diff --git a/data/QPI.ipynb b/data/QPI.ipynb
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Welcome to the Quantum Parallel Universe"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Initial Setup"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Imports"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import math\n",
+    "from IPython.display import Latex\n",
+    "from qiskit import Aer, execute, QuantumCircuit\n",
+    "from qiskit.circuit import Qubit\n",
+    "from qiskit.visualization import plot_histogram"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Globals"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Manually Managed Variables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# number of qubits: int\n",
+    "N = 16\n",
+    "\n",
+    "# shots: int\n",
+    "shots = 2**16\n",
+    "\n",
+    "# Quantum Simulator Object\n",
+    "simulator = Aer.get_backend(\"aer_simulator\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Automatically Managed Variables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# linear GHZ container\n",
+    "linear = {\n",
+    "  'circuit': None,\n",
+    "  'job': None,\n",
+    "  'result': None,\n",
+    "  'time': None,\n",
+    "  'error': { '0': None, '1': None }\n",
+    "}\n",
+    "\n",
+    "# logarithmic GHZ container\n",
+    "log = {\n",
+    "  'circuit': None,\n",
+    "  'job': None,\n",
+    "  'result': None,\n",
+    "  'time': None,\n",
+    "  'error': { '0': None, '1': None }\n",
+    "}\n",
+    "\n",
+    "# ideal shots per state\n",
+    "isps = shots / 2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Generate $|\\text{GHZ}_N\\rangle$ Circuits<sup>1</sup>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Generate Linear Time Complexity Circuits for $|\\text{GHZ}_N\\rangle$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def linear_complexity_GHZ(N: int) -> QuantumCircuit:\n",
+    "  if not isinstance(N, int):\n",
+    "    raise TypeError(\"Only integer arguments accepted.\")\n",
+    "  if N < 1:\n",
+    "    raise ValueError(\"There must be one or more qubits.\")\n",
+    "\n",
+    "  c = QuantumCircuit(N)\n",
+    "  for i in range(N):\n",
+    "    c.reset(i)\n",
+    "  c.h(0)\n",
+    "  for i in range(1, N):\n",
+    "    c.cx(i-1, i)\n",
+    "  c.measure_active()\n",
+    "  return c"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+      "text/plain": [
+       "<Figure size 3053.19x1454.83 with 1 Axes>"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "linear['circuit'] = linear_complexity_GHZ(N)\n",
+    "linear['circuit'].draw(output='mpl', fold=-1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Generate Logaritmic Complexity Circuits for $|\\text{GHZ}_{2^m}\\rangle$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def _log_complexity_GHZ(m: int) -> QuantumCircuit:\n",
+    "  if not isinstance(m, int):\n",
+    "    raise TypeError(\"Only integer arguments accepted.\")\n",
+    "  if m < 0:\n",
+    "    raise ValueError(\"`m` must be at least 0 (evaluated 2^m).\")\n",
+    "\n",
+    "  if m == 0:\n",
+    "    c = QuantumCircuit([Qubit()])\n",
+    "    c.reset(0)\n",
+    "    c.h(0)\n",
+    "  else:\n",
+    "    c = _log_complexity_GHZ(m - 1)\n",
+    "    for i in range(c.num_qubits):\n",
+    "      c.add_bits([Qubit()])\n",
+    "      new_qubit_index = c.num_qubits - 1\n",
+    "      c.reset(new_qubit_index)\n",
+    "      c.cx(i, new_qubit_index)\n",
+    "  return c"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Generate Logaritmic Complexity Circuits for $|\\text{GHZ}_N\\rangle$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def log_complexity_GHZ(N: int) -> QuantumCircuit:\n",
+    "  if not isinstance(N, int):\n",
+    "    raise TypeError(\"Only an integer argument is accepted.\")\n",
+    "  if N < 1:\n",
+    "    raise ValueError(\"There must be one or more qubits.\")\n",
+    "\n",
+    "  m = math.ceil(math.log2(N))\n",
+    "  num_qubits_to_erase = 2**m - N\n",
+    "  old_circuit = _log_complexity_GHZ(m=m)\n",
+    "  new_num_qubits = old_circuit.num_qubits - num_qubits_to_erase\n",
+    "  new_circuit = QuantumCircuit(new_num_qubits)\n",
+    "  for gate in old_circuit.data:\n",
+    "    qubits_affected = gate.qubits\n",
+    "    if all(old_circuit.find_bit(qubit).index < new_num_qubits for qubit in qubits_affected):\n",
+    "      new_circuit.append(gate[0], [old_circuit.find_bit(qubit).index for qubit in qubits_affected])\n",
+    "  new_circuit.measure_active()\n",
+    "  return new_circuit\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 3053.19x1454.83 with 1 Axes>"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "log['circuit'] = log_complexity_GHZ(N)\n",
+    "log['circuit'].draw(output='mpl', fold=-1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Quantum Simulation & Results"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Create Simulator Jobs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "linear['job'] = execute(linear['circuit'], simulator, shots=shots)\n",
+    "log['job'] = execute(log['circuit'], simulator, shots=shots)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Execution Histograms"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Linear"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 700x500 with 1 Axes>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "linear['result'] = linear['job'].result()\n",
+    "plot_histogram(linear['result'].get_counts())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Logaritmic"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 700x500 with 1 Axes>"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "log['result'] = log['job'].result()\n",
