 5), extrapolator='LinearExtrapolator'), execution=ExecutionOptions(shots=4000, init_qubits=True), environment=EnvironmentOptions(log_level='WARNING', callback=None, job_tags=[]), simulator=SimulatorOptions(noise_model=None, seed_simulator=None, coupling_map=None, basis_gates=None))"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "options"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "cb1e33e3",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "with Session(service=service, backend=backend) as session:\n",
+    "    estimator = Estimator(options=options)\n",
+    "    job = estimator.run(circuits=qc, observables=obsv, parameter_values=params)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "e79054ba",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "EstimatorResult(values=array([-14.53703338]), metadata=[{'variance': 0.11849432741875174, 'shots': 4000}])"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "job.result()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "cd9e3658",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "results.append(job.result().values[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "96c7b63a",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "options.optimization_level = 0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "cc63b626",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "options.resilience_level = 1\n",
+    "\n",
+    "with Session(service=service, backend=backend) as session:\n",
+    "    estimator = Estimator(options=options)\n",
+    "    job = estimator.run(circuits=qc, observables=obsv, parameter_values=params)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "7ee6b117",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "EstimatorResult(values=array([-14.54698572]), metadata=[{'variance': 0.16701960026941956, 'shots': 4000, 'readout_mitigation_num_twirled_circuits': 16, 'readout_mitigation_shots_calibration': 8192}])"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "job.result()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "6839233d",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "results.append(job.result().values[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "4f10f9fd",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "options.resilience_level = 2\n",
+    "options.resilience.noise_amplifier = 'TwoQubitAmplifier'\n",
+    "options.resilience.noise_factors = (1, 1.2, 1.5)\n",
+    "options.resilience.extrapolator = 'LinearExtrapolator'\n",
+    "\n",
+    "with Session(service=service, backend=backend) as session:\n",
+    "    estimator = Estimator(options=options)\n",
+    "    job = estimator.run(circuits=qc, observables=obsv, parameter_values=params)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "68f1ca01",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "EstimatorResult(values=array([-14.59176797]), metadata=[{'zne': {'noise_amplification': {'noise_amplifier': \"<TwoQubitAmplifier:{'noise_factor_relative_tolerance': 0.01, 'random_seed': None, 'sub_folding_option': 'from_first'}>\", 'noise_factors': [1, 1.2, 1.5], 'values': [-14.543479768105339, -14.512218600605587, -14.51305556999569], 'variance': [0.12659279703299403, 0.1419320289939639, 0.1640169091918978], 'shots': [4000, 4000, 4000]}, 'extrapolation': {'extrapolator': 'LinearExtrapolator'}}}])"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "job.result()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "ec339fef",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "results.append(job.result().values[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "55c21cae",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from datetime import datetime\n",
+    "\n",
+    "def interim_results_callback(job_id, result):\n",
+    "    now = datetime.now()\n",
+    "    print(now, \"*** Callback ***\", result, \"\\n\")\n",
+    "    \n",
+    "options.optimization_level = 0\n",
+    "options.execution.shots = 300\n",
+    "options.environment.callback = interim_results_callback"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "07a00854",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "options.resilience_level = 3\n",
+    "\n",
+    "with Session(service=service, backend=backend) as session:\n",
+    "    estimator = Estimator(options=options)\n",
+    "    job = estimator.run(circuits=qc, observables=obsv, parameter_values=params)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "64504a51",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "```\n",
+    "2023-03-16 18:59:00.897374 *** Callback *** {'unique_layers_detected': 3, 'total_layers': 70} \n",
+    "\n",
+    "2023-03-16 19:01:12.812642 *** Callback *** {'msg': 'Sampling overhead for layer 1/3: 1.0381497062307443'} \n",
+    "\n",
+    "2023-03-16 19:03:21.394775 *** Callback *** {'msg': 'Sampling overhead for layer 2/3: 1.0418104322947281'} \n",
+    "\n",
