diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..262a959
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,12 @@
+
+.vscode/
+**/.vscode/
+.DS_Store
+.DS_Store?
+**/.DS_Store
+**/.DS_Store?
+
+__pycache__/
+**/__pycache__/
+
+**/*.ipynb_checkpoints/
\ No newline at end of file
diff --git a/backend.ipynb b/backend.ipynb
new file mode 100644
index 0000000..bbfc4f1
--- /dev/null
+++ b/backend.ipynb
@@ -0,0 +1,390 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 116,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# import qiskit\n",
+    "from qiskit import QuantumCircuit, QuantumRegister, ClassicalRegister\n",
+    "from qiskit import BasicAer, execute\n",
+    "from qiskit.circuit.library import QFT\n",
+    "from qiskit.quantum_info import Statevector\n",
+    "from qiskit.visualization import plot_bloch_multivector\n",
+    "\n",
+    "from qiskit_ionq import IonQProvider \n",
+    "\n",
+    "#Call provider and set token value\n",
+    "# provider = IonQProvider(token='EDEq7Meo9Re0MIVV2loVBe2hZJCUG4VY')\n",
+    "\n",
+    "# numpy\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "\n",
+    "# plotting\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 117,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1, 2, 3, 4, 5])"
+      ]
+     },
+     "execution_count": 117,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.arange(1, 6)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 118,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def normalize_data(data):\n",
+    "    for d in data:\n",
+    "        d = d / np.linalg.norm(d)\n",
+    "    \n",
+    "    return data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 119,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/opt/homebrew/anaconda3/envs/iqh/lib/python3.11/site-packages/qiskit/visualization/circuit/matplotlib.py:266: FutureWarning: The default matplotlib drawer scheme will be changed to \"iqp\" in a following release. To silence this warning, specify the current default explicitly as style=\"clifford\", or the new default as style=\"iqp\".\n",
+      "  self._style, def_font_ratio = load_style(self._style)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 1440x480 with 3 Axes>"
+      ]
+     },
+     "execution_count": 119,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# player = QuantumRegister(1, name='player')\n",
+    "# n_starting_people = 2\n",
+    "\n",
+    "# network = QuantumRegister(n_starting_people, name='network')\n",
+    "# bit = ClassicalRegister(1, name='bit')\n",
+    "\n",
+    "# qc = QuantumCircuit(player, network, bit)\n",
+    "\n",
+    "# # get the data\n",
+    "# data = pd.read_csv('records.csv').to_numpy()\n",
+    "# data = normalize_data(data)\n",
+    "\n",
+    "# # people considered in the network\n",
+    "# people_we_consider = []\n",
+    "\n",
+    "# # creates random network starting states\n",
+    "# def initial_state(n):\n",
+    "#     temp = QuantumCircuit(n)\n",
+    "#     for i in range(n):\n",
+    "#         index = np.random.randint(0, len(data))\n",
+    "#         people_we_consider.append(index)\n",
+    "#         # cartesian to spherical\n",
+    "#         theta = np.arctan(data[index][1] / data[index][0])\n",
+    "#         phi = np.arccos(data[index][2] / np.linalg.norm(data[index]))\n",
+    "#         temp.rx(theta, i)\n",
+    "#         temp.ry(phi, i)\n",
+    "#     return temp\n",
+    "    \n",
+    "\n",
+    "# # gives the people in the network the random starting positions\n",
+    "# q2 = initial_state(n_starting_people)\n",
+    "# q2.draw()\n",
+    "# qc.compose(q2, np.arange(1, n_starting_people+1), inplace=True)\n",
+    "\n",
+    "# # debug \n",
+    "# qc.draw(output='mpl')\n",
+    "# plot_bloch_multivector(qc)\n",
+    "\n",
+    "\n",
+    "# # epochs = 10\n",
+    "\n",
+    "# # player_interactions = [(np.random.randint(0, n_starting_people+1)) for i ]\n",
+    "\n",
+    "# # for i in range (epochs):\n",
+    "#     # s = input('interact with:')\n",
+    "\n",
+    "# # while True:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 120,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1 2]\n"
+     ]
+    }
+   ],
+   "source": [
+    "player_interactions = np.arange(1, n_starting_people+1)\n",
+    "np.random.shuffle(player_interactions)\n",
+    "print(player_interactions)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 121,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                                         ┌────────────┐┌─┐\n",
+      "   player: ──────────────────────────────┤ Rx(2.9108) ├┤M├\n",
+      "           ┌─────────────┐ ┌────────────┐└─────┬──────┘└╥┘\n",
+      "network_0: ┤ Rx(0.46044) ├─┤ Ry(0.8409) ├──────■────────╫─\n",
+      "           ├─────────────┴┐├────────────┤               ║ \n",
+      "network_1: ┤ Rx(0.056137) ├┤ Ry(1.4026) ├───────────────╫─\n",
+      "           └──────────────┘└────────────┘               ║ \n",
+      "    bit: 1/═════════════════════════════════════════════╩═\n",
+      "                                                        0 \n",
+      "                                         ┌──────────────┐┌─┐\n",
+      "   player: ──────────────────────────────┤ Rx(0.050998) ├┤M├\n",
+      "           ┌─────────────┐ ┌────────────┐└──────┬───────┘└╥┘\n",
+      "network_0: ┤ Rx(0.46044) ├─┤ Ry(0.8409) ├───────┼─────────╫─\n",
+      "           ├─────────────┴┐├────────────┤       │         ║ \n",
+      "network_1: ┤ Rx(0.056137) ├┤ Ry(1.4026) ├───────■─────────╫─\n",
+      "           └──────────────┘└────────────┘                 ║ \n",
+      "    bit: 1/═══════════════════════════════════════════════╩═\n",
+      "                                                          0 \n"
+     ]
+    }
+   ],
+   "source": [
+    "# gates_types = ['crx', 'cry', 'crz']\n",
+    "# fix gate as crx\n",
+    "gates_types = ['crx']\n",
+    "gate = [np.random.choice(gates_types) for _ in range(n_starting_people)] # list of gates as str\n",
+    "\n",
+    "circuits = [qc.copy() for _ in range(n_starting_people)]\n",
+    "\n",
+    "for i, circ in enumerate(circuits):\n",
+    "    theta = (np.sum(data[people_we_consider[i]]) % 1)  * np.pi\n",
+    "    if gate[i] == 'crx':\n",
+    "        circ.crx(theta, network[i], player)\n",
+    "    elif gate[i] == 'cry':\n",
+    "        circ.cry(theta, network[i], player)\n",
+    "    else:\n",
+    "        circ.crz(theta, network[i], player)\n",
+    "    \n",
+    "    circ.measure(player, bit)\n",
+    "    # plt.figure()\n",
+    "    # circ.draw()\n",
+    "    print(circ)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 122,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'1': 216, '0': 808}\n",
+      "{'0': 1024}\n",
+      "                                         ┌────────────┐┌─┐\n",
+      "   player: ──────────────────────────────┤ Rx(2.9108) ├┤M├\n",
+      "           ┌─────────────┐ ┌────────────┐└─────┬──────┘└╥┘\n",
+      "network_0: ┤ Rx(0.46044) ├─┤ Ry(0.8409) ├──────■────────╫─\n",
+      "           ├─────────────┴┐├────────────┤               ║ \n",
+      "network_1: ┤ Rx(0.056137) ├┤ Ry(1.4026) ├───────────────╫─\n",