+    "plot_histogram(log['result'].get_counts())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Error Analysis"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Linear Error Percentage"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "##### State $|0\\rangle$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "\\begin{equation*}0.103759765625\\%\\end{equation*}"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Latex object>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "linear['error']['0'] = abs((linear['result'].get_counts()['0' * N] - isps) / isps)\n",
+    "Latex(f\"\"\"\\\\begin{{equation*}}{linear['error']['0'] * 100}\\%\\\\end{{equation*}}\"\"\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### State $|1\\rangle$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "\\begin{equation*}0.103759765625\\%\\end{equation*}"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Latex object>"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "linear['error']['1'] = abs((linear['result'].get_counts()['1' * N] - isps) / isps)\n",
+    "Latex(f\"\"\"\\\\begin{{equation*}}{linear['error']['1'] * 100}\\%\\\\end{{equation*}}\"\"\")"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Logarithmic Error Percentage"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### State $|0\\rangle$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "\\begin{equation*}0.1434326171875\\%\\end{equation*}"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Latex object>"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "log['error']['0'] = abs((log['result'].get_counts()['0' * N] - isps) / isps)\n",
+    "Latex(f\"\"\"\\\\begin{{equation*}}{log['error']['0'] * 100}\\%\\\\end{{equation*}}\"\"\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### State $|1\\rangle$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "\\begin{equation*}0.1434326171875\\%\\end{equation*}"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Latex object>"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "log['error']['1'] = abs((log['result'].get_counts()['1' * N] - isps) / isps)\n",
+    "Latex(f\"\"\"\\\\begin{{equation*}}{log['error']['1'] * 100}\\%\\\\end{{equation*}}\"\"\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Speed-Up Analysis"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Run-Times"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Linear"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "\\begin{equation*}1.291309118270874\\space\\text{seconds}\\end{equation*}"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Latex object>"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "linear['time'] = linear['result'].time_taken\n",
+    "Latex(f\"\"\"\\\\begin{{equation*}}{linear['time']}\\\\space\\\\text{{seconds}}\\\\end{{equation*}}\"\"\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Log"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "\\begin{equation*}0.635613203048706\\space\\text{seconds}\\end{equation*}"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Latex object>"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "log['time'] = log['result'].time_taken\n",
+    "Latex(f\"\"\"\\\\begin{{equation*}}{log['time']}\\\\space\\\\text{{seconds}}\\\\end{{equation*}}\"\"\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Amdahl's Law"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Parallel Portion"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "\\begin{equation*}\n",
+       "    P = \\dfrac{N\\left(1 - \\dfrac{1}{S_\\text{latency}}\\right)}{N - 1} = \\dfrac{16\\left(1 - \\dfrac{1}{2.031595807131028}\\right)}{15} = 54.162784599052614\\%\n",
+       "    \\end{equation*}\n",
+       "  "
+      ],
+      "text/plain": [
+       "<IPython.core.display.Latex object>"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "S_latency = linear['time'] / log['time']\n",
+    "P = (N * (1 - (1 / S_latency))) / (N - 1)\n",
+    "Latex(f\"\"\"\\\\begin{{equation*}}\n",
+    "    P = \\\\dfrac{{N\\\\left(1 - \\\\dfrac{{1}}{{S_\\\\text{{latency}}}}\\\\right)}}{{N - 1}} = \\\\dfrac{{{N}\\\\left(1 - \\\\dfrac{{1}}{{{S_latency}}}\\\\right)}}{{{N - 1}}} = {P * 100}\\%\n",
+    "    \\\\end{{equation*}}\n",
+    "  \"\"\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Sequential Portion"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "\\begin{equation*}S_\\text{EQ} = 1 - P = 45.837215400947386\\%\\end{equation*}"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Latex object>"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "S_EQ = 1 - P\n",
+    "Latex(f\"\"\"\\\\begin{{equation*}}S_\\\\text{{EQ}} = 1 - P = {S_EQ * 100}\\%\\\\end{{equation*}}\"\"\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## References"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "1. [arXiv:1807.05572](https://arxiv.org/abs/1807.05572)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.8.16"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/data/Backing_Research.pdf b/data/arXiv.1807.05572.pdf
similarity index 100%
rename from data/Backing_Research.pdf
rename to data/arXiv.1807.05572.pdf
diff --git a/data/init.ipynb b/data/init.ipynb
deleted file mode 100644
index 0353621..0000000
--- a/data/init.ipynb
+++ /dev/null
@@ -1,401 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Welcome to the Quantum Parallel Universe"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Before Running this Notebook"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "- Create a `token.txt` file in the same folder as this notebook\n",
-    "  - `token.txt` should contain a token sufficient for [`qiskit_ibm_provider.IBMProvider()`](https://github.com/Qiskit/qiskit-ibm-provider#enable-account-for-current-session) authentication."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "---"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Imports"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import math\n",