+    "2023-03-16 19:04:45.393501 *** Callback *** {'msg': 'Sampling overhead for layer 3/3: 1.0308667982144537'} \n",
+    "\n",
+    "2023-03-16 19:04:45.588625 *** Callback *** {'sampling_overhead_by_layer': [1.0308667982144537, 1.0418104322947281, 1.0381497062307443], 'unique_layers': 3, 'total_sampling_overhead': 1.4439176359094685} \n",
+    "\n",
+    "2023-03-16 19:10:00.282664 *** Callback *** {'values': [-15.004731920399502], 'metadata': [{'standard_error': 0.4063455807551261, 'confidence_interval': [-15.161583714947211, -14.847880125851793], 'confidence_level': 0.95, 'shots': 55424, 'samples': 433, 'sampling_overhead': 1.4439176359094685, 'total_mitigated_layers': 10}]} \n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "ac4aea23",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "EstimatorResult(values=array([-15.00473192]), metadata=[{'standard_error': 0.4063455807551261, 'confidence_interval': [-15.161583714947211, -14.847880125851793], 'confidence_level': 0.95, 'shots': 55424, 'samples': 433, 'sampling_overhead': 1.4439176359094685, 'total_mitigated_layers': 10}])"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "job.result()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "db3af8c8",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "results.append(job.result().values[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "e8cf5924",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Energy')"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "plt.plot(range(len(results)-1), results[1:], ls= ' ', marker='o')\n",
+    "plt.plot([0, len(results)-1], [results[0], results[0]], ls='--')\n",
+    "plt.xlabel('Mitigation Strategy')\n",
+    "plt.ylabel('Energy')"
+   ]
+  }
+ ],
+ "metadata": {
+  "celltoolbar": "Slideshow",
+  "kernelspec": {
+   "display_name": "qiskit (stable)",
+   "language": "python",
+   "name": "qiskit-stable"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.13"
+  },
+  "rise": {
+   "theme": "monokai",
+   "transition": "none"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From c34edd596a1da9c6387ae9fa9ccc5c46b9b504dd Mon Sep 17 00:00:00 2001
From: nick bronn <ntbronn@us.ibm.com>
Date: Wed, 30 Aug 2023 21:27:08 -0400
Subject: [PATCH 2/2] added e07 notebook and vqd energies

---
 .../energies-vqd.json                         |    1 +
 .../ep07-notebook.ipynb                       | 3586 +++++++++++++++++
 2 files changed, 3587 insertions(+)
 create mode 100644 video-companions/coding-with-qiskit-runtime/energies-vqd.json
 create mode 100644 video-companions/coding-with-qiskit-runtime/ep07-notebook.ipynb

diff --git a/video-companions/coding-with-qiskit-runtime/energies-vqd.json b/video-companions/coding-with-qiskit-runtime/energies-vqd.json
new file mode 100644
index 0000000..72be020
--- /dev/null
+++ b/video-companions/coding-with-qiskit-runtime/energies-vqd.json
@@ -0,0 +1 @@
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-0.5588333284787621}, {"288": 0.17816272785263498}, {"289": -0.31441593775691057}, {"290": -0.41681618048121893}, {"291": -0.369277459784218}, {"292": -0.3641713720752008}, {"293": -0.6974976336142495}, {"294": 0.20482284676286866}, {"295": -0.7296862432358077}, {"296": -0.7201382292028409}, {"297": -0.37393667556180277}, {"298": -0.6047002865187845}, {"299": -0.2199790967333327}, {"300": 0.10015600229259869}, {"301": 0.4807656559786795}, {"302": 0.816019389352082}, {"303": 0.13955236124971737}, {"304": -0.21969169859170845}, {"305": 0.4765123777688659}, {"306": -0.0765967310470348}, {"307": 1.758590257461464}, {"308": 3.3159268212076536}, {"309": 0.4112797378459614}, {"310": -0.5900445845148359}, {"311": -0.41683514039283365}, {"312": -0.5251189591458569}, {"313": -0.5187027724015312}, {"314": -0.07061416887681649}, {"315": -0.7777090286472124}, {"316": -0.2598090989216941}, {"317": -0.25734825872527956}, {"318": -0.11448908085407805}, {"319": -0.31077941789832564}, {"320": -0.13324327025926763}, {"321": -0.10950112680310153}, {"322": -0.3120390452470355}, {"323": 0.06791424845978389}, {"324": -0.5262833342603914}, {"325": -0.2864569430600228}, {"326": 0.4537719850397184}, {"327": -0.5816905968748617}, {"328": 2.0513298823180097}, {"329": 1.4962052332292886}, {"330": 2.858500126499419}, {"331": 2.208791010678465}, {"332": 2.5065416706583044}, {"333": 1.4541251403413418}]}
\ No newline at end of file
diff --git a/video-companions/coding-with-qiskit-runtime/ep07-notebook.ipynb b/video-companions/coding-with-qiskit-runtime/ep07-notebook.ipynb
new file mode 100644
index 0000000..657d8d9
--- /dev/null
+++ b/video-companions/coding-with-qiskit-runtime/ep07-notebook.ipynb
@@ -0,0 +1,3586 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "0a9a4370",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "SparsePauliOp(['II', 'IZ', 'ZI', 'ZZ', 'XX'],\n",
+      "              coeffs=[-1.05237325+0.j,  0.39793742+0.j, -0.39793742+0.j, -0.0112801 +0.j,\n",