+      "           └──────────────┘└────────────┘               ║ \n",
+      "    bit: 1/═════════════════════════════════════════════╩═\n",
+      "                                                        0 \n"
+     ]
+    }
+   ],
+   "source": [
+    "# run the circuit\n",
+    "better_circuit = circ[0]\n",
+    "max_ = -1\n",
+    "\n",
+    "index_of_person_who_in_circuit = 0\n",
+    "\n",
+    "for i, circ in enumerate(circuits):\n",
+    "    backend = BasicAer.get_backend('qasm_simulator')\n",
+    "    job = execute(circ, backend)\n",
+    "    result = job.result()\n",
+    "    counts = result.get_counts()\n",
+    "\n",
+    "    try:\n",
+    "        if counts['1'] > max_:\n",
+    "            max_ = counts['1']\n",
+    "            better_circuit = circ\n",
+    "            index_of_person_who_in_circuit = i\n",
+    "    except:\n",
+    "        pass\n",
+    "    \n",
+    "    print(counts)\n",
+    "\n",
+    "print(better_circuit)\n",
+    "\n",
+    "# remove the person who is in the circuit from our data\n",
+    "data = np.delete(data, people_we_consider[index_of_person_who_in_circuit], axis=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 130,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# negative theta is representative of sad story depending on magnitude\n",
+    "# positive theta is good\n",
+    "def life_event():\n",
+    "    circ = QuantumCircuit(1)\n",
+    "    event_types = ['rx', 'rz']\n",
+    "    event = np.random.choice(event_types)\n",
+    "    theta = (np.random.normal() % 1) * np.random.choice([-1, 1]) * np.pi/2\n",
+    "    if event[i] == 'rx':\n",
+    "        circ.rx(theta, 0)\n",
+    "    elif event[i] == 'rz':\n",
+    "        circ.ry(theta, 0)\n",
+    "    return circ"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 129,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.07655120252953096"
+      ]
+     },
+     "execution_count": 129,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.random.normal()%1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 123,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# adding new people to the network\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# creates random network starting states\n",
+    "def initial_state(n):\n",
+    "    temp = QuantumCircuit(n)\n",
+    "    for i in range(n):\n",
+    "        index = np.random.randint(0, len(data))\n",
+    "        people_we_consider.append(index)\n",
+    "        # cartesian to spherical\n",
+    "        theta = np.arctan(data[index][1] / data[index][0])\n",
+    "        phi = np.arccos(data[index][2] / np.linalg.norm(data[index]))\n",
+    "        temp.rx(theta, i)\n",
+    "        temp.ry(phi, i)\n",
+    "    return temp\n",
+    "\n",
+    "def core_story(n_interactions):\n",
+    "    player = QuantumRegister(1, name='player')\n",
+    "    n_people = n_interactions\n",
+    "\n",
+    "    network = QuantumRegister(n_people, name='network')\n",
+    "    bit = ClassicalRegister(1, name='bit')\n",
+    "    qc = QuantumCircuit(player, network, bit)  \n",
+    "\n",
+    "    # get the data\n",
+    "    data = pd.read_csv('records.csv').to_numpy()\n",
+    "    data = normalize_data(data)\n",
+    "\n",
+    "    # people considered in the network\n",
+    "    people_we_consider = []\n",
+    "\n",
+    "    # gives the people in the network the random starting positions\n",
+    "    q2 = initial_state(n_people)\n",
+    "    q2.draw()\n",
+    "    qc.compose(q2, np.arange(1, n_people+1), inplace=True)\n",
+    "\n",
+    "    for i in range(n_interactions):\n",
+    "        \n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "iqh",
+   "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": "3.11.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/data-gen/records_simulation.ipynb b/data-gen/records_simulation.ipynb
new file mode 100644
index 0000000..b6ed4fc
--- /dev/null
+++ b/data-gen/records_simulation.ipynb
@@ -0,0 +1,621 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "dHjlI6vkLgr3"
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "np.random.seed(42)"
+      ],
+      "metadata": {
+        "id": "nemzaRoRLjtf"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Role Data\n",
+        "# df = 3, mu = 0.7, sigma = 0.2\n",
+        "def genRole(mu, sigma, df):\n",
+        "\n",
+        "  # if np.random.rand() < 0.5:\n",
+        "  return np.random.chisquare(df)/ 10\n",
+        "  # else:\n",
+        "  #   return np.random.normal(mu, sigma) + 1\n"
+      ],
+      "metadata": {
+        "id": "U3LIwJIIMTsz"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "N_ENTRY = 20\n",
+        "entries = []\n",
+        "mu, sigma, deg_fr = 0.7, 0.2, 3"
+      ],
+      "metadata": {
+        "id": "MBsdgucRNgXa"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "\n",
+        "for _ in range(N_ENTRY):\n",
+        "\n",
+        "    stock_price = np.random.rand()\n",
+        "    role = max(0, min(1, genRole(mu, sigma, deg_fr)))\n",
+        "    mood = np.random.rand()\n",
+        "\n",
+        "    # Adjust stock price based on the given conditions\n",
+        "    if stock_price < 0.25:\n",
+        "        stock_price = np.random.uniform(0, 0.25)\n",
+        "\n",
+        "    entry = {\n",
+        "        'stock_price': stock_price,\n",
+        "        'role': role,\n",
+        "        'mood': mood\n",
+        "    }\n",
+        "    entries.append(entry)"
+      ],
+      "metadata": {
+        "id": "bFEl647-NPn3"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "entries"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "VBl_tYEpNsJg",
+        "outputId": "a9ade78b-5b12-4655-b6e5-e091f9577650"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "[{'stock_price': 0.3745401188473625,\n",
+              "  'role': 0.06613141580159124,\n",
+              "  'mood': 0.05808361216819946},\n",
+              " {'stock_price': 0.8661761457749352,\n",
+              "  'role': 0.30922640143603375,\n",
+              "  'mood': 0.7080725777960455},\n",
+              " {'stock_price': 0.13118910790805946,\n",
+              "  'role': 0.1289997910632404,\n",
+              "  'mood': 0.3042422429595377},\n",
+              " {'stock_price': 0.43194501864211576,\n",
+              "  'role': 0.13727725034527527,\n",
+              "  'mood': 0.6118528947223795},\n",
+              " {'stock_price': 0.049918445539589934,\n",
+              "  'role': 0.08701055182077448,\n",
+              "  'mood': 0.7851759613930136},\n",
+              " {'stock_price': 0.5142344384136116,\n",
+              "  'role': 0.041895648037166514,\n",
+              "  'mood': 0.046450412719997725},\n",
+              " {'stock_price': 0.6075448519014384,\n",
+              "  'role': 0.12620080066776382,\n",
+              "  'mood': 0.6842330265121569},\n",
+              " {'stock_price': 0.4401524937396013,\n",
+              "  'role': 0.5036789471024806,\n",
+              "  'mood': 0.4951769101112702},\n",
+              " {'stock_price': 0.1300170052944527,\n",
+              "  'role': 0.187193360515561,\n",
+              "  'mood': 0.31171107608941095},\n",