-    "from qiskit import execute, QuantumCircuit\n",
-    "from qiskit.circuit import Qubit\n",
-    "from qiskit.compiler import transpile\n",
-    "from qiskit.visualization import plot_histogram\n",
-    "from qiskit_ibm_provider import IBMBackend, IBMProvider, least_busy"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Globals"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Manually Managed Variables"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# number of qubits\n",
-    "N = 8\n",
-    "\n",
-    "# use simulator\n",
-    "use_sim = True\n",
-    "\n",
-    "# shots\n",
-    "shots = 1024"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Automanaged Variables"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# IBM token\n",
-    "token = None\n",
-    "\n",
-    "# linear complexity GHZ circuit\n",
-    "linear_complexity_circuit = None\n",
-    "\n",
-    "# linear complexity mapped circuit\n",
-    "linear_complexity_mapped_circuit = None\n",
-    "\n",
-    "# linear complexity job\n",
-    "linear_complexity_job = None\n",
-    "\n",
-    "# linear complexity result\n",
-    "linear_complexity_result = None\n",
-    "\n",
-    "# logarithmic complexity GHZ circuit\n",
-    "log_complexity_circuit = None\n",
-    "\n",
-    "# log complexity mapped circuit\n",
-    "log_complexity_mapped_circuit = None\n",
-    "\n",
-    "# log complexity job\n",
-    "log_complexity_job = None\n",
-    "\n",
-    "# log complexity result\n",
-    "log_complexity_result = None\n",
-    "\n",
-    "# IBMBackend\n",
-    "backend = None\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Generate Linear Time Complexity Circuits for $|\\text{GHZ}_N\\rangle$"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def linear_complexity_GHZ(N: int) -> QuantumCircuit:\n",
-    "  if not isinstance(N, int):\n",
-    "    raise TypeError(\"Only integer arguments accepted.\")\n",
-    "  if N < 1:\n",
-    "    raise ValueError(\"There must be one or more qubits.\")\n",
-    "\n",
-    "  c = QuantumCircuit(N)\n",
-    "  for i in range(N):\n",
-    "    c.reset(i)\n",
-    "  c.h(0)\n",
-    "  for i in range(1, N):\n",
-    "    c.cx(i-1, i)\n",
-    "  c.measure_active()\n",
-    "  return c"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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RkCFDlJeXJ0lKTEys0e7VV1/Vd9991yxXZb9QSkqK/P39NWDAgBrbg4ODlZiYWGcQO27cOG3YsEGVlZWNVSYAuCQs5NLzdDakmLbGXdtZJpN0Q7zr7Tq1k6LaeL4eoCmJj4/Xjh07FB/vxv9JAKCJ6N+/f/WfN26se27zS3E27JSkzz//XIWFhZKkfv36uXU9AA2HwNOHzJkzR23btlVGRoYSEhLUu3dvxcXFacCAAercubOGDh0qqeb8nXl5efrtb3+rp59+WpWVlcrPz1d+fr4kxzxP+fn5HllMqLGsWrVKNptNs2bNqrF96tSpslgseuedd2ps7969u7p166bk5ORGrBIAXBcT7pvXdsU1sa7PUXpzzwYpBWhSAgMDFR4ersDAQKNLAQC3nR86ujMVmythpyTZ7fbqOUJjYmLqnToNgDEIPH1IdHS0PvvsM40ePVpms1nHjx9XeHi43njjDa1fv7661+f5gWdmZqbOnj2radOmqU2bNtW/JEdP0TZt2ujkyZOG3I879u/fryVLlmjChAlas2aNpkyZooULF2rRokXatm2bVq5cWeP4cePGqby83O1vCAGgsXTvaMx1A/2lzu2MubarggOk+2+SQoOcO350X6lX01ugFvC4kydP6oEHHmhWz3QAcKEuXbpU//mbb75xqa2rYWdd1zn/+gCMxxyePqZHjx5at25dre1FRUU6fvy4/Pz81KtXr+rtXbt2rXNl8iFDhujee+/V5MmTL2sS2YaWmpqqFStW1Ng2a9YsHT9+XPfff79Gjx6tvLw8vfLKK3r66adlt9trHDtu3Dht3bpVZ8+ebcSqAcB1SZ2lj1Olc408+8Y1sVJocONe83Jc0Uaaeav07k7peF7dx7QwS7f1la7r2ri1AUY5e/astm7dqgcffNDoUgDAbXv27NG5c+cUFBRU74rpdZk+fbpbYackpaen69NPP1VpaamKiopcrhlAwyHwhCTHZPV2u13x8fE1Vuds0aKFbrrppjrbxMbG1ruvqdi3b5/27dtXY5vNZtOiRYu0aNGii7bt0KGDrr32Wh7+ATQL5kCpfyfp88ONe90b4hr3ep7QIUyadauUeVr6/Ii066hktUkBftLPB0p9YqQAf6OrBAAArrhw2jJnJScn6+GHH1bnzp1dCjslx8JFr7/+ulvXBdCwGNIOSf/tin/+cHZfN3bsWEli/k4AzcbwBMew7cbS98rmsWBRfaLDpZ8NkFr82EPVEuzosUrYCQCA78jIyNBNN92kX/3qVy6FnQCaNnp4QpLrgeeFQ7+90fLly/XWW2/p3LlzRpcCAE5pY5HG95NW7Wr4a1mCpZ8mNfx1AAAAGlpGRobefPNNo8sA4EH08IQkenjWxWq1EnYCaHau6yL1vMK1NoWlUn6J43dn/WyA1NLs2nUANE0dOnTQ3Llz1aFDB6NLAQAA8Ah6eEKStGXLFqNLAAB4gMkk3XuD9PqW+hfludCiTa5d4/Z+juHsALxDRESEJk+ebHQZAAAAHkMPTwAAvExwoPTroVKchztrmeQYxv6T7p49LwBjFRQUaNOmTSooKDC6FAAAAI8g8AQAwAuZfww9R/WV/D3wr327ltJDw6Ub4i//XACalszMTD388MPKzMw0uhQAAACPYEg7AABeyt9PuqWX1CtK+nCPdPg7188RHCANipNG9JGCeGoAAAAA0Azw6gIAgJe7oo304DApO1/6/LD01QmpqLz+400/thnYVerfydFbFAAAAACaCwJPAAB8RMfW0oQk6f/1d6zKnnFayi2UKmySn0kKCZSi2jh+BRNyAgAAAGimCDwBAPAxJpPUxuL4BQBms1k9evSQ2Ww2uhQAAACPIPAEAAAAfFiXLl30wQcfGF0GAACAx7BKOwAAAAAAAACvQeAJAAAA+LC0tDT16dNHaWlpRpcCAADgEQSeAAAAgA+z2+2qqKiQ3W43uhQAAACPYA5PNBuhoaEqKioyugyXhIaGGl0CAAAAAHgFT78TvvDGP1RYXKIwS6gemzax1mdP4J0QMAaBJ5oNk8kki4UlhQEAAADAF3n6nTAo2KygCquCgs2yWCy1PgNovhjSDgAAAAAAAMBr0MMTAAAA8GFdunTR2rVrFRMTY3QpAAAAHkHgCQAAAPgws9msuLg4o8sAAADwGIa0AwAAAD4sKytLTz31lLKysowuBQAAwCMIPAEAAAAflp+frzVr1ig/P9/oUgAAADyCwBMAAAAAAACA1yDwBAAAAAAAAOA1CDwBAAAAAAAAeA0CTwAAAMCHRUREaOrUqYqIiDC6FAAAAI8g8AQAAAB8mMlkUlBQkEwmk9GlAAAAeASBJwAAAODDcnNztWTJEuXm5hpdCgAAgEcQeAIAAAAAAADwGgSeAAAAAAAAALwGgScAAAAAAAAAr0HgCQAAAPiwVq1aacyYMWrVqpXRpQAAAHhEgNEFAAAAADBOdHS0FixYYHQZAAAAHkMPTwAAAMCHlZeX68SJEyovLze6FAAAAI8g8AQAAAB82JEjRzRixAgdOXLE6FIAAAA8giHtaDbsdrtKSkqMLsMloaGhMplMRpcBAAAAAPACvBcDziHwRLNRUlKiFi1aGF2GS4qKimSxWIwuAwAAAADgBXgvBpzDkHYAAAAAAAAAXoPAEwAAAAAAAIDXYEg7AAAA4MMSEhKUnp5udBkAAAAeQw9PAAAAAAAAAF6DwBMAAPgku13KL5GsNsdnq02qtBpbE2CEY8eOaeLEiTp27JjRpQAAAHgEQ9oBAIDPOFMs7fpWOp4rZZyWisv/u6+oXJq7WrqitRTTVkq8UorrIJlMhpULNIqSkhLt27dPJSUlRpcCAADgEQSeAADA6337vbQlXTqQ5ejZWR+rzRGEZpyWPj8stQ+TboyXro+T/BkXAwAAADQLBJ4AAMBrlVdIH38lbT/sXvvvC6U1u6Uvjko/HyhFtfFsfQAAAAA8j74KAADAK506Iy3Y4H7Yeb6sM9KLG6VtBy//XAAAAAAaFj08AQCA18n4QXp9i1RyznPntNmlj/ZIpeekkX08d17AaFFRUZo/f76ioqKMLgUAAMAj6OEJAAC8yveF0p+3ejbsPN8/v5G2pTfMuQEjtG7dWmPHjlXr1q2NLgUA0MwEBQUpKSnJ6DKAWujhCQAAvIbVJv3985qrr1/KIyOksBCpsFRatMm5Nmu/krp2kKLD3asTaEpOnz6tjRs3auTIkQoP5z9qAPB27du31/Dhw9WvXz/169dPV155pYKDg1VRUaHc3Fzt2bNHe/bs0datW3X4cP1zAwUFBWnNmjUaNmyYxo0bp82bNzfiXQAXR+AJAAC8xtZ06eQPrrUJC5Fah7rWxmaXVu50hKUB/q61BZqa7OxszZs3T4mJiQSeAODFBg0apAceeEA//elPFRQUVOcxV155pfr161f9ecuWLXrttdeUnJysysrK6u1VYedtt90mSVq5cqViY2NVVFTUsDcBOIkh7T4qLy9Pc+bMUdeuXWU2mxUTE6OZM2equLhYU6ZMkclk0quvvmp0mYaaNm2arFarIiMjjS4FAOCEglJp09eNd71T+Z5ZEMlIeWelHYelLWnS9kNS5mmjKwIAAJ7WoUMHffjhh9q+fbt+/vOf1wo7c3NzdfLkSWVnZ8tms9XYN3ToUL3//vtKSUlR3759JdUOO4uLizVhwgTCTjQpBJ4+KDU1Vb1799YLL7ygnJwc9ezZUxUVFXr55Zd15513Kj3dMTFZYmKisYV6wL333qutW7fW2GYymTRr1iylp6ertLRUJ0+e1MKFCxUaWrN7z9q1ayVJY8aMabR6AQDu23lEqrRd+jhP2v4fR2/P5uZQjvTnLdK8tdJ7XzqG6L+fIi3cKL30Tyn1pNEVAgAAT/jpT3+qtLQ0jR8/vnpbbm6u5s+fr1tvvVURERFq3769rrrqKl1xxRUKCwvToEGDNGfOHB06dKi6TWJiolJSUvTss8/WCjtHjx6t//u//2vsWwMuisDTx+Tl5WnMmDHKycnR7NmzlZ2drb179yonJ0fz58/X+vXrlZKSIpPJpD59vHMJ2sWLF2vx4sVKS0vTQw89pPfee08zZszQxx9/LJPJVH1cdna2du/eXeMfBgBA02S1STsN6G2ZVyT9J7vxr3s5th+SXv9EOlhP3cfzpBWfSR9/JdmbYZgLAAAcpk+frvfee696upLvv/9e9913n2JiYvT4449r8+bN+uGHmnMBFRcX6/PPP9cLL7yg7t27a/jw4fr6a8cQmsDAQD3zzDOEnWgWCDx9zIwZM5SZmanp06dr4cKFatmyZfW+OXPmqG/fvqqsrFRsbKzCwsIMrLRh9OzZUw899JDWrFmjCRMmaNmyZZo9e7YeeeQRDR06VBMnTqxx/EcffaShQ4fKYrEYVDEAwBnf5jqGtBvhqxPGXNcdqSccPTmdyTE/SXMMdYf3s1gsGjRoEM87AOBFpk6dqldeeaX68+rVq5WQkKAVK1aovNy51R3tdrv+/e9/q3///vrDH/4g+3nfhFZUVBB2okkj8PQh6enpWrVqlSIiIvTcc8/VeUzV5MRVc3NI0rZt22QymWr9ao5D3u+66y75+fnppZdeqrF96dKlKi4u1t13311je3Jyssxms0aMGNGIVQIAXJXh4kJFHr12M5n30maTkr9yrc0/v5FKzjVMPWg6YmNjtWzZMsXGxhpdCgDAA/r376/XX3+9+vPvf/973XnnncrLy3PrfCaTSX379q0xIjIwMFDdu3e/7FqBhkLg6UPeffdd2Ww2TZo0SS1atKjzmJCQEEk1A88qS5Ys0c6dO6t/vf322w1ab0NISkqS1WrVl19+WWN7eXm5UlNTlZSUVGN7WlqaDh8+zLB2AGjijAwdvyuQzlVe+jijpZ+SzhS71uacVUr5tmHqQdNhtVpVVFQkq9VqdCkAgMsUFBSkv/71r/L395ckvfjii3r66acv63znz9lZVlZWve+FF17QVVdddXkFAw2EwNOHbNmyRZI0ZMiQeo/JzMyUVHfg2bNnT1133XXVv3r37t0whTagK664Qnl5eTp3rnZ3laysLLVr106BgYE1ticnJ2vUqFHV/2AAAJqe3ELjrm2zSz80g0VJ97o59N7ddmg+Dh48qKSkJB08eNDoUgAAl+mJJ55Qr169JEl79uzR448/7va56lqNfcSIEXrjjTckSS1bttSf//znyy8aaAABRheAxnPihOONpb5vYCorK7Vjxw5JdQeentS/f3/l5OS41MZmu/yld0NDQ+udr6Tqm6rQ0FAVFBRUb09OTtajjz6qwYMH11rx/VLi4uLk58f3CgDQ0G6ZvVVh7ePq3PfICCkspP62Yeb//v7s7Re/TmGptGhTHdcfcZvOZKY6V6xBbvzVSnWIG+xyuwP/Oa7oKTc0QEVoSJMnT3b62OxsxwpWGzZs0FdfOT/vwYoVK1ysqum5/b5ZsrQIU3ZOtqKjo2t99nbcf+379fWfAfff9O//Yu/FZrNZDz30kCTHHJv33XefKivdG4ZSV9hZNWfn3r17NXLkSF155ZUaMWKEevXqpf3799d7Lt6L4a7IyEjt3r3brbYEnj6kuNgxjq20tO5VHVatWqW8vDy1bNlSnTp1qrW/as6Ptm3bauzYsXr++ecVERHhVi05OTnKyspyq+3lKCkpUfv27evcZzabq485X9XwLnd6eFa9QAAAGta5i0y+HxYitQ699Dn8/Jw7ri7ff5et7w34d80VpSUujmf/0bnyMkP+zcblufB55mKqvvQtKytzqZ03/Hdh+/E5z2a1Kisrq9Znb8f9175fX/8ZcP/N+/7vuOMOtW3bVpLj/f6bb75x6zwXCzsl6ezZs1qwYIFeffVVSdJvfvMbPfjgg/Wej/diGIHA04dERkbqzJkz2rt3rwYOHFhjX3Z2th577DFJUp8+fWpMRtyqVSs99thjGjx4sFq0aKGdO3fqueee0xdffKHdu3dXB4Wu1uIqm8122X9Rnjp1Sj179lRQUFCtYe1RUVHKzc1VRUVFje3jx49Xfn6+tm3b5vL1OnbsyDdZANAITLayevcVXmL19jCzI+y02aTC+k9z0XO1CQtVYFTUJao0VkWRe/+GluVnKKqJ3xtqCw11Pr2vepYzm80utfOG/y78fvxC28/fX1FRUbU+ezvuv/b9+vrPgPtv+vd/sffiX/7yl9V/fu2119w6/6XCzipvv/22nn/+ebVo0UL33HOPZs2aVetdugrvxXCXO9lRFQJPHzJs2DClp6dr/vz5Gj58uOLj4yVJKSkpuueee6pXbLtw9fWrr75aV199dfXnm266Sb169dLYsWP17rvv6r777nO5Fne6JBcXF9e72JKzUlJSdOutt2rAgAHavn179fbg4GAlJibq008/rdVm3Lhx2rBhg1tDAQ4fPiyLxXJZNQMALm31LunzI3Xvq2sI+vmevd3Rs7OwTHr2Q9evHRQgpX31mZr6c/ypM9KCDa63e/o3w9XrD5meLwgNypX5OA8cOKDly5dr1KhRSkhIcLrdvHnz3CmtSfnjkndUWFSsjpEdlZmZWeuzt+P+a9+vr/8MuP+mf//1vRf7+/trwIABkqRjx45p586dLp/b2bBTkgoLC/Xxxx/rrrvuUsuWLZWQkKDU1NQ6z8t7MYzQxB/N4Ulz5sxR27ZtlZGRoYSEBPXu3VtxcXEaMGCAOnfurKFDh0pybv7O2267TRaLxe25FIyyatUq2Ww2zZo1q8b2qVOnymKx6J133qmxvXv37urWrZuSk5MbsUoAgKti2hp37eg2avJhpyRd0Ubq3M61Nm1CpZ5XNEw9aDri4+O1Y8eO6i/DAQDNT/fu3at76aekpLjc3pWws8r51+nXr5/L1wQaUjN4PIenREdH67PPPtPo0aNlNpt1/PhxhYeH64033tD69et16NAhSa4tWHT+0PfmYP/+/VqyZIkmTJigNWvWaMqUKVq4cKEWLVqkbdu2aeXKlTWOHzdunMrLy7Vx40aDKgYAOCPWvSmlm/21XTXxOik0yLljA/ykewY1jzAXlycwMFDh4eEKDAw0uhQAgJv69OlT/ee9e/e61NadsFNyrAJfpaEXPgZcxZB2H9OjRw+tW7eu1vaioiIdP35cfn5+6tWr1yXPs3btWhUXF1d3mW+qUlNTa60gOmvWLB0/flz333+/Ro8erby8PL3yyit6+umnZbfbaxw7btw4bd26VWfPnm3EqgEArurYWrqqrXTih8a/9rVdGv+a7mofJk0fJr2xVSq4yNym5kDpl4OlznWv8wcvc/LkST3//PN6/PHHdeWVVxpdDgDADWFhYdV/zsnJcbqdu2GnJH333XfVf27ZsqUL1QINj8ATkhxzN9ntdsXHx9earP7uu+9W586ddc0111QvWrRgwQIlJiZq4sSJBlXsnH379mnfvn01ttlsNi1atEiLFi26aNsOHTro2muvvehqcwCApmNQvHTC9emqLktcB6lDq8a95uW6oo30xBhp9zFp+yEpp+C/+0wmaXRfR4jb0vU1CdFMnT17Vlu3buWZBwCasb/97W9at26dQkJClJub63S7tm3bqkePHpJcCzsl6dtvv1V8fLzKyspUWFjoVt1AQyHwhCTpm2++kVR3N/SEhAStXLlSL730kkpLSxUdHa2pU6fqmWeeUVCQk+PimqGxY8dKEvN3AkAzcfVV0sZ90pmSxrvm0J6Ndy1PMgdKN8RLg+IcizW9sF4qKpdaBkvDnF+zBgAANBGlpaXKyspyuV12draGDBmidevWacaMGU6HnZJUUVGhw4cPu3xNoDEQeELSxQPPJ554Qk888URjl2S45cuX66233tK5c+eMLgUA4IRAf8ccla9vaZzr9Y+VejTzBX1MJqlViOTv99/PAADAt2RkZOjqq6+WzWYzuhTAY5iGHpIuHnj6KqvVStgJAM1Mt47SwK6utSkslfJLHL87K8ws3d7ftesAAAA0VYSd8Db08IQkacuWRuoOAwBAA7u9n/RdgfStk9NXLdrk2vmDAqRf/kSyBLteG9AUdejQQXPnzlWHDh2MLgUAAMAj6OEJAAC8SlCANPUmqXM7z587OEC6/yYpNsLz5waMEhERocmTJysigv+wAQCAdyDwBAAAXickSPr1UOm6Lp47Z2Qr6aHhUlc6wcHLFBQUaNOmTSooKDC6FAAAAI8g8AQAAF4pKMCxiNG0IVLrUPfP42eShidIj46UosM9Vx/QVGRmZurhhx9WZmam0aUAAAB4BHN4AgAAr9bjCul/xkh7j0vbD0uZp51rFxrk6CF6fZwU0bJBSwQAAADgQQSeAADA6wUFSNd1la7tImWdkY7nSSd/kE6dkUorJJtdCvSX2oc5enFeGS7FRTq2AQAAAGheCDwBAIDPMJkcgSZD0wEAAADvxRyeAAAAgA8zm83q0aOHzGaz0aUAAAB4BD08AQAAAB/WpUsXffDBB0aXAQAA4DH08AQAAAAAAADgNQg8AQAAAB+WlpamPn36KC0tzehSAAAAPILAEwAAAPBhdrtdFRUVstvtRpcCAADgEczhiWYjNDRURUVFRpfhktDQUKNLAAAAAAB4CU+/F7/wxj9UWFyiMEuoHps2sdZnT+C9GEYg8ESzYTKZZLFYjC4DAAAAAABDePq9OCjYrKAKq4KCzbJYLLU+A80VQ9oBAAAAAAAAeA16eAIAAAA+rEuXLlq7dq1iYmKMLgUAAMAjCDwBAAAAH2Y2mxUXF2d0GQAAAB7DkHYAAADAh2VlZempp55SVlaW0aUAAAB4BIEnAAAA4MPy8/O1Zs0a5efnG10KAACARxB4AgAAAAAAAPAaBJ4AAAAAAAAAvAaBJwAAAAAAAACvQeAJAAAA+DA/Pz8lJSXJz49XAwAA4B14qgEAAAB8mM1mU0pKimw2m9GlAAAAeASBJwAAAAAAAACvQeAJAAAAAAAAwGsQeAIAAAAAAADwGgSeAAAAgA9r1aqVxowZo1atWhldCgAAgEcEGF0AAAAAAONER0drwYIFRpcBAADgMfTwBAAAAHxYeXm5Tpw4ofLycqNLAQAA8AgCTwAAAMCHHTlyRCNGjNCRI0eMLgUAAMAjGNKOZsNut6ukpMToMlwSGhoqk8lkdBkAAAAAADR75AJwFoEnmo2SkhK1aNHC6DJcUlRUJIvFYnQZAAAAAAA0e+QCcBZD2gEAAAAAAAB4DQJPAAAAAAAAAF6DIe0AAACAD0tISFB6errRZQAAAHgMPTwBAAAAAAAAeA0CTwAAAMCHHTt2TBMnTtSxY8eMLgUAAMAjGNIOAADgQ8orpeO5UsZpKfO0lF8inS1z7CsulzZ9I8WES50ipNBgY2tF4ygpKdG+fftUUlJidCkAAAAeQeAJAADgA74rlHYckr78ViqrqPuYSpu06WvHnwP9pauvkm6Il65s23h1AgAAAJeLwBMAAMCLlZ6TkvdKXxx1rV2F1RGOfvmtlBAl/WyA1Cq0YWoEAAAAPIk5PAEAALzUf7Kl+etdDzsvdCBLen69tJspHgEAANAM0MMTAADAC+06Kv1jl2S3e+Z8peekv38u5RVJt/aSTCbPnBfGi4qK0vz58xUVFWV0KQAAAB5B4AkAAOBldh+T3v2iYc696WvJ3yQN79Uw50fja926tcaOHWt0GQAANEt+fn6y2WxGl4ELEHgCAAB4kczT0sqdDXuN9fukjq2lXtENex00jtOnT2vjxo0aOXKkwsPDjS4HAIBG0bFjR/Xv318JCQmyWCyy2+0qLi7WgQMHtGfPHmVnZ1/yHFFRUdq4caMeffRRbd68uRGqhrMIPAEAALxEpVVa+YVkc3EY+yMjpLAQqbBUWrTJuTarv5Q6t5NCg12vE01Ldna25s2bp8TERAJPAIBXu+qqqzRt2jTdc889io6++De3GRkZ+tvf/qY33nhDGRkZtfZHRUVp27Zt6tq1q5KTkzVq1Cht3bq1oUqHi1i0yEfl5eVpzpw56tq1q8xms2JiYjRz5kwVFxdrypQpMplMevXVV40u01DTpk2T1WpVZGSk0aUAAOCULWnSqTOutwsLkVqHOn53VmGp9NFe16/V1NjsUsk5xxylnprvFAAANC1RUVF6//339e233+qJJ564ZNgpSTExMXryySd17NgxrV69WldccUWN81WFnZIjHD106FCD1Q/X0cPTB6WmpmrkyJHKycmRxWJRz549derUKb388ss6evSoTp8+LUlKTEw0tlAPuPfeezV58mQNGTKkepvJZNLMmTM1bdo0xcbGKjc3V6tXr9bTTz+tkpKS6uPWrl2r1157TWPGjNHSpUuNKB8AAKedq5S2HWzca+4+Jo3uK7UKbdzresKpM9L2Q9Lu446fnSSFBkkDOkuD4qV2LQ0tDwAAeMh9992nxYsXq1WrVtXbKioq9Omnn2r37t366quvlJeXJ0mKiIjQ1VdfraSkJN14440KDAyUv7+/7rjjDg0bNkyzZs3SJ598UiPsPHz4sIYMGaKsrCxD7g91I/D0MXl5eRozZoxycnI0e/ZsPfPMM2rZ0vFEv2DBAs2dO1cBAQEymUzq06ePwdU2jMWLF2vmzJn64IMP9OKLL6pHjx6aMWOGrr76ag0bNkz2H7t3ZGdna/fu3Ro/fjyBJwCgyUs96eip2JhsdmnnEWlEM3pksNqkD3dL2w/X3ldyzhEabzvouCdWowcAoPny9/fX0qVLdd9991Vvy8nJ0SuvvKI333xT3333XZ3tVq1aJUmKjIzUr371K02fPl0dOnRQmzZt9NZbb6mgoKA6PCXsbLoY0u5jZsyYoczMTE2fPl0LFy6sDjslac6cOerbt68qKysVGxursLAwAyttGD179tRDDz2kNWvWaMKECVq2bJlmz56tRx55REOHDtXEiRNrHP/RRx9p6NChslgsBlUMAIBzvjhizHV3Hmk+Q8HtdmnVrrrDzgtt+lrasK/ha2oKLBaLBg0axPMOAMBr+Pn56e23364Rdq5YsUI9evTQH//4x3rDzvPl5ORo3rx56tGjh95+++3q7YSdzQOBpw9JT0/XqlWrFBERoeeee67OY/r16ydJ6tu3b619H374oa6//npZLBa1atVKgwYN0oEDBxq0Zk+766675Ofnp5deeqnG9qVLl6q4uFh33313je3Jyckym80aMWJEI1YJAIBrKq3SiR+MuXZBqXS62Jhru+qrE9KX3zp//L8OSN9+33D1NBWxsbFatmyZYmNjjS4FAACPeO6553TXXXdJksrLy/Wzn/1M9913n/Lz810+15kzZ/TEE08oJyenxvZNmzYRdjZhBJ4+5N1335XNZtOkSZPUokWLOo8JCXGsVnBh4Pnyyy/rZz/7mW644QatXbtW7777roYNG6bS0tIGr9uTkpKSZLVa9eWXX9bYXl5ertTUVCUlJdXYnpaWpsOHD2v8+PGNWCUAAK7JLnAM1TZK5mnjru2K7W6sJfCZD6w/YLVaVVRUJKvVanQpAABctkGDBunRRx+V5Jirc8KECXrvvffcPl/VAkVVCxpXTYP3wAMP6Nprr738gtEgCDx9yJYtWySpxgI+F8rMzJRUM/A8evSoHnvsMS1evFgLFizQzTffrFGjRul3v/ud+vfv37BFe9gVV1yhvLw8nTtXe5KzrKwstWvXToGBgTW2Jycna9SoUfL392+sMgEAcEmWGyuze1JzCDxzCqRvc11v93WGVFTm+XqakoMHDyopKUkHDzbyqlcAAHhYSEiIli9fLj8/R9z1xBNPaP369W6f78LV2A8fPqznn39ekmOO0L/+9a8KDg6+/MLhcSxa5ENOnDghSbrqqqvq3F9ZWakdO3ZIqhl4Ll++XIGBgZo6darHaunfv3+t7uCXYrNdfteV0NBQlZeX17mvrKys+piCgoLq7cnJyXr00Uc1ePBgbd261aXrxcXFVf9FCwBAQ4m7car63vZMnfseGSGFhVy8fZj5v78/e3v9xxWWSos21d7++tIVmpb8lJPVGiOq92gNvPsNl9tZbdL1Q8fq9Mm9DVBVw5k8ebLTx2ZnZ0uSNmzYoK+++srpditWrHCxqqbn9vtmydIiTNk52YqOjq712dtx/7Xv19d/Bty/b92/1Px+BpfKBSZPnqz4+HhJ0s6dO7V48WK3r1VX2DlkyBDl5OTo5ptv1oABA9SjRw/dc889WrZsWb3nIRdwX2RkpHbv3u1WWwJPH1Jc7Jhgq75h6KtWrVJeXp5atmypTp06VW///PPP1a1bN/3973/XvHnzlJGRobi4OD399NPVc2K4Kicnx5C5LkpKStS+ffs695nN5upjzlc1vMudHp5VLxAAADSkyLP1T6IZFiK1DnXuPH5+zh97vtLSsiY/h1WLq4rcbns6v7DJ39+FLnyeuZiqL33LyspcatfcfiZ1sf34nGezWpWVlVXrs7fj/mvfr6//DLh/37p/yft+Bg888ECNP7vbcaq+sLPqZ/Lggw8qJSWl+joXCzzJBYxB4OlDIiMjdebMGe3du1cDBw6ssS87O1uPPfaYJKlPnz4ymUw19mVlZemJJ57Q/PnzFRMTozfffFM///nP1a5dOw0bNsytWlxls9ku+y+KU6dOqWfPngoKCqo1rD0qKkq5ubmqqKiosX38+PHKz8/Xtm3bXL5ex44d+SYHANDgLKH1D6UqdGK67TCzI+y02aTCiwzfru9c5iB/RUVFXfpCBrK4MdrMbrfLZDIpLMRfauL3d6HQUOeT66ovfc1ms0vtmvr/5s7w+/ELbT9/x3/DF372dtx/7fv19Z8B9+9b9y81v5/BxXKBG264Qb169ZIkbd++XampqW5d41JhpyTt3r1bu3bt0rXXXqurr75a1113nb744os6z0cu4D53sqMqBJ4+ZNiwYUpPT9f8+fM1fPjw6m7eKSkpuueee5SXlydJSkxMrNHOZrOpqKhIb7/9dvXiPTfffLPS0tL0+9//3q3A050uycXFxfUutuSslJQU3XrrrRowYIC2b99evT04OFiJiYn69NNPa7UZN26cNmzYoMrKSpevd/jwYVkslsuqGQCAS0nLkv6yre59dQ1Bv9Cztzt6dhb+//buPK7qKv/j+PuyXkBcEFdQSXEBFFfcFdRccSstbbPUNLNFG0vnN1nTTI5NRTRTaeNoaWOO06RO7pblUlrjjuaSmTsKriGCsvP7g4EkQLnXK1/43tfz8eCR93u/53s+53aj6/ue7zlp0iv/sb3/3z43Tt3+5rilb+6EzOy8saUWv7JNsSwWi2pWlg7s/Eo3fBdcIdiyHueBAwf04YcfasCAAQoLCyt1uxkzZthTWrkyc9YiJaekqk7tOoqPjy/y2OwYf9HxOvtrwPida/xSxXsNbpYL9O3bt+DPf//73+26fmnCzhv7yN+0qE+fPiUGnuQCxiBidiJTp05V9erVdfr0aYWFhalFixZq3Lix2rdvr4YNG6pnz56Siu7Q7ufnJ0mFgk2LxaK7775b+/fvL7sBOMAnn3yinJwcTZ48udDxcePGycfHR4sWLSp0vFmzZmratKmWL19ehlUCAGCben4G91/d2P5Lw91V6tDI9nZdGqvChZ22atKkibZu3VrwZTgAABVR27ZtC/68efNmm9vbEnb+uo8b+0b5QODpRAIDA/XNN98oOjpaVqtVJ06ckJ+fn+bMmaPVq1frxx9/lFQ08LzZN/35az5VFPv379esWbM0bNgwLV26VGPHjlVMTIxiY2O1adMm/fOf/yx0/pAhQ5Senq61a9caVDEAALfma8M6nY7mYpHqVjWmb1t1b/rLBk2lUbOyfSFpRePu7i4/Pz+5u7sbXQoAAHbLDx0vXryoU6dO2dTW1rBTko4ePaqkpCRJeRszo3wh8HQyISEhWrVqla5evaqrV69q27ZtGj9+vFJTU3XixAm5uLgUrHmRb8iQIZKkL774ouBYTk6O1q9fr4iIiDKt31ZxcXFFdhCdPHmypkyZorCwMM2aNUsjR47Uu+++q4EDByo3N7fQuUOGDNHGjRt19erVMqwaAADbtaxvTL+hAZJHBVkkqaq3NL6HVKkU63lWryQ90UOyOkEGeOrUKU2cONHmvxwCAFBeuLq6FmxQnD+Zq7TsCTvz5fdVt25dGyvGnVZBPp7iTjtw4IByc3PVpEmTIovVDxo0SN26ddP48eN16dIl1a9fX/PmzdOBAwe0fv16gyounb1792rv3r2FjuXk5Cg2NlaxsbE3bVurVi116NBBTz311J0sEQAAh+jcWNpc+mUbHaZrBbsLOtBPmtxXWrdP2nNKyv7V5q0eblK7u6T+LfJmzjqDq1evauPGjXzmAQBUWBaLRS+++KKsVqvNa49+8MEHdoWdkrRgwQJ9/vnnun79ulxcXOzeFR6OR+AJSdL3338vqejt7FLeL44VK1Zo2rRp+t3vfqfk5GS1bNlSa9asKVj304wGDx4sSazfCQCoEGpVlprWlg4nll2fNXylJvZvnmkYf1/p4S7S0LbSnpPSqjgpPUvycpdeHip5eRhdIQAAsEVWVpZmzpxpV9tx48Zp06ZNys7OtinslKT333/frj5x53FLOyTdPPCUpKpVq2rOnDm6cOGC0tPTtX379kI7oJnRhx9+KC8vLyUkJBhdCgAApTK0reRahp/uhkfkreFZUVWySt2a/nLbuocbYScAAM7m9OnTioqKsjnsRPlG4AlJtw48nVF2drYyMjKMLgMAgFKrU1Xq16Js+uoULDWtUzZ9AQAA3EmnT58m7DQZbmmHJGnDhg1GlwAAABygZ6j0Y6J05Fzp2yRfL/zPW6ldRRrSxvbaUD7VqlVL06ZNU61atYwuBQAAwCEIPAEAAEzE1UUaGym9v0E6ebF0bWLXlf76/pWkCT2dY/dyZ+Hv76/HHnvM6DIAAAAchlvaAQAATMbqLk3sKYXUdex16/lJz/aRqno79row1pUrV7Ru3TpduXLF6FIAAAAcgsATAADAhDzdpfFR0n3tJc/bvKfH1UXqHy5N7itV9nJIeShH4uPj9dxzzyk+Pt7oUgAAAByCW9oBAAB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",