+      "  0.1809312 +0.j])\n"
+     ]
+    }
+   ],
+   "source": [
+    "from hydrogen import pauli_operator\n",
+    "\n",
+    "H2_op = pauli_operator()\n",
+    "print(H2_op)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "74c240bf",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[-1.85727503 -1.24458455 -0.88272215 -0.22491125]\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "eigens_exact = np.linalg.eigvalsh(H2_op.to_matrix())\n",
+    "print(eigens_exact)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "56b21f8d",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from qiskit.algorithms.optimizers import SPSA \n",
+    "from qiskit.algorithms.state_fidelities import ComputeUncompute\n",
+    "from qiskit.circuit.library import EfficientSU2\n",
+    "from qiskit.primitives import Sampler, Estimator\n",
+    "\n",
+    "ansatz = EfficientSU2(2)\n",
+    "sampler = Sampler()\n",
+    "estimator = Estimator()\n",
+    "fidelity = ComputeUncompute(sampler)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "e42fefb2",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "callback = lambda nfev, params, fval, step, acc: print(f'Evaluation {nfev}: {fval}')\n",
+    "optimizer = SPSA(callback=callback, maxiter=1000)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "4aa2a0d4",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Evaluation 3: -1.0685834202466682\n",
+      "Evaluation 6: -1.0422844887620013\n",
+      "Evaluation 9: -1.167572537942755\n",
+      "Evaluation 12: -1.1919776495025491\n",
+      "Evaluation 15: -1.5348071875529823\n",
+      "Evaluation 18: -1.4938870528555046\n",
+      "Evaluation 21: -1.1583543920390789\n",
+      "Evaluation 24: -0.8676592998454092\n",
+      "Evaluation 27: -0.8732921226825441\n",
+      "Evaluation 30: -1.050351709049314\n",
+      "Evaluation 33: -1.5129948759968552\n",
+      "Evaluation 36: -1.6068141992888052\n",
+      "Evaluation 39: -1.4586152403120407\n",
+      "Evaluation 42: -1.2892206942677986\n",
+      "Evaluation 45: -1.3190087402842623\n",
+      "Evaluation 48: -1.34114226600988\n",
+      "Evaluation 51: -1.1524041513017884\n",
+      "Evaluation 54: -0.9282231193549484\n",
+      "Evaluation 57: -1.4708121111577275\n",
+      "Evaluation 60: -1.0832607765656608\n",
+      "Evaluation 63: -1.2037091753515081\n",
+      "Evaluation 66: -1.0130353694398704\n",
+      "Evaluation 69: -0.7924239114473179\n",
+      "Evaluation 72: -0.973901578824278\n",
+      "Evaluation 75: -0.9957492548563529\n",
+      "Evaluation 78: -1.2044225264319042\n",
+      "Evaluation 81: -1.2596164531578997\n",
+      "Evaluation 84: -1.259405001211938\n",
+      "Evaluation 87: -1.3297826898694223\n",
+      "Evaluation 90: -1.2415715914445968\n",
+      "Evaluation 93: -1.2424405175578974\n",
+      "Evaluation 96: -1.4566669508380472\n",
+      "Evaluation 99: -1.5132804051986894\n",
+      "Evaluation 102: -1.5595471404082553\n",
+      "Evaluation 105: -1.5588839664635918\n",
+      "Evaluation 108: -1.540656108641205\n",
+      "Evaluation 111: -1.538036051272365\n",
+      "Evaluation 114: -1.521697628793966\n",
+      "Evaluation 117: -1.53010579474387\n",
+      "Evaluation 120: -1.533493795674062\n",
+      "Evaluation 123: -1.5309880732174976\n",
+      "Evaluation 126: -1.528463029683273\n",
+      "Evaluation 129: -1.5565128119797653\n",
+      "Evaluation 132: -1.487623065727689\n",
+      "Evaluation 135: -1.4753717034913911\n",
+      "Evaluation 138: -1.4856548509621788\n",
+      "Evaluation 141: -1.5690124356524113\n",
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+      "Evaluation 147: -1.5663212183920772\n",
+      "Evaluation 150: -1.5693606396528892\n",
+      "Evaluation 153: -1.6340339857120483\n",
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+      "Evaluation 174: -1.6981455746949639\n",
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+      "Evaluation 2169: -1.2329196014790957\n",
+      "Evaluation 2172: -1.2331193629232278\n",
+      "Evaluation 2175: -1.2330888644632887\n",
+      "Evaluation 2178: -1.2348586973422337\n",
+      "Evaluation 2181: -1.2349040149295338\n",
+      "Evaluation 2184: -1.235672377203704\n",
+      "Evaluation 2187: -1.235727767269194\n",
+      "Evaluation 2190: -1.2357823822952687\n",
+      "Evaluation 2193: -1.2354967311574823\n",
+      "Evaluation 2196: -1.235444849868215\n",
+      "Evaluation 2199: -1.2357561707603544\n",
+      "Evaluation 2202: -1.235114599632585\n",
+      "Evaluation 2205: -1.235037276826327\n",
+      "Evaluation 2208: -1.2358985946891303\n",
+      "Evaluation 2211: -1.2362368175421004\n",
+      "Evaluation 2214: -1.236004350892963\n",
+      "Evaluation 2217: -1.2363273786122972\n",
+      "Evaluation 2220: -1.236294827542802\n",
+      "Evaluation 2223: -1.2363496141772554\n",
+      "Evaluation 2226: -1.2172759993341489\n",