+              " {'stock_price': 0.5467102793432796,\n",
+              "  'role': 0.3279532546747701,\n",
+              "  'mood': 0.9695846277645586},\n",
+              " {'stock_price': 0.7751328233611146,\n",
+              "  'role': 0.43197762972720694,\n",
+              "  'mood': 0.1959828624191452},\n",
+              " {'stock_price': 0.06783725794347398,\n",
+              "  'role': 0.2723454321123816,\n",
+              "  'mood': 0.388677289689482},\n",
+              " {'stock_price': 0.8287375091519293,\n",
+              "  'role': 0.046571587637386884,\n",
+              "  'mood': 0.14092422497476265},\n",
+              " {'stock_price': 0.8021969807540397,\n",
+              "  'role': 0.09011703172483441,\n",
+              "  'mood': 0.9868869366005173},\n",
+              " {'stock_price': 0.7722447692966574,\n",
+              "  'role': 0.3830301689763141,\n",
+              "  'mood': 0.7712703466859457},\n",
+              " {'stock_price': 0.21577585646889838,\n",
+              "  'role': 0.48244676714311874,\n",
+              "  'mood': 0.11586905952512971},\n",
+              " {'stock_price': 0.6232981268275579,\n",
+              "  'role': 0.14437253957433874,\n",
+              "  'mood': 0.32518332202674705},\n",
+              " {'stock_price': 0.7296061783380641,\n",
+              "  'role': 0.1954804733530948,\n",
+              "  'mood': 0.8872127425763265},\n",
+              " {'stock_price': 0.4722149251619493,\n",
+              "  'role': 0.32049869667428815,\n",
+              "  'mood': 0.5612771975694962},\n",
+              " {'stock_price': 0.770967179954561,\n",
+              "  'role': 0.11987558355667216,\n",
+              "  'mood': 0.5227328293819941}]"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 6
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import pandas as pd"
+      ],
+      "metadata": {
+        "id": "2rhBFJ8rN1l5"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df = pd.DataFrame(entries)\n",
+        "df"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 676
+        },
+        "id": "4mcddNfqN4hs",
+        "outputId": "014f47f0-7c78-4c7d-c684-fe136f2605c4"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "    stock_price      role      mood\n",
+              "0      0.374540  0.066131  0.058084\n",
+              "1      0.866176  0.309226  0.708073\n",
+              "2      0.131189  0.129000  0.304242\n",
+              "3      0.431945  0.137277  0.611853\n",
+              "4      0.049918  0.087011  0.785176\n",
+              "5      0.514234  0.041896  0.046450\n",
+              "6      0.607545  0.126201  0.684233\n",
+              "7      0.440152  0.503679  0.495177\n",
+              "8      0.130017  0.187193  0.311711\n",
+              "9      0.546710  0.327953  0.969585\n",
+              "10     0.775133  0.431978  0.195983\n",
+              "11     0.067837  0.272345  0.388677\n",
+              "12     0.828738  0.046572  0.140924\n",
+              "13     0.802197  0.090117  0.986887\n",
+              "14     0.772245  0.383030  0.771270\n",
+              "15     0.215776  0.482447  0.115869\n",
+              "16     0.623298  0.144373  0.325183\n",
+              "17     0.729606  0.195480  0.887213\n",
+              "18     0.472215  0.320499  0.561277\n",
+              "19     0.770967  0.119876  0.522733"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-e2de8103-e5dc-427f-873d-98b8c90a6c85\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>stock_price</th>\n",
+              "      <th>role</th>\n",
+              "      <th>mood</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>0.374540</td>\n",
+              "      <td>0.066131</td>\n",
+              "      <td>0.058084</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>0.866176</td>\n",
+              "      <td>0.309226</td>\n",
+              "      <td>0.708073</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>0.131189</td>\n",
+              "      <td>0.129000</td>\n",
+              "      <td>0.304242</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>0.431945</td>\n",
+              "      <td>0.137277</td>\n",
+              "      <td>0.611853</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>0.049918</td>\n",
+              "      <td>0.087011</td>\n",
+              "      <td>0.785176</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>0.514234</td>\n",
+              "      <td>0.041896</td>\n",
+              "      <td>0.046450</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6</th>\n",
+              "      <td>0.607545</td>\n",
+              "      <td>0.126201</td>\n",
+              "      <td>0.684233</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7</th>\n",
+              "      <td>0.440152</td>\n",
+              "      <td>0.503679</td>\n",
+              "      <td>0.495177</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8</th>\n",
+              "      <td>0.130017</td>\n",
+              "      <td>0.187193</td>\n",
+              "      <td>0.311711</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>9</th>\n",
+              "      <td>0.546710</td>\n",
+              "      <td>0.327953</td>\n",
+              "      <td>0.969585</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>10</th>\n",
+              "      <td>0.775133</td>\n",
+              "      <td>0.431978</td>\n",
+              "      <td>0.195983</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>11</th>\n",
+              "      <td>0.067837</td>\n",
+              "      <td>0.272345</td>\n",
+              "      <td>0.388677</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>12</th>\n",
+              "      <td>0.828738</td>\n",
+              "      <td>0.046572</td>\n",
+              "      <td>0.140924</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>13</th>\n",
+              "      <td>0.802197</td>\n",
+              "      <td>0.090117</td>\n",
+              "      <td>0.986887</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>14</th>\n",
+              "      <td>0.772245</td>\n",
+              "      <td>0.383030</td>\n",
+              "      <td>0.771270</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>15</th>\n",
+              "      <td>0.215776</td>\n",
+              "      <td>0.482447</td>\n",
+              "      <td>0.115869</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>16</th>\n",
+              "      <td>0.623298</td>\n",
+              "      <td>0.144373</td>\n",
+              "      <td>0.325183</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>17</th>\n",
+              "      <td>0.729606</td>\n",
+              "      <td>0.195480</td>\n",
+              "      <td>0.887213</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>18</th>\n",
+              "      <td>0.472215</td>\n",
+              "      <td>0.320499</td>\n",
+              "      <td>0.561277</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>19</th>\n",
+              "      <td>0.770967</td>\n",
+              "      <td>0.119876</td>\n",