-      "text/plain": [
-       "<Figure size 1715.41x869.556 with 1 Axes>"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "linear_complexity_circuit = linear_complexity_GHZ(N)\n",
-    "linear_complexity_circuit.draw(output='mpl')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Generate Logaritmic Complexity Circuits for $|\\text{GHZ}_{2^m}\\rangle$"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def _log_complexity_GHZ(m: int) -> QuantumCircuit:\n",
-    "  if not isinstance(m, int):\n",
-    "    raise TypeError(\"Only integer arguments accepted.\")\n",
-    "  if m < 0:\n",
-    "    raise ValueError(\"`m` must be at least 0 (evaluated 2^m).\")\n",
-    "\n",
-    "  if m == 0:\n",
-    "    c = QuantumCircuit([Qubit()])\n",
-    "    c.reset(0)\n",
-    "    c.h(0)\n",
-    "  else:\n",
-    "    c = _log_complexity_GHZ(m - 1)\n",
-    "    for i in range(c.num_qubits):\n",
-    "      c.add_bits([Qubit()])\n",
-    "      new_qubit_index = c.num_qubits - 1\n",
-    "      c.reset(new_qubit_index)\n",
-    "      c.cx(i, new_qubit_index)\n",
-    "  return c"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Generate Logaritmic Complexity Circuits for $|\\text{GHZ}_N\\rangle$"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def log_complexity_GHZ(N: int) -> QuantumCircuit:\n",
-    "  if not isinstance(N, int):\n",
-    "    raise TypeError(\"Only an integer argument is accepted.\")\n",
-    "  if N < 1:\n",
-    "    raise ValueError(\"There must be one or more qubits.\")\n",
-    "\n",
-    "  m = math.ceil(math.log2(N))\n",
-    "  num_qubits_to_erase = 2**m - N\n",
-    "  old_circuit = _log_complexity_GHZ(m=m)\n",
-    "  new_num_qubits = old_circuit.num_qubits - num_qubits_to_erase\n",
-    "  new_circuit = QuantumCircuit(new_num_qubits)\n",
-    "  for gate in old_circuit.data:\n",
-    "    qubits_affected = gate.qubits\n",
-    "    if all(old_circuit.find_bit(qubit).index < new_num_qubits for qubit in qubits_affected):\n",
-    "      new_circuit.append(gate[0], [old_circuit.find_bit(qubit).index for qubit in qubits_affected])\n",
-    "  new_circuit.measure_active()\n",
-    "  return new_circuit\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1715.41x785.944 with 1 Axes>"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "log_complexity_circuit = log_complexity_GHZ(N)\n",
-    "log_complexity_circuit.draw(output='mpl')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "---"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Get Token"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "with open('token.txt', 'r') as f:\n",
-    "  token = f.read().strip()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Setup IBM Machine"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "backend = least_busy(IBMProvider(token).backends(min_num_qubits=N, simulator=use_sim, operational=True))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Transpile Circuits"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "```python\n",
-    "linear_complexity_mapped_circuit = transpile(linear_complexity_circuit, backend=backend)\n",
-    "log_complexity_mapped_circuit = transpile(log_complexity_circuit, backend=backend)\n",
-    "```"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Run Circuits"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "TranspilerError",
-     "evalue": "\"The number of qubits for Instruction(name='cx', num_qubits=2, num_clbits=0, params=[]) does not match the number of qubits in the properties dictionary: (0,)\"",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mTranspilerError\u001b[0m                           Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[11], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m linear_complexity_job \u001b[39m=\u001b[39m execute(linear_complexity_circuit, backend\u001b[39m=\u001b[39;49mbackend, shots\u001b[39m=\u001b[39;49mshots)\n\u001b[1;32m      2\u001b[0m log_complexity_job \u001b[39m=\u001b[39m execute(log_complexity_circuit, backend\u001b[39m=\u001b[39mbackend, shots\u001b[39m=\u001b[39mshots)\n",
-      "File \u001b[0;32m/usr/local/lib/python3.8/site-packages/qiskit/execute_function.py:302\u001b[0m, in \u001b[0;36mexecute\u001b[0;34m(experiments, backend, basis_gates, coupling_map, backend_properties, initial_layout, seed_transpiler, optimization_level, pass_manager, qobj_id, qobj_header, shots, memory, max_credits, seed_simulator, default_qubit_los, default_meas_los, qubit_lo_range, meas_lo_range, schedule_los, meas_level, meas_return, memory_slots, memory_slot_size, rep_time, rep_delay, parameter_binds, schedule_circuit, inst_map, meas_map, scheduling_method, init_qubits, **run_config)\u001b[0m\n\u001b[1;32m    299\u001b[0m     experiments \u001b[39m=\u001b[39m pass_manager\u001b[39m.\u001b[39mrun(experiments)\n\u001b[1;32m    300\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m    301\u001b[0m     \u001b[39m# transpiling the circuits using given transpile options\u001b[39;00m\n\u001b[0;32m--> 302\u001b[0m     experiments \u001b[39m=\u001b[39m transpile(\n\u001b[1;32m    303\u001b[0m         experiments,\n\u001b[1;32m    304\u001b[0m         basis_gates\u001b[39m=\u001b[39;49mbasis_gates,\n\u001b[1;32m    305\u001b[0m         coupling_map\u001b[39m=\u001b[39;49mcoupling_map,\n\u001b[1;32m    306\u001b[0m         backend_properties\u001b[39m=\u001b[39;49mbackend_properties,\n\u001b[1;32m    307\u001b[0m         initial_layout\u001b[39m=\u001b[39;49minitial_layout,\n\u001b[1;32m    308\u001b[0m         seed_transpiler\u001b[39m=\u001b[39;49mseed_transpiler,\n\u001b[1;32m    309\u001b[0m         optimization_level\u001b[39m=\u001b[39;49moptimization_level,\n\u001b[1;32m    310\u001b[0m         backend\u001b[39m=\u001b[39;49mbackend,\n\u001b[1;32m    311\u001b[0m     )\n\u001b[1;32m    313\u001b[0m \u001b[39mif\u001b[39;00m schedule_circuit:\n\u001b[1;32m    314\u001b[0m     experiments \u001b[39m=\u001b[39m schedule(\n\u001b[1;32m    315\u001b[0m         circuits\u001b[39m=\u001b[39mexperiments,\n\u001b[1;32m    316\u001b[0m         backend\u001b[39m=\u001b[39mbackend,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    319\u001b[0m         method\u001b[39m=\u001b[39mscheduling_method,\n\u001b[1;32m    320\u001b[0m     )\n",