+      "Evaluation 2229: -1.2173740459730418\n",
+      "Evaluation 2232: -1.219190518566874\n",
+      "Evaluation 2235: -1.2212950479254812\n",
+      "Evaluation 2238: -1.222824457629958\n",
+      "Evaluation 2241: -1.2235650275182566\n",
+      "Evaluation 2244: -1.231145523277105\n",
+      "Evaluation 2247: -1.2324345576255622\n",
+      "Evaluation 2250: -1.2332484005872448\n",
+      "Evaluation 2253: -1.2313891444106044\n",
+      "Evaluation 2256: -1.2323811635450264\n",
+      "Evaluation 2259: -1.2276335374323724\n",
+      "Evaluation 2262: -1.2287280291070712\n",
+      "Evaluation 2265: -1.2289376474142957\n",
+      "Evaluation 2268: -1.2295349253900485\n",
+      "Evaluation 2271: -1.2295366544983533\n",
+      "Evaluation 2274: -1.2329689544871134\n",
+      "Evaluation 2277: -1.2329693818984027\n",
+      "Evaluation 2280: -1.2320625307189672\n",
+      "Evaluation 2283: -1.2320277346594186\n",
+      "Evaluation 2286: -1.230818445515068\n",
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+      "Evaluation 2292: -1.2306846957568076\n",
+      "Evaluation 2295: -1.230680739190894\n",
+      "Evaluation 2298: -1.2304770288843685\n",
+      "Evaluation 2301: -1.2318238649562274\n",
+      "Evaluation 2304: -1.2321897093788814\n",
+      "Evaluation 2307: -1.2321390356953872\n",
+      "Evaluation 2310: -1.2319205870854737\n",
+      "Evaluation 2313: -1.2337744996135789\n",
+      "Evaluation 2316: -1.2336840985920248\n",
+      "Evaluation 2319: -1.2336690188548984\n",
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+      "Evaluation 2325: -1.2346170236551082\n",
+      "Evaluation 2328: -1.2348184013127264\n",
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+      "Evaluation 2337: -1.2352538146924072\n",
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+      "Evaluation 2358: -1.2353600790244352\n",
+      "Evaluation 2361: -1.2357998581886456\n",
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+      "Evaluation 2379: -1.2347544845394258\n",
+      "Evaluation 2382: -1.2349561264403417\n",
+      "Evaluation 2385: -1.2349573139278818\n",
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+      "Evaluation 2391: -1.2361778606586744\n",
+      "Evaluation 2394: -1.2361965068109584\n",
+      "Evaluation 2397: -1.2367598191445848\n",
+      "Evaluation 2400: -1.2373094711031574\n",
+      "Evaluation 2403: -1.237257334734367\n",
+      "Evaluation 2406: -1.2312389941739372\n",
+      "Evaluation 2409: -1.2312726329475712\n",
+      "Evaluation 2412: -1.2303112029279009\n",
+      "Evaluation 2415: -1.231473302061025\n",
+      "Evaluation 2418: -1.235590834401039\n",
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+      "Evaluation 2424: -1.236216712195367\n",
+      "Evaluation 2427: -1.2356407225163066\n",
+      "Evaluation 2430: -1.2355605115235486\n",
+      "Evaluation 2433: -1.2345196554208573\n",
+      "Evaluation 2436: -1.2351056984003\n",
+      "Evaluation 2439: -1.23508189139236\n",
+      "Evaluation 2442: -1.2351328346922967\n",
+      "Evaluation 2445: -1.2356179061012065\n",
+      "Evaluation 2448: -1.2352697044800753\n",
+      "Evaluation 2451: -1.235207344521842\n",
+      "Evaluation 2454: -1.2383693023839941\n",
+      "Evaluation 2457: -1.2389383009389088\n",
+      "Evaluation 2460: -1.239069799353309\n",
+      "Evaluation 2463: -1.2404128112929704\n",
+      "Evaluation 2466: -1.2403027811496372\n",
+      "Evaluation 2469: -1.2402718601262823\n",
+      "Evaluation 2472: -1.2392001424242465\n",
+      "Evaluation 2475: -1.2392670777904475\n",
+      "Evaluation 2478: -1.2402241668091747\n",
+      "Evaluation 2481: -1.2401244794978339\n",
+      "Evaluation 2484: -1.2407977548766962\n",
+      "Evaluation 2487: -1.241237057916336\n",
+      "Evaluation 2490: -1.2409057051694437\n",
+      "Evaluation 2493: -1.2418306982472003\n",
+      "Evaluation 2496: -1.2382575711690578\n",
+      "Evaluation 2499: -1.2387256788422183\n",
+      "Evaluation 2502: -1.2377063635714358\n",
+      "Evaluation 2505: -1.2377870214039959\n",
+      "Evaluation 2508: -1.239577448264046\n",
+      "Evaluation 2511: -1.240177162939022\n",
+      "Evaluation 2514: -1.2392329421136472\n",
+      "Evaluation 2517: -1.2392483550721085\n",
+      "Evaluation 2520: -1.240297345919281\n",
+      "Evaluation 2523: -1.2403466861643366\n",
+      "Evaluation 2526: -1.2409460284025269\n",
+      "Evaluation 2529: -1.2391337924927714\n",
+      "Evaluation 2532: -1.2391383009028898\n",
+      "Evaluation 2535: -1.2391351440310736\n",
+      "Evaluation 2538: -1.2391565665029547\n",
+      "Evaluation 2541: -1.2405185864689425\n",
+      "Evaluation 2544: -1.240844926580584\n",
+      "Evaluation 2547: -1.2408362537292237\n",
+      "Evaluation 2550: -1.241145945051929\n",
+      "Evaluation 2553: -1.24105848715534\n",
+      "Evaluation 2556: -1.2411273140964105\n",
+      "Evaluation 2559: -1.241389098327062\n",