+              "      <td>0.522733</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e2de8103-e5dc-427f-873d-98b8c90a6c85')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-e2de8103-e5dc-427f-873d-98b8c90a6c85 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-e2de8103-e5dc-427f-873d-98b8c90a6c85');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-ef979bb5-11e0-4a02-8813-98d21ddc6426\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-ef979bb5-11e0-4a02-8813-98d21ddc6426')\"\n",
+              "            title=\"Suggest charts\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-ef979bb5-11e0-4a02-8813-98d21ddc6426 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "    </div>\n",
+              "  </div>\n"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 8
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df.to_csv('records.csv', index=False)"
+      ],
+      "metadata": {
+        "id": "nwvhHjnnOJDh"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "X9erCkP_OUov"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}
\ No newline at end of file
diff --git a/game-sim/ascii.py b/game-sim/ascii.py
new file mode 100644
index 0000000..4a9de0c
--- /dev/null
+++ b/game-sim/ascii.py
@@ -0,0 +1,65 @@
+import networkx as nx
+from asciicode import graph_to_ascii
+
+class Graph:
+
+    def __init__(self) -> None:
+    
+        '''
+        Creates a graph and returns it
+        '''
+        self.graph = nx.Graph()
+        self.potential_node_dic = {'q0' : ['q1', 'q2'], 'q1' : ['q3', 'q4'], 'q3' : ['q5'], 'q5' : ['q6']}
+        self.graph.add_node("q0")
+        self.graph.add_node("q1")
+        self.graph.add_node("q2")
+
+
+    def logging(self, log = False):
+        '''
+        Logs the graph in ascii art
+        '''
+
+        ascii_art = graph_to_ascii(self.graph)
+
+        if log:
+            print(ascii_art)
+
+
+    def connect(self, q0, qx):
+        '''
+        Connects q0 to qx and updates the potential_node_dic
+        '''
+        self.graph.add_edge(q0, qx)
+        
+        if qx in self.potential_node_dic.keys():   
+            for node in self.potential_node_dic[qx]:
+                self.potential_node_dic[q0].append(node)
+                self.graph.add_node(node)
+
+        self.potential_node_dic[q0].remove(qx)
+
+  
+graphObj = None 
+flag = False
+
+def getPotentialNodeDic():
+    '''
+    Returns the potential node dictionary
+    '''
+    global graphObj, flag
+    if not flag:
+        graphObj = Graph()
+        flag = True
+
+    return graphObj.potential_node_dic
+
+def setConnection(qx):
+    '''
+    Sets a connection from q0 to qx
+    '''
+    graphObj.connect('q0', qx)
+    graphObj.logging( True)
+
+
+
diff --git a/game-sim/asciicode.py b/game-sim/asciicode.py
new file mode 100644
index 0000000..71b3a32
--- /dev/null
+++ b/game-sim/asciicode.py
@@ -0,0 +1,108 @@
+#!/usr/bin/env python
+#
+# author: Cosmin Basca
+#
+# Copyright 2010 University of Zurich
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#        http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+from subprocess import Popen, PIPE, call
+import uuid
+from natsort import natsorted
+import networkx as nx
+import threading
+import atexit
+import os
+import requests
+from msgpack import dumps, loads
+from requests.exceptions import ConnectionError, Timeout
+from asciinet._libutil import latest_jar, check_java
+
+__author__ = 'basca'
+
+DEVNULL = open(os.devnull, 'w')
+
+__all__ = ['graph_to_ascii', 'JavaNotFoundException', 'GraphConversionError']
+
+
+class GraphConversionError(Exception):
+    pass
+
+
+class _AsciiGraphProxy(object):
+    @staticmethod
+    def instance():
+        if not hasattr(_AsciiGraphProxy, "_instance"):
+            _AsciiGraphProxy._instance = _AsciiGraphProxy()
+        return _AsciiGraphProxy._instance
+
+    def __init__(self, port=0):
+        check_java("Java is needed to run graph_to_ascii")
+        self._prefix = '{0}='.format(uuid.uuid1())
+        ascii_opts = ['--port', str(port), '--die_on_broken_pipe', '--port_notification_prefix', self._prefix]
+        latest_version, jar_path = latest_jar()
+        self._command = ["java", "-classpath", jar_path] + ['.'.join(['com', 'ascii', 'Server'])] + ascii_opts
+        self._proc = None
+        self._port = None
+        self._url = None
+        self._start()
+
+    def _start(self):
+        self._proc = Popen(self._command, stdout=PIPE, stdin=PIPE)
+        try:
+            line = ''
+            while not line.startswith(self._prefix):
+                line = self._proc.stdout.readline().decode(encoding='UTF-8')
+            self._port = int(line.replace(self._prefix, '').strip())
+        except Exception as e:
+            self._proc.kill()
+            raise e
+        self._url = 'http://127.0.0.1:{0}/asciiGraph'.format(self._port)
+
+    def _restart(self):
+        self._proc.kill()
+        self._start()
+
+    def graph_to_ascii(self, graph, timeout=10):
+        try:
+            graph_repr = dumps({
+                'vertices': [str(v) for v in graph.nodes()],
+                'edges': [[str(e[0]), str(e[1])] for e in graph.edges()],
+            })
+            response = requests.post(self._url, data=graph_repr, timeout=timeout)
+            if response.status_code == 200:
+                return loads(response.content)
+            else:
+                raise ValueError('internal error: \n{0}'.format(response.content))
+        except (ConnectionError, Timeout):
+            self._restart()
+            raise GraphConversionError('could not convert graph {0} to ascii'.format(graph))
+
+
+    def close(self):
+        self._proc.kill()
+
+
+_asciigraph = _AsciiGraphProxy.instance()
+
+
+@atexit.register
+def _cleanup():
+    _asciigraph.close()
+
+
+def graph_to_ascii(graph, timeout=10):
+    if not isinstance(graph, nx.Graph):
+        raise ValueError('graph must be a networkx.Graph')
+
+    return _asciigraph.graph_to_ascii(graph, timeout=timeout)
diff --git a/game-sim/backend_game.py b/game-sim/backend_game.py
new file mode 100644
index 0000000..8da2979
--- /dev/null
+++ b/game-sim/backend_game.py
@@ -0,0 +1,200 @@
+# import qiskit
+from qiskit import QuantumCircuit, QuantumRegister, ClassicalRegister
+from qiskit import BasicAer, execute
+from qiskit.circuit.library import QFT
+from qiskit.quantum_info import Statevector
+from qiskit.visualization import plot_bloch_multivector
+
+# from qiskit_ionq import IonQProvider 
+
+#Call provider and set token value
+# provider = IonQProvider(token='EDEq7Meo9Re0MIVV2loVBe2hZJCUG4VY')
+
+# numpy
+import numpy as np
+import pandas as pd
+
+# plotting
+import matplotlib.pyplot as plt
+from ascii import getPotentialNodeDic, setConnection
+from interactiveAscii import *
+
+### TO BE REMOVED
+from interactiveAscii import getStatusCode, checkResponse
+
+def normalize_data(data):
+    for d in data:
+        d = d / np.linalg.norm(d)
+    return data
+
+# creates random network starting states
+def initial_state(n, data):
+    temp = QuantumCircuit(n)
+    for i in range(n):
+        index = np.random.randint(0, n)
+        # people_we_consider.append(index)
+        # cartesian to spherical