-      "File \u001b[0;32m/usr/local/lib/python3.8/site-packages/qiskit/compiler/transpiler.py:326\u001b[0m, in \u001b[0;36mtranspile\u001b[0;34m(circuits, backend, basis_gates, inst_map, coupling_map, backend_properties, initial_layout, layout_method, routing_method, translation_method, scheduling_method, instruction_durations, dt, approximation_degree, timing_constraints, seed_transpiler, optimization_level, callback, output_name, unitary_synthesis_method, unitary_synthesis_plugin_config, target, hls_config, init_method, optimization_method, ignore_backend_supplied_default_methods)\u001b[0m\n\u001b[1;32m    314\u001b[0m \u001b[39mif\u001b[39;00m (\n\u001b[1;32m    315\u001b[0m     scheduling_method \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m\n\u001b[1;32m    316\u001b[0m     \u001b[39mand\u001b[39;00m backend \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m\n\u001b[1;32m    317\u001b[0m     \u001b[39mand\u001b[39;00m target \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m\n\u001b[1;32m    318\u001b[0m     \u001b[39mand\u001b[39;00m \u001b[39mnot\u001b[39;00m instruction_durations\n\u001b[1;32m    319\u001b[0m ):\n\u001b[1;32m    320\u001b[0m     warnings\u001b[39m.\u001b[39mwarn(\n\u001b[1;32m    321\u001b[0m         \u001b[39m\"\u001b[39m\u001b[39mWhen scheduling circuits without backend,\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m    322\u001b[0m         \u001b[39m\"\u001b[39m\u001b[39m \u001b[39m\u001b[39m'\u001b[39m\u001b[39minstruction_durations\u001b[39m\u001b[39m'\u001b[39m\u001b[39m should be usually provided.\u001b[39m\u001b[39m\"\u001b[39m,\n\u001b[1;32m    323\u001b[0m         \u001b[39mUserWarning\u001b[39;00m,\n\u001b[1;32m    324\u001b[0m     )\n\u001b[0;32m--> 326\u001b[0m unique_transpile_args, shared_args \u001b[39m=\u001b[39m _parse_transpile_args(\n\u001b[1;32m    327\u001b[0m     circuits,\n\u001b[1;32m    328\u001b[0m     backend,\n\u001b[1;32m    329\u001b[0m     basis_gates,\n\u001b[1;32m    330\u001b[0m     inst_map,\n\u001b[1;32m    331\u001b[0m     coupling_map,\n\u001b[1;32m    332\u001b[0m     backend_properties,\n\u001b[1;32m    333\u001b[0m     initial_layout,\n\u001b[1;32m    334\u001b[0m     layout_method,\n\u001b[1;32m    335\u001b[0m     routing_method,\n\u001b[1;32m    336\u001b[0m     translation_method,\n\u001b[1;32m    337\u001b[0m     scheduling_method,\n\u001b[1;32m    338\u001b[0m     instruction_durations,\n\u001b[1;32m    339\u001b[0m     dt,\n\u001b[1;32m    340\u001b[0m     approximation_degree,\n\u001b[1;32m    341\u001b[0m     seed_transpiler,\n\u001b[1;32m    342\u001b[0m     optimization_level,\n\u001b[1;32m    343\u001b[0m     callback,\n\u001b[1;32m    344\u001b[0m     output_name,\n\u001b[1;32m    345\u001b[0m     timing_constraints,\n\u001b[1;32m    346\u001b[0m     unitary_synthesis_method,\n\u001b[1;32m    347\u001b[0m     unitary_synthesis_plugin_config,\n\u001b[1;32m    348\u001b[0m     target,\n\u001b[1;32m    349\u001b[0m     hls_config,\n\u001b[1;32m    350\u001b[0m     init_method,\n\u001b[1;32m    351\u001b[0m     optimization_method,\n\u001b[1;32m    352\u001b[0m     ignore_backend_supplied_default_methods,\n\u001b[1;32m    353\u001b[0m )\n\u001b[1;32m    354\u001b[0m \u001b[39m# Get transpile_args to configure the circuit transpilation job(s)\u001b[39;00m\n\u001b[1;32m    355\u001b[0m \u001b[39mif\u001b[39;00m coupling_map \u001b[39min\u001b[39;00m unique_transpile_args:\n",
-      "File \u001b[0;32m/usr/local/lib/python3.8/site-packages/qiskit/compiler/transpiler.py:652\u001b[0m, in \u001b[0;36m_parse_transpile_args\u001b[0;34m(circuits, backend, basis_gates, inst_map, coupling_map, backend_properties, initial_layout, layout_method, routing_method, translation_method, scheduling_method, instruction_durations, dt, approximation_degree, seed_transpiler, optimization_level, callback, output_name, timing_constraints, unitary_synthesis_method, unitary_synthesis_plugin_config, target, hls_config, init_method, optimization_method, ignore_backend_supplied_default_methods)\u001b[0m\n\u001b[1;32m    648\u001b[0m \u001b[39m# If target is not specified and any hardware constraint object is\u001b[39;00m\n\u001b[1;32m    649\u001b[0m \u001b[39m# manually specified then do not use the target from the backend as\u001b[39;00m\n\u001b[1;32m    650\u001b[0m \u001b[39m# it is invalidated by a custom basis gate list or a custom coupling map\u001b[39;00m\n\u001b[1;32m    651\u001b[0m \u001b[39melif\u001b[39;00m basis_gates \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m \u001b[39mand\u001b[39;00m coupling_map \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m--> 652\u001b[0m     target \u001b[39m=\u001b[39m _parse_target(backend, target)\n\u001b[1;32m    654\u001b[0m basis_gates \u001b[39m=\u001b[39m _parse_basis_gates(basis_gates, backend)\n\u001b[1;32m    655\u001b[0m initial_layout \u001b[39m=\u001b[39m _parse_initial_layout(initial_layout, circuits)\n",