+      "Evaluation 2562: -1.2413720835453526\n",
+      "Evaluation 2565: -1.2393875652164943\n",
+      "Evaluation 2568: -1.2399293122540684\n",
+      "Evaluation 2571: -1.2399394787270628\n",
+      "Evaluation 2574: -1.2400086077332042\n",
+      "Evaluation 2577: -1.2401026698089204\n",
+      "Evaluation 2580: -1.240145251281764\n",
+      "Evaluation 2583: -1.238604620072531\n",
+      "Evaluation 2586: -1.2425994612622215\n",
+      "Evaluation 2589: -1.242189435913417\n",
+      "Evaluation 2592: -1.242809640174365\n",
+      "Evaluation 2595: -1.2428255003143496\n",
+      "Evaluation 2598: -1.2421576030646573\n",
+      "Evaluation 2601: -1.2421050154020283\n",
+      "Evaluation 2604: -1.2417608480191653\n",
+      "Evaluation 2607: -1.2408092188469337\n",
+      "Evaluation 2610: -1.2400943139380212\n",
+      "Evaluation 2613: -1.2413099288208616\n",
+      "Evaluation 2616: -1.2413195222905882\n",
+      "Evaluation 2619: -1.2413773191690665\n",
+      "Evaluation 2622: -1.2407459485899088\n",
+      "Evaluation 2625: -1.2406010378967078\n",
+      "Evaluation 2628: -1.2410983427056672\n",
+      "Evaluation 2631: -1.2407272623611088\n",
+      "Evaluation 2634: -1.2417820002577231\n",
+      "Evaluation 2637: -1.2419634654225362\n",
+      "Evaluation 2640: -1.2425621426322766\n",
+      "Evaluation 2643: -1.2426157479353925\n",
+      "Evaluation 2646: -1.2427038752423292\n",
+      "Evaluation 2649: -1.241851732292443\n",
+      "Evaluation 2652: -1.2418178029562132\n",
+      "Evaluation 2655: -1.2412944826199248\n",
+      "Evaluation 2658: -1.242070428696488\n",
+      "Evaluation 2661: -1.2408127984086337\n",
+      "Evaluation 2664: -1.2415400969461214\n",
+      "Evaluation 2667: -1.2416139332746294\n",
+      "Evaluation 2670: -1.2415256047447494\n",
+      "Evaluation 2673: -1.2410207912456008\n",
+      "Evaluation 2676: -1.2412198247052786\n",
+      "Evaluation 2679: -1.2396812027134103\n",
+      "Evaluation 2682: -1.240397108673253\n",
+      "Evaluation 2685: -1.239664393383666\n",
+      "Evaluation 2688: -1.239828493508056\n",
+      "Evaluation 2691: -1.2404200393030718\n",
+      "Evaluation 2694: -1.241397483669605\n",
+      "Evaluation 2697: -1.2413428854419164\n",
+      "Evaluation 2700: -1.2428059854596238\n",
+      "Evaluation 2703: -1.2427748928588747\n",
+      "Evaluation 2706: -1.2427880153044368\n",
+      "Evaluation 2709: -1.2422332615150662\n",
+      "Evaluation 2712: -1.2424306322320586\n",
+      "Evaluation 2715: -1.2430703241731365\n",
+      "Evaluation 2718: -1.2327488819477692\n",
+      "Evaluation 2721: -1.2327863968417465\n",
+      "Evaluation 2724: -1.2368126013301324\n",
+      "Evaluation 2727: -1.2368088587749355\n",
+      "Evaluation 2730: -1.2423907342722007\n",
+      "Evaluation 2733: -1.2416607286052137\n",
+      "Evaluation 2736: -1.2419727497783326\n",
+      "Evaluation 2739: -1.242097839295897\n",
+      "Evaluation 2742: -1.242076041727482\n",
+      "Evaluation 2745: -1.242345274309447\n",
+      "Evaluation 2748: -1.2405770102560234\n",
+      "Evaluation 2751: -1.2408309927294432\n",
+      "Evaluation 2754: -1.240602287331878\n",
+      "Evaluation 2757: -1.2388141001369648\n",
+      "Evaluation 2760: -1.2412348474689534\n",
+      "Evaluation 2763: -1.2363967421576108\n",
+      "Evaluation 2766: -1.2382158406498778\n",
+      "Evaluation 2769: -1.2404995331614703\n",
+      "Evaluation 2772: -1.2413818945637602\n",
+      "Evaluation 2775: -1.2418183876297682\n",
+      "Evaluation 2778: -1.2412458242424025\n",
+      "Evaluation 2781: -1.2419994544469655\n",
+      "Evaluation 2784: -1.2420078530152416\n",
+      "Evaluation 2787: -1.2408384364934846\n",
+      "Evaluation 2790: -1.2409159563020693\n",
+      "Evaluation 2793: -1.2407035904066197\n",
+      "Evaluation 2796: -1.241007925060609\n",
+      "Evaluation 2799: -1.2406008154372477\n",
+      "Evaluation 2802: -1.2423260608221445\n",
+      "Evaluation 2805: -1.2424586472300962\n",
+      "Evaluation 2808: -1.2425684872734886\n",
+      "Evaluation 2811: -1.2425998810028422\n",
+      "Evaluation 2814: -1.2425893273174273\n",
+      "Evaluation 2817: -1.242382214564612\n",
+      "Evaluation 2820: -1.2423751951285729\n",
+      "Evaluation 2823: -1.2426200341317857\n",
+      "Evaluation 2826: -1.2426426457264885\n",
+      "Evaluation 2829: -1.2432726965218162\n",
+      "Evaluation 2832: -1.2430419180094134\n",
+      "Evaluation 2835: -1.2426665974039688\n",
+      "Evaluation 2838: -1.2426691967208021\n",
+      "Evaluation 2841: -1.2427752465842854\n",
+      "Evaluation 2844: -1.2428853353254137\n",
+      "Evaluation 2847: -1.242917152405584\n",
+      "Evaluation 2850: -1.242605326712891\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Evaluation 2853: -1.2422876343566835\n",
+      "Evaluation 2856: -1.2414308743894087\n",
+      "Evaluation 2859: -1.2414870095287438\n",