+        theta = np.arctan(data[index][1] / data[index][0])
+        phi = np.arccos(data[index][2] / np.linalg.norm(data[index]))
+        temp.rx(theta, i)
+        temp.ry(phi, i)
+    return temp
+
+def core_story(n_interactions, mode='sim'):
+    player = QuantumRegister(1, name='player')
+    n_people = n_interactions+1
+
+    network = QuantumRegister(n_people, name='network')
+    bit = ClassicalRegister(1, name='bit')
+
+    qc = QuantumCircuit(player, network, bit)  
+
+    # get the data
+    data = pd.read_csv('./records.csv').to_numpy()
+    data = normalize_data(data)
+    all_people = {}
+    for i in range(0, len(data)):
+        all_people['q' + str(i+1)] = data[i]
+    # gives the people in the network the random starting positions
+    qc2 = initial_state(n_people, data)
+    qc.compose(qc2, np.arange(1, n_people+1), inplace=True)
+
+    for i in range(n_interactions):
+        if (mode == 'sim'):
+            connect_to = make_contact(all_people)
+            add_alice_interaction(qc, connect_to, data)
+
+    # print(qc)
+    qc.measure(player, bit)
+    return qc
+
+
+# negative theta is representative of sad story depending on magnitude
+# positive theta is good
+def life_event():
+    circ = QuantumCircuit(1)
+    event_types = ['rx', 'rz']
+    event = np.random.choice(event_types)
+    theta = (np.random.normal() % 1) * np.random.choice([-1, 1]) * np.pi/2
+    if event == 'rx':
+        circ.rx(theta, 0)
+    elif event == 'rz':
+        circ.ry(theta, 0)
+    return circ
+
+def add_alice_interaction(qc, i, data):
+    # fix gate as crx
+    if (i != 0):
+        theta = (np.sum(data[i]) % 1)  * np.pi
+        qc.crx(theta, i, 0)
+        le = life_event()
+        qc.compose(le, i, inplace=True)
+
+
+
+def make_contact(all_people):
+    if getStatusCode() == 200:
+
+        backend_stat = 200
+        checkResponse()
+        
+        if getStatusCode() == 404:
+            backend_stat = 404
+
+        graph_dic = getPotentialNodeDic()
+        # TODO: Add UI
+        alice_potential_contacts = None
+        if (graph_dic.get('q0') != None):
+            alice_potential_contacts = graph_dic.get('q0')
+        best_score = 0
+        best_contact = None
+
+        # add people with highest gain
+        for contact in alice_potential_contacts:
+            score = np.sum(all_people[contact])
+            if(score > best_score):
+                best_score = score
+                best_contact = contact
+
+        if(best_contact != None):
+            setConnection(best_contact)
+            best_contact_id = int(best_contact[1:]) - 1
+            # print(best_contact_id)
+            return best_contact_id
+        return 0
+
+def get_user_input(all_people):
+    action = str(input('input your choice <person_id  crx|crz>:'))
+    action = action.split()
+    person_id = int(action[0])
+    gate = str((action))
+    theta = (np.sum([all_people[i]]) % 1)  * np.pi
+    
+
+qc = core_story(14)
+backend = BasicAer.get_backend('qasm_simulator')
+job = execute(qc, backend)
+result = job.result()
+counts = result.get_counts()
+print(counts)
+qc.draw(output='mpl')
+
+    #     if gate == 'crx':
+    #         circ.crx(theta, network[i], player)
+    #     elif gate == 'crz':
+    #         circ.crz(theta, network[i], player)
+    #     else:
+    #         circ.crz(theta, network[i], player)
+    
+
+
+    # gate = [np.random.choice(gates_types) for _ in range(n_starting_people)] # list of gates as str
+
+    # circuits = [qc.copy() for _ in range(n_starting_people)]
+
+    # for i, circ in enumerate(circuits):
+    #     theta = (np.sum(data[people_we_consider[i]]) % 1)  * np.pi
+    #     if gate[i] == 'crx':
+    #         circ.crx(theta, network[i], player)
+    #     elif gate[i] == 'cry':
+    #         circ.cry(theta, network[i], player)
+    #     else:
+    #         circ.crz(theta, network[i], player)
+        
+    #     circ.measure(player, bit)
+    #     # plt.figure()
+    #     # circ.draw()
+    #     print(circ)
+        
+    # return circuits
+
+# def check_status():
+#     # run the circuit
+#     better_circuit = circ[0]
+#     max_ = -1
+
+#     index_of_person_who_in_circuit = 0
+
+#     for i, circ in enumerate(circuits):
+#         backend = BasicAer.get_backend('qasm_simulator')
+#         job = execute(circ, backend)
+#         result = job.result()
+#         counts = result.get_counts()
+
+#         try:
+#             if counts['1'] > max_:
+#                 max_ = counts['1']
+#                 better_circuit = circ
+#                 index_of_person_who_in_circuit = i
+#         except:
+#             pass
+        
+#         print(counts)
+
+#     print(better_circuit)
+
+#     # remove the person who is in the circuit from our data
+#     data = np.delete(data, people_we_consider[index_of_person_who_in_circuit], axis=0)
+#     people_we_consider = np.delete(people_we_consider, people_we_consider[index_of_person_who_in_circuit], axis=0)
+
+#     return better_circuit
\ No newline at end of file
diff --git a/game-sim/cli_req.txt b/game-sim/cli_req.txt
new file mode 100644
index 0000000..d6dde42
--- /dev/null
+++ b/game-sim/cli_req.txt
@@ -0,0 +1,36 @@
+# This file may be used to create an environment using:
+# $ conda create --name <env> --file <this file>
+# platform: osx-arm64
+asciinet=0.4.pre.cec4d1f=0
+brotli-python=1.0.9=py312h313beb8_7
+bzip2=1.0.8=h620ffc9_4
+ca-certificates=2023.12.12=hca03da5_0
+certifi=2023.11.17=py312hca03da5_0
+cffi=1.16.0=py312h80987f9_0
+charset-normalizer=2.0.4=pyhd3eb1b0_0
+cryptography=41.0.7=py312hd4332d6_0
+expat=2.5.0=h313beb8_0
+idna=3.4=py312hca03da5_0
+libcxx=14.0.6=h848a8c0_0
+libffi=3.4.4=hca03da5_0
+msgpack-python=1.0.3=py312h48ca7d4_0
+natsort=7.1.1=pyhd3eb1b0_0
+ncurses=6.4=h313beb8_0
+networkx=3.1=py312hca03da5_0
+openjdk=11.0.13=h98b2900_0
+openssl=3.0.13=h1a28f6b_0
+pip=23.3.1=py312hca03da5_0
+pycparser=2.21=pyhd3eb1b0_0
+pyopenssl=23.2.0=py312hca03da5_0
+pysocks=1.7.1=py312hca03da5_0
+python=3.12.1=h99e199e_0
+readline=8.2=h1a28f6b_0
+requests=2.31.0=py312hca03da5_0
+setuptools=68.2.2=py312hca03da5_0
+sqlite=3.41.2=h80987f9_0
+tk=8.6.12=hb8d0fd4_0
+tzdata=2023d=h04d1e81_0
+urllib3=1.26.18=py312hca03da5_0
+wheel=0.41.2=py312hca03da5_0
+xz=5.4.5=h80987f9_0
+zlib=1.2.13=h5a0b063_0
diff --git a/game-sim/constants.py b/game-sim/constants.py
new file mode 100644
index 0000000..fa1ee08
--- /dev/null
+++ b/game-sim/constants.py
@@ -0,0 +1,63 @@
+
+START = '''
+    ------------------- Welcome to the interactive ascii art graph creator! -------------------\n
+    ------------------------------- The Tale of Alice's Choices -------------------------------\n
+    -------------------------------------------------------------------------------------------\n
+    Alice, residing in a bustling city known for its vibrant community and endless opportunities, has established a solid network of potential connections. These connections span across various sectors including technology, arts, and social activism, providing her with a rich tapestry of relationships that could propel her career and personal growth. However, Alice finds herself at a crossroads, where each choice leads her down vastly different paths.