-      "File \u001b[0;32m/usr/local/lib/python3.8/site-packages/qiskit/compiler/transpiler.py:1004\u001b[0m, in \u001b[0;36m_parse_target\u001b[0;34m(backend, target)\u001b[0m\n\u001b[1;32m   1003\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m_parse_target\u001b[39m(backend, target):\n\u001b[0;32m-> 1004\u001b[0m     backend_target \u001b[39m=\u001b[39m \u001b[39mgetattr\u001b[39;49m(backend, \u001b[39m\"\u001b[39;49m\u001b[39mtarget\u001b[39;49m\u001b[39m\"\u001b[39;49m, \u001b[39mNone\u001b[39;49;00m)\n\u001b[1;32m   1005\u001b[0m     \u001b[39mif\u001b[39;00m target \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m   1006\u001b[0m         target \u001b[39m=\u001b[39m backend_target\n",
-      "File \u001b[0;32m/usr/local/lib/python3.8/site-packages/qiskit_ibm_provider/ibm_backend.py:329\u001b[0m, in \u001b[0;36mIBMBackend.target\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    327\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_get_properties()\n\u001b[1;32m    328\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_get_defaults()\n\u001b[0;32m--> 329\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_convert_to_target()\n\u001b[1;32m    330\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_target\n",
-      "File \u001b[0;32m/usr/local/lib/python3.8/site-packages/qiskit_ibm_provider/ibm_backend.py:284\u001b[0m, in \u001b[0;36mIBMBackend._convert_to_target\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    282\u001b[0m \u001b[39m\u001b[39m\u001b[39m\"\"\"Converts backend configuration, properties and defaults to Target object\"\"\"\u001b[39;00m\n\u001b[1;32m    283\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_target:\n\u001b[0;32m--> 284\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_target \u001b[39m=\u001b[39m convert_to_target(\n\u001b[1;32m    285\u001b[0m         configuration\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_configuration,\n\u001b[1;32m    286\u001b[0m         properties\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_properties,\n\u001b[1;32m    287\u001b[0m         defaults\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_defaults,\n\u001b[1;32m    288\u001b[0m     )\n",
-      "File \u001b[0;32m/usr/local/lib/python3.8/site-packages/qiskit_ibm_provider/utils/backend_converter.py:120\u001b[0m, in \u001b[0;36mconvert_to_target\u001b[0;34m(configuration, properties, defaults)\u001b[0m\n\u001b[1;32m    118\u001b[0m gate_len \u001b[39m=\u001b[39m \u001b[39mlen\u001b[39m(gate\u001b[39m.\u001b[39mcoupling_map[\u001b[39m0\u001b[39m]) \u001b[39mif\u001b[39;00m \u001b[39mhasattr\u001b[39m(gate, \u001b[39m\"\u001b[39m\u001b[39mcoupling_map\u001b[39m\u001b[39m\"\u001b[39m) \u001b[39melse\u001b[39;00m \u001b[39m0\u001b[39m\n\u001b[1;32m    119\u001b[0m \u001b[39mif\u001b[39;00m name \u001b[39min\u001b[39;00m name_mapping:\n\u001b[0;32m--> 120\u001b[0m     target\u001b[39m.\u001b[39;49madd_instruction(name_mapping[name], gate_props)\n\u001b[1;32m    121\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m    122\u001b[0m     custom_gate \u001b[39m=\u001b[39m Gate(name, gate_len, [])\n",
-      "File \u001b[0;32m/usr/local/lib/python3.8/site-packages/qiskit/transpiler/target.py:381\u001b[0m, in \u001b[0;36mTarget.add_instruction\u001b[0;34m(self, instruction, properties, name)\u001b[0m\n\u001b[1;32m    379\u001b[0m \u001b[39mfor\u001b[39;00m qarg \u001b[39min\u001b[39;00m properties:\n\u001b[1;32m    380\u001b[0m     \u001b[39mif\u001b[39;00m qarg \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m \u001b[39mand\u001b[39;00m \u001b[39mlen\u001b[39m(qarg) \u001b[39m!=\u001b[39m instruction\u001b[39m.\u001b[39mnum_qubits:\n\u001b[0;32m--> 381\u001b[0m         \u001b[39mraise\u001b[39;00m TranspilerError(\n\u001b[1;32m    382\u001b[0m             \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mThe number of qubits for \u001b[39m\u001b[39m{\u001b[39;00minstruction\u001b[39m}\u001b[39;00m\u001b[39m does not match the number \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m    383\u001b[0m             \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mof qubits in the properties dictionary: \u001b[39m\u001b[39m{\u001b[39;00mqarg\u001b[39m}\u001b[39;00m\u001b[39m\"\u001b[39m\n\u001b[1;32m    384\u001b[0m         )\n\u001b[1;32m    385\u001b[0m     \u001b[39mif\u001b[39;00m qarg \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m    386\u001b[0m         \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mnum_qubits \u001b[39m=\u001b[39m \u001b[39mmax\u001b[39m(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mnum_qubits, \u001b[39mmax\u001b[39m(qarg) \u001b[39m+\u001b[39m \u001b[39m1\u001b[39m)\n",
-      "\u001b[0;31mTranspilerError\u001b[0m: \"The number of qubits for Instruction(name='cx', num_qubits=2, num_clbits=0, params=[]) does not match the number of qubits in the properties dictionary: (0,)\""
-     ]
-    }
-   ],
-   "source": [
-    "linear_complexity_job = execute(linear_complexity_circuit, backend=backend, shots=shots)\n",
-    "log_complexity_job = execute(log_complexity_circuit, backend=backend, shots=shots)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Results"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "linear_complexity_result = linear_complexity_job.result()\n",
-    "log_complexity_result = log_complexity_job.result()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "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": "3.8.16"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/requirements.txt b/requirements.txt
index 27bc6cc..10d2d32 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,8 +1,4 @@
 qiskit==0.42.1
-qiskit-ibm-provider==0.5.2
 matplotlib==3.7.1
 jupyterlab==3.6.3
 pylatexenc==2.10
-# ipywidgets==8.0.6
-# ipyvuetify==1.8.10
-# pyperclip==1.8.2