+      "Evaluation 2862: -1.2415283797408974\n",
+      "Evaluation 2865: -1.2414423019390288\n",
+      "Evaluation 2868: -1.2415329798572328\n",
+      "Evaluation 2871: -1.2412854494977166\n",
+      "Evaluation 2874: -1.239654696884565\n",
+      "Evaluation 2877: -1.2399497673356747\n",
+      "Evaluation 2880: -1.2400693268115837\n",
+      "Evaluation 2883: -1.2413550844233632\n",
+      "Evaluation 2886: -1.2413522670474304\n",
+      "Evaluation 2889: -1.241752906203216\n",
+      "Evaluation 2892: -1.2416058175206641\n",
+      "Evaluation 2895: -1.24149972213475\n",
+      "Evaluation 2898: -1.2401574885547377\n",
+      "Evaluation 2901: -1.2395807696341123\n",
+      "Evaluation 2904: -1.239580831297384\n",
+      "Evaluation 2907: -1.2414413204964323\n",
+      "Evaluation 2910: -1.2412701631399659\n",
+      "Evaluation 2913: -1.2412491689742335\n",
+      "Evaluation 2916: -1.2405152042946181\n",
+      "Evaluation 2919: -1.241573678180665\n",
+      "Evaluation 2922: -1.2411179003026813\n",
+      "Evaluation 2925: -1.2421107076555975\n",
+      "Evaluation 2928: -1.2421410284031338\n",
+      "Evaluation 2931: -1.242465103472868\n",
+      "Evaluation 2934: -1.242466082806483\n",
+      "Evaluation 2937: -1.242524925952567\n",
+      "Evaluation 2940: -1.2419621196214778\n",
+      "Evaluation 2943: -1.2419264507897363\n",
+      "Evaluation 2946: -1.2420952233401366\n",
+      "Evaluation 2949: -1.2421147151243201\n",
+      "Evaluation 2952: -1.2420132244400084\n",
+      "Evaluation 2955: -1.2418761634851552\n",
+      "Evaluation 2958: -1.241860394480052\n",
+      "Evaluation 2961: -1.2426461932654167\n",
+      "Evaluation 2964: -1.2427603093240098\n",
+      "Evaluation 2967: -1.242872140454859\n",
+      "Evaluation 2970: -1.2425272206860631\n",
+      "Evaluation 2973: -1.240953508826314\n",
+      "Evaluation 2976: -1.2415114791099948\n",
+      "Evaluation 2979: -1.2406828093125408\n",
+      "Evaluation 2982: -1.240939548744013\n",
+      "Evaluation 2985: -1.2409521106991137\n",
+      "Evaluation 2988: -1.241595182856894\n",
+      "Evaluation 2991: -1.2411936978280753\n",
+      "Evaluation 2994: -1.2429496559166842\n",
+      "Evaluation 2997: -1.2429963361324865\n",
+      "Evaluation 3000: -1.2430344133197917\n",
+      "Eigenvalue 1: -1.2433528947921406\n"
+     ]
+    }
+   ],
+   "source": [
+    "kk = 2\n",
+    "beta = 10\n",
+    "results = []\n",
+    "wavefuncs = []\n",
+    "for eidx in range(kk):\n",
+    "    cost_func = lambda params: \\\n",
+    "        estimator.run(ansatz, H2_op, parameter_values=params).result().values[0] + \\\n",
+    "        sum([beta*fidelity.run(ansatz, ansatz, params, wavefuncs[idx]).result().fidelities[0] for idx in range(eidx)])\n",
+    "    result = optimizer.minimize(cost_func, x0=np.zeros(ansatz.num_parameters))\n",
+    "    wavefuncs.append(result.x)\n",
+    "    results.append(estimator.run(ansatz, H2_op, parameter_values=result.x).result().values[0])\n",
+    "    print(f'Eigenvalue {eidx}: {results[eidx]}')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "0f0f7014",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[-1.8572377869802361, -1.2433528947921406]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(results)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "639a1093",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[-1.85727503 -1.24458455]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(eigens_exact[:kk])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "d43e6880",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from qiskit_ibm_runtime import Estimator, Sampler, Session, Options"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "d5d07d44",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "options = Options()\n",
+    "options.execution.shot = 5000\n",
+    "options.optimization_level = 2\n",
+    "options.resilience_level = 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "08c3bdf3",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from qiskit_ibm_runtime import QiskitRuntimeService\n",
+    "\n",
+    "service = QiskitRuntimeService()\n",
+    "backend = service.get_backend('ibm_lagos')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "748911bd",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "results = []\n",
+    "wavefuncs = []\n",
+    "with Session(service=service, backend=backend) as session:\n",
+    "    estimator = Estimator(options=options)\n",
+    "    sampler = Sampler(options=options)\n",
+    "    fidelity = ComputeUncompute(sampler)\n",
+    "    for eidx in range(kk):\n",
+    "        cost_func = lambda params: \\\n",
+    "            estimator.run(ansatz, H2_op, parameter_values=params).result().values[0] + \\\n",
+    "            sum([beta*fidelity.run(ansatz, ansatz, params, wavefuncs[idx]).result().fidelities[0] for idx in range(eidx)])\n",