+    \n
+    REMEMBER! Once Alice makes a choice, she cannot go back or be able to select for another 9 months. Her decision will shape her future and the connections she will make. 
+'''
+
+
+SCENARIOS = [
+    
+    "Alice hears of a tech conference in Silicon Valley where Elon Musk is speaking. She can either stay and deepen ties with her local tech network or venture to the conference, hoping to gain insights and possibly meet Elon.",
+    
+    "A prestigious art residency in Paris offers Alice a chance to work alongside the renowned artist, Claude Monet. Staying would mean building her local art community, but Paris offers a once-in-a-lifetime creative mentorship.",
+
+    "An opportunity arises for Alice to attend a summit in Nairobi, with Malala Yousafzai as a keynote speaker. She can either continue her local social activism projects or seek global perspectives and personal advice from Malala.",
+
+    "Quentin Tarantino will be mentoring young filmmakers at a festival in Cannes. Alice could stay and work on her documentary with local filmmakers or try her luck in Cannes, potentially collaborating with Tarantino.",
+
+    "Alice has the chance to pitch her startup idea in Singapore, in front of tech mogul, Jack Ma. She could stay and refine her pitch with her current network or take a leap and present her idea on an international stage.",
+
+    "Attending Milan Fashion Week, Alice could potentially meet and learn from Giorgio Armani. She can either stay and cultivate her fashion startup at home or dive into the heart of global fashion trends and contacts.", 
+
+    "A conference in Iceland offers Alice the chance to meet Greta Thunberg and discuss climate initiatives. She can either further her local environmental efforts or seek inspiration and global partnerships in Iceland.",
+
+    "A music production workshop in Nashville promises a meeting with Dolly Parton. Alice could stay and strengthen her local music connections or pursue a dream mentorship in Nashville.",
+
+    "Renowned chef, Jiro Ono, is offering a selective sushi-making course in Tokyo. Alice could remain and grow her culinary startup locally or learn from a master and expand her culinary horizons.",
+
+    "J.K. Rowling is hosting a writers' retreat in Edinburgh. Alice can either continue her novel with the support of her local writers' group or seek Rowling's mentorship and inspiration in the historic city.",
+
+    "An opportunity to work with Kofi Annan’s foundation in Geneva presents itself. Alice can either stay and impact her community through local NGOs or gain international exposure and guidance in Geneva.",
+
+    "A symposium in Chile offers Alice the chance to meet Neil deGrasse Tyson at the world's largest observatory. She can either stay engaged with her local science community or embark on a journey to explore the cosmos with a leading astronomer.",
+
+    "Peter Jackson is offering a workshop for aspiring filmmakers in New Zealand. Alice could stay and produce her current project or take on the challenge of learning from a master in the breathtaking landscapes of New Zealand.",
+
+    "Bjarke Ingels is speaking at a summit on sustainable architecture. Alice can either continue her urban development projects at home or seek innovative insights and connections in Copenhagen.",
+
+    "Tony Robbins is hosting a retreat for entrepreneurs in Bali. Alice has the choice to stay and solidify her current business plans or attend the retreat, hoping to gain transformative strategies and personal advice from Robbins."
+
+]
+
+SCENARIOS_NAMES = [
+    "Elon Musk",
+    "Claude Monet",
+    "Malala Yousafzai",
+    "Quentin Tarantino",
+    "Jack Ma",
+    "Giorgio Armani",
+    "Greta Thunberg",
+    "Dolly Parton",
+    "Jiro Ono",
+    "J.K. Rowling",
+    "Kofi Annan",
+    "Neil deGrasse Tyson",
+    "Peter Jackson",
+    "Bjarke Ingels",
+    "Tony Robbins"
+]
+
diff --git a/game-sim/interactiveAscii.py b/game-sim/interactiveAscii.py
new file mode 100644
index 0000000..758fd00
--- /dev/null
+++ b/game-sim/interactiveAscii.py
@@ -0,0 +1,87 @@
+
+from constants import *
+from ascii import *
+import networkx as nx
+from asciicode import graph_to_ascii
+import random
+
+def startScreen():
+    '''
+    Prints the start screen of the game
+    '''
+
+    print(START)
+    print("\n\n------------------------------------------------------------------------------------ \n")
+    print("------------------------------------------------------------------------------------ \n")
+
+
+def printScenarios(list_of_scenarios, list_of_names):
+    '''
+    Prints the scenarios and removes the chosen scenario from the list
+    '''
+
+    rand = random.randint(0, len(list_of_scenarios)-1)
+    ret_scenario, ret_name = list_of_scenarios[rand], list_of_names[rand]
+    list_of_scenarios.pop(rand)
+    list_of_names.pop(rand)
+    return ret_scenario, ret_name
+
+statusCode = 404
+
+def askUser(list_of_scenarios, list_of_names, n_times = 1, cooldown = 0):
+
+    global statusCode, graphObj
+    '''
+    Asks the user for input
+    '''
+
+    userInput = ""
+
+    for i in range(n_times):
+
+        statusCode = 404
+
+        if cooldown == 0:
+            scenario, name = printScenarios(list_of_scenarios, list_of_names)
+            potential_node_dic = getPotentialNodeDic()
+            print("Current potential connections: ", potential_node_dic["q0"])
+            print("\n\n")
+            print(scenario)
+            print("\n\n")
+                
+            userInput = input("Enter your choice (y/n): ")
+
+        if (userInput == "y" and cooldown == 0):
+            potential_node_dic = getPotentialNodeDic()
+            potential_node_dic["q0"].append(name)
+            # graphObj.logging(True)
+            cooldown = 3
+            statusCode = 200
+            
+        elif (userInput == "n"):
+            print("You chose to stay")
+            # graphObj.logging(True)
+            cooldown = cooldown - 1 if cooldown > 0 else 0
+            statusCode = 200
+
+        else:
+            print("You cannot make a choice yet")
+            # graphObj.logging(True)
+            cooldown = cooldown - 1 if cooldown > 0 else 0
+            statusCode = 200
+
+def getStatusCode():
+    '''
+    Returns the status code
+    '''
+    return statusCode
+    
+
+
+list_of_scenarios = SCENARIOS[:]
+list_of_names = SCENARIOS_NAMES[:]
+cooldown = 0
+
+getPotentialNodeDic()
+startScreen()
+askUser(list_of_scenarios, list_of_names, 13)
diff --git a/game-sim/records.csv b/game-sim/records.csv
new file mode 100644
index 0000000..a03cd5a
--- /dev/null
+++ b/game-sim/records.csv
@@ -0,0 +1,21 @@
+stock_price,role,mood
+0.3745401188473625,0.06613141580159124,0.05808361216819946
+0.8661761457749352,0.30922640143603375,0.7080725777960455
+0.13118910790805946,0.1289997910632404,0.3042422429595377
+0.43194501864211576,0.13727725034527527,0.6118528947223795
+0.049918445539589934,0.08701055182077448,0.7851759613930136
+0.5142344384136116,0.041895648037166514,0.046450412719997725
+0.6075448519014384,0.12620080066776382,0.6842330265121569
+0.4401524937396013,0.5036789471024806,0.4951769101112702
+0.1300170052944527,0.187193360515561,0.31171107608941095
+0.5467102793432796,0.3279532546747701,0.9695846277645586
+0.7751328233611146,0.43197762972720694,0.1959828624191452
+0.06783725794347398,0.2723454321123816,0.388677289689482
+0.8287375091519293,0.046571587637386884,0.14092422497476265
+0.8021969807540397,0.09011703172483441,0.9868869366005173
+0.7722447692966574,0.3830301689763141,0.7712703466859457
+0.21577585646889838,0.48244676714311874,0.11586905952512971
+0.6232981268275579,0.14437253957433874,0.32518332202674705
+0.7296061783380641,0.1954804733530948,0.8872127425763265
+0.4722149251619493,0.32049869667428815,0.5612771975694962
+0.770967179954561,0.11987558355667216,0.5227328293819941
diff --git a/graph.ipynb b/graph.ipynb
new file mode 100644
index 0000000..7c34458
--- /dev/null
+++ b/graph.ipynb
@@ -0,0 +1,170 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import rustworkx \n",
+    "import matplotlib.pyplot as plt\n",
+    "from rustworkx.visualization import mpl_draw"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "graph = rustworkx.PyGraph()\n",
+    "\n",
+    "graph.add_node(\"q0\")\n",
+    "graph.add_node(\"q1\")\n",
+    "graph.add_node(\"q2\")\n",
+    "\n",
+    "potential_node_dic = {0 : [1,2], 1 : [3,4], 2: [5,6]}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def visualize_graph(graph):\n",
+    "    # Create a larger figure with a specified size (adjust the values as needed)\n",
+    "    # fig = plt.figure(figsize=(40, 40))\n",
+    "\n",
+    "    # Create a subplot within the larger figure\n",
+    "    subax1 = plt.subplot(121)\n",
+    "\n",
+    "    # Now, you can draw your graph with_labels=True on the larger subplot\n",
+    "    mpl_draw(graph, with_labels=True, ax=subax1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def connect(q0, qx, graph, potential_node_dic):\n",
+    "    graph.add_edge(q0, qx, 1)\n",
+    "    \n",
+    "    if qx in potential_node_dic.keys():   \n",
+    "        for node in potential_node_dic[qx]:\n",
+    "            potential_node_dic[q0].append(node)\n",
+    "            graph.add_node(node)\n",
+    "\n",
+    "    potential_node_dic[q0].remove(qx)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "visualize_graph(graph)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "connect(0, 1, graph ,potential_node_dic)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "visualize_graph(graph)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "connect(0, 2, graph ,potential_node_dic)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "visualize_graph(graph)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.10.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/images/circuit.png b/images/circuit.png
new file mode 100644
index 0000000..14630c8
Binary files /dev/null and b/images/circuit.png differ
diff --git a/images/output.png b/images/output.png
new file mode 100644
index 0000000..745dc5f
Binary files /dev/null and b/images/output.png differ
diff --git a/images/small_circuit.png b/images/small_circuit.png
new file mode 100644
index 0000000..1cd5d0c
Binary files /dev/null and b/images/small_circuit.png differ
diff --git a/images/trend.png b/images/trend.png
new file mode 100644
index 0000000..0e0b009
Binary files /dev/null and b/images/trend.png differ
diff --git a/images/trend2.png b/images/trend2.png
new file mode 100644
index 0000000..c49f924
Binary files /dev/null and b/images/trend2.png differ
diff --git a/presentation/FalQon_Team24.pdf b/presentation/FalQon_Team24.pdf
new file mode 100644
index 0000000..a6a378b
Binary files /dev/null and b/presentation/FalQon_Team24.pdf differ
diff --git a/presentation/iQuHACK_word.pdf b/presentation/iQuHACK_word.pdf
new file mode 100644
index 0000000..065623f
Binary files /dev/null and b/presentation/iQuHACK_word.pdf differ
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000..a1f2227
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,175 @@
+# This file may be used to create an environment using:
+# $ conda create --name <env> --file <this file>
+# platform: osx-64
+amazon-braket-default-simulator=1.20.1=pypi_0
+amazon-braket-schemas=1.19.1.post0=pypi_0
+amazon-braket-sdk=1.68.1=pypi_0
+antlr4-python3-runtime=4.9.2=pypi_0
+appnope=0.1.3=pyhd8ed1ab_0
+asciinet=0.4.pre.cec4d1f=0
+asttokens=2.4.1=pyhd8ed1ab_0
+backoff=2.2.1=pypi_0
+beartype=0.17.0=pypi_0
+bloqade=0.15.5=pypi_0
+bokeh=3.3.4=pypi_0
+boltons=23.1.1=pypi_0
+boto3=1.34.31=pypi_0
+botocore=1.34.31=pypi_0
+brotli=1.1.0=h0dc2134_1
+brotli-bin=1.1.0=h0dc2134_1
+brotli-python=1.1.0=py311hdf8f085_1
+bzip2=1.0.8=h10d778d_5
+ca-certificates=2024.2.2=h8857fd0_0
+certifi=2024.2.2=pyhd8ed1ab_0
+charset-normalizer=3.3.2=pyhd8ed1ab_0
+cloudpickle=2.2.1=pypi_0
+comm=0.2.1=pyhd8ed1ab_0
+contourpy=1.2.0=py311h7bea37d_0
+cycler=0.12.1=pyhd8ed1ab_0
+debugpy=1.8.0=py311hdf8f085_1
+decorator=5.1.1=pyhd8ed1ab_0
+dill=0.3.8=pyhd8ed1ab_0
+exceptiongroup=1.2.0=pyhd8ed1ab_2
+executing=2.0.1=pyhd8ed1ab_0
+fonttools=4.47.2=py311he705e18_0
+freetype=2.12.1=h60636b9_2
+gf2x=1.3.0=hb2a7efb_2
+gmp=6.3.0=h93d8f39_0
+gmpy2=2.1.2=py311hc5b4402_1
+idna=3.6=pyhd8ed1ab_0
+importlib-metadata=7.0.1=pyha770c72_0
+importlib_metadata=7.0.1=hd8ed1ab_0
+ipykernel=6.29.0=pyh3cd1d5f_0
+ipython=8.21.0=pyh707e725_0
+jedi=0.19.1=pyhd8ed1ab_0
+jinja2=3.1.3=pypi_0
+jmespath=1.0.1=pypi_0
+juliacall=0.9.15=pypi_0
+juliapkg=0.1.10=pypi_0
+jupyter_client=8.6.0=pyhd8ed1ab_0
+jupyter_core=5.7.1=py311h6eed73b_0
+kiwisolver=1.4.5=py311h5fe6e05_1
+lark-parser=0.12.0=pypi_0
+lcms2=2.16=ha2f27b4_0
+lerc=4.0.0=hb486fe8_0
+libblas=3.9.0=21_osx64_openblas
+libbrotlicommon=1.1.0=h0dc2134_1
+libbrotlidec=1.1.0=h0dc2134_1
+libbrotlienc=1.1.0=h0dc2134_1
+libcblas=3.9.0=21_osx64_openblas
+libcxx=16.0.6=hd57cbcb_0
+libdeflate=1.19=ha4e1b8e_0
+libexpat=2.5.0=hf0c8a7f_1
+libffi=3.4.2=h0d85af4_5
+libflint=2.9.0=hfd2f71f_ntl_100
+libgfortran=5.0.0=13_2_0_h97931a8_2
+libgfortran5=13.2.0=h2873a65_2
+libjpeg-turbo=3.0.0=h0dc2134_1
+liblapack=3.9.0=21_osx64_openblas
+libopenblas=0.3.26=openmp_hfef2a42_0
+libpng=1.6.42=h92b6c6a_0
+libsodium=1.0.18=hbcb3906_1
+libsqlite=3.44.2=h92b6c6a_0
+libtiff=4.6.0=h684deea_2
+libwebp-base=1.3.2=h0dc2134_0
+libxcb=1.15=hb7f2c08_0
+libzlib=1.2.13=h8a1eda9_5
+llvm-openmp=17.0.6=hb6ac08f_0
+llvmlite=0.41.1=pypi_0
+markdown-it-py=3.0.0=pypi_0
+markupsafe=2.1.4=pypi_0
+matplotlib=3.8.2=py311h6eed73b_0
+matplotlib-base=3.8.2=py311hd316c10_0
+matplotlib-inline=0.1.6=pyhd8ed1ab_0
+mdurl=0.1.2=pypi_0
+mpc=1.3.1=h81bd1dd_0
+mpfr=4.2.1=h0c69b56_0
+mpmath=1.3.0=pyhd8ed1ab_0
+msgpack-python=1.0.7=py311h7bea37d_0
+munkres=1.1.4=pyh9f0ad1d_0
+mypy-extensions=1.0.0=pypi_0
+natsort=8.4.0=pyhd8ed1ab_0
+ncurses=6.4=h93d8f39_2
+nest-asyncio=1.6.0=pyhd8ed1ab_0
+networkx=3.2.1=pyhd8ed1ab_0
+ntl=11.4.3=h0ab3c2f_1
+numba=0.58.1=pypi_0
+numpy=1.26.3=py311hc43a94b_0
+openjdk=21.0.2=h2d185b6_0
+openjpeg=2.5.0=ha4da562_3
+openpulse=0.4.2=pypi_0
+openqasm3=0.4.0=pypi_0
+openssl=3.2.1=hd75f5a5_0
+opt-einsum=3.3.0=pypi_0
+oqpy=0.2.1=pypi_0
+packaging=23.2=pyhd8ed1ab_0
+pandas=2.2.0=py311h8f6166a_0
+parso=0.8.3=pyhd8ed1ab_0
+pbr=6.0.0=pyhd8ed1ab_0
+pexpect=4.9.0=pyhd8ed1ab_0
+pickleshare=0.7.5=py_1003
+pillow=10.2.0=py311hea5c87a_0
+pip=23.3.2=pyhd8ed1ab_0
+platformdirs=4.2.0=pyhd8ed1ab_0
+plotext=5.2.8=pypi_0
+plum-dispatch=2.3.2=pypi_0
+ply=3.11=py_1
+prompt-toolkit=3.0.42=pyha770c72_0
+psutil=5.9.8=py311he705e18_0
+pthread-stubs=0.4=hc929b4f_1001
+ptyprocess=0.7.0=pyhd3deb0d_0
+pure_eval=0.2.2=pyhd8ed1ab_0
+py=1.11.0=pypi_0
+pydantic=1.10.14=pypi_0
+pygments=2.17.2=pyhd8ed1ab_0
+pylatexenc=2.10=pyhd8ed1ab_0
+pyparsing=3.1.1=pyhd8ed1ab_0
+pysocks=1.7.1=pyha2e5f31_6
+python=3.11.7=h9f0c242_1_cpython
+python-dateutil=2.8.2=pyhd8ed1ab_0
+python-graphviz=0.20.1=pypi_0
+python-symengine=0.11.0=py311h0d02041_1
+python-tzdata=2023.4=pyhd8ed1ab_0
+python_abi=3.11=4_cp311
+pytket=1.24.0=pypi_0
+pytz=2023.4=pypi_0
+pyyaml=6.0.1=pypi_0
+pyzmq=25.1.2=py311h889d6d6_0
+qiskit=0.45.2=pyhd8ed1ab_0
+qiskit-ionq=0.4.7=pypi_0
+qiskit-terra=0.45.2=py311h1976286_0
+qwasm=1.0.1=pypi_0
+readline=8.2=h9e318b2_1
+requests=2.31.0=pyhd8ed1ab_0
+requests-aws-sign=0.1.6=pypi_0
+requests-sigv4=0.1.6=pypi_0
+retry=0.9.2=pypi_0
+rich=13.7.0=pypi_0
+rustworkx=0.13.2=py311he599670_0
+s3transfer=0.10.0=pypi_0
+scipy=1.12.0=py311h86d0cd9_2
+semantic-version=2.10.0=pypi_0
+setuptools=69.0.3=pyhd8ed1ab_0
+simplejson=3.19.2=pypi_0
+six=1.16.0=pyh6c4a22f_0
+stack_data=0.6.2=pyhd8ed1ab_0
+stevedore=5.1.0=pyhd8