+    "        result = optimizer.minimize(cost_func, x0=np.zeros(ansatz.num_parameters))\n",
+    "        wavefuncs.append(result.x)\n",
+    "        results.append(estimator.run(ansatz, H2_op, parameter_values=result.x).result().values[0])\n",
+    "        print(f'Eigenvalue {eidx}: {results[eidx]}')\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fc62c726",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "source": [
+    "These enrgies are stored in `energies-vqd.json` from a previous run and displayed below:\n",
+    "\n",
+    "```\n",
+    "Iteration 3: -1.0382056008617166\n",
+    "Iteration 6: -1.043449010055383\n",
+    "Iteration 9: -1.0991475296769626\n",
+    "Iteration 12: -1.048740248917583\n",
+    "Iteration 15: -1.0917513815319064\n",
+    "Iteration 18: -1.0908207936962153\n",
+    "Iteration 21: -1.0045573480928254\n",
+    "Iteration 24: -1.1665278591668955\n",
+    "Iteration 27: -0.9577752043151959\n",
+    "Iteration 30: -1.1267180223380715\n",
+    "Iteration 33: -1.0326271696284972\n",
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+    "Iteration 1035: -1.7958787999985975\n",
+    "Iteration 1038: -1.7825000627789547\n",
+    "Iteration 1041: -1.82270857053919\n",
+    "Iteration 1044: -1.784563343034513\n",
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+    "Iteration 1050: -1.817993953520346\n",
+    "Iteration 1053: -1.812396261178737\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "03ebe9fd",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[-1.812396261178737, 1.4541251403413418]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(results)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cb869312",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "jobs = service.jobs(session_id=session.session_id, limit=None)[::-1]\n",
+    "sidx = next(jidx for jidx, job in enumerate(jobs) if job.program_id == 'sampler' and job.done())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3bf43617",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "ejobs = {}\n",
+    "ejobs[0] = {}\n",
+    "ejobs[1] = {}\n",
+    "ejobs[0]['estimator'] = jobs[:sidx-2]\n",
+    "ejobs[1]['estimator'] = jobs[sidx-1::2]\n",
+    "ejobs[1]['sampler'] = jobs[sidx::2]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a2899916",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "energies = {}\n",
+    "energies[0] = [job.result().values[0] for job in ejobs[0]['estimator']]\n",
+    "energies[1] = [job.result().values[0] for job in ejobs[1]['estimator']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "id": "45a55ff0",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x16809f850>"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "plt.style.use('dark_background')\n",
+    "\n",
+    "[plt.plot(range(len(energies[eidx])), energies[eidx], label=f'{eidx} level') for eidx in range(kk)]\n",
+    "[plt.plot(range(len(energies[eidx])), [eigens_exact[eidx]]*len(energies[eidx]), ls='--') for eidx in range(kk)]\n",
+    "plt.xlabel('Iteration Count')\n",
+    "plt.ylabel('Energy')\n",
+    "plt.legend(loc=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "4f8d10f5",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import json\n",
+    "with open('energies-vqd.json', 'r') as handle:\n",
+    "    stored_energies = json.load(handle)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "a4a4e920",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "energies = {}\n",
+    "energies[0] = [list(stored_energies['0'][idx].values())[0] for idx in range(len(stored_energies['0']))]\n",
+    "energies[1] = [list(stored_energies['1'][idx].values())[0] for idx in range(len(stored_energies['1']))]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "1d26611b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[-1.812396261178737, 1.4541251403413418]\n"
+     ]
+    }
+   ],
+   "source": [
+    "results = [energies[0][-1], energies[1][-1]]\n",
+    "print(results)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "a86c071a",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iteration 3: -1.0382056008617166\n",
+      "Iteration 6: -1.043449010055383\n",
+      "Iteration 9: -1.0991475296769626\n",
+      "Iteration 12: -1.048740248917583\n",
+      "Iteration 15: -1.0917513815319064\n",
+      "Iteration 18: -1.0908207936962153\n",
+      "Iteration 21: -1.0045573480928254\n",
+      "Iteration 24: -1.1665278591668955\n",
+      "Iteration 27: -0.9577752043151959\n",
+      "Iteration 30: -1.1267180223380715\n",
+      "Iteration 33: -1.0326271696284972\n",
+      "Iteration 36: -1.037688639771772\n",
+      "Iteration 39: -0.987659283105804\n",
+      "Iteration 42: -1.124828085753853\n",
+      "Iteration 45: -0.9730646395626537\n",
+      "Iteration 48: -1.0764693032544939\n",
+      "Iteration 51: -0.9774715309095001\n",
+      "Iteration 54: -1.0691231309956273\n",
+      "Iteration 57: -1.009745661248089\n",
+      "Iteration 60: -1.1487429489225114\n",
+      "Iteration 63: -1.1690133316853355\n",
+      "Iteration 66: -1.0091157125554784\n",
+      "Iteration 69: -1.130684179135256\n",
+      "Iteration 72: -0.9998160962250462\n",
+      "Iteration 75: -1.0910099525926027\n",
+      "Iteration 78: -1.0145267640484403\n",
+      "Iteration 81: -1.1450295977762361\n",
+      "Iteration 84: -1.0186948149960828\n",
+      "Iteration 87: -1.0012827371337458\n",
+      "Iteration 90: -1.1735177143285604\n",
+      "Iteration 93: -1.1127192275573563\n",
+      "Iteration 96: -1.0473699442993365\n",
+      "Iteration 99: -0.9737242385335628\n",
+      "Iteration 102: -1.0798189340966715\n",
+      "Iteration 105: -1.0410746017915977\n",
+      "Iteration 108: -1.0509351588166596\n",
+      "Iteration 111: -1.1440115293671567\n",
+      "Iteration 114: -1.0252157140059417\n",
+      "Iteration 117: -1.0961171266336873\n",
+      "Iteration 120: -0.9773892829951627\n",
+      "Iteration 123: -1.0722488510527524\n",
+      "Iteration 126: -1.063469910878646\n",
+      "Iteration 129: -1.0829858683312552\n",
+      "Iteration 132: -1.0600191242353867\n",
+      "Iteration 135: -1.0587548488614689\n",
+      "Iteration 138: -1.1072786014015783\n",
+      "Iteration 141: -1.115318850701003\n",
+      "Iteration 144: -1.0545909000175269\n",
+      "Iteration 147: -1.1393798017453545\n",
+      "Iteration 150: -1.0238671929262682\n",
+      "Iteration 153: -1.1372584608183984\n",
+      "Iteration 156: -1.0380173433952609\n",
+      "Iteration 159: -1.1668135728626885\n",
+      "Iteration 162: -1.23173230584167\n",
+      "Iteration 165: -1.1857092195760086\n",
+      "Iteration 168: -1.2721347242887666\n",
+      "Iteration 171: -1.3271014852204899\n",
+      "Iteration 174: -1.2234849764565119\n",
+      "Iteration 177: -1.4354450886941321\n",
+      "Iteration 180: -1.4121505495619489\n",
+      "Iteration 183: -1.2349457041132195\n",
+      "Iteration 186: -0.5739465116090634\n",
+      "Iteration 189: -0.4395928129072497\n",
+      "Iteration 192: -0.7461882850801045\n",
+      "Iteration 195: -0.8175308241014825\n",
+      "Iteration 198: -0.7732486442306853\n",
+      "Iteration 201: -1.044340588073701\n",
+      "Iteration 204: -1.3359631025512113\n",
+      "Iteration 207: -1.4227897681963881\n",
+      "Iteration 210: -1.212922398891234\n",
+      "Iteration 213: -1.270692226777346\n",
+      "Iteration 216: -1.3170154193235064\n",
+      "Iteration 219: -1.1738827845815332\n",
+      "Iteration 222: -1.2606392100262545\n",
+      "Iteration 225: -1.2121291315582245\n",
+      "Iteration 228: -1.2813498134267785\n",
+      "Iteration 231: -1.2964798973247489\n",
+      "Iteration 234: -1.3053405606354276\n",
+      "Iteration 237: -1.2075402090566392\n",
+      "Iteration 240: -1.283769185944147\n",
+      "Iteration 243: -1.49082650998405\n",
+      "Iteration 246: -1.0003597587730684\n",
+      "Iteration 249: -1.189386420879612\n",
+      "Iteration 252: -1.2716489331680538\n",
+      "Iteration 255: -1.061590388588323\n",
+      "Iteration 258: -1.3482979035904505\n",
+      "Iteration 261: -1.2775751092286245\n",
+      "Iteration 264: -1.4503103461616385\n",
+      "Iteration 267: -1.4712925926127225\n",
+      "Iteration 270: -1.5105228389229879\n",
+      "Iteration 273: -1.3071793373638865\n",
+      "Iteration 276: -1.4071191929443998\n",
+      "Iteration 279: -1.2712057620594401\n",
+      "Iteration 282: -1.4593775777290903\n",
+      "Iteration 285: -1.4315276593968649\n",
+      "Iteration 288: -1.3497611256174746\n",
+      "Iteration 291: -1.4709616385724384\n",
+      "Iteration 294: -1.4587320168491291\n",
+      "Iteration 297: -1.3496988547798145\n",
+      "Iteration 300: -1.4880319579453658\n",
+      "Iteration 303: -1.495190334265667\n",
+      "Iteration 306: -1.5393215408540204\n",
+      "Iteration 309: -1.4573883310635245\n",
+      "Iteration 312: -1.5445130747047715\n",
+      "Iteration 315: -1.5491279264642355\n",
+      "Iteration 318: -1.5184143945007573\n",
+      "Iteration 321: -1.5244273837511833\n",
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+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None,\n",
+       " None]"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# this code produces the output shown from the VQD run on ibm_lagos\n",
+    "# ground state, replace 0 index with 1 for first excited state\n",
+    "[print(f'Iteration {3*(idx+1)}: {energies[0][idx]}') for idx in range(len(energies[0]))]"
+   ]
+  }
+ ],
+ "metadata": {
+  "celltoolbar": "Slideshow",
+  "kernelspec": {
+   "display_name": "qiskit (stable)",
+   "language": "python",
+   "name": "qiskit-stable"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.13"
+  },
+  "rise": {
+   "theme": "monokai",
+   "transition": "none"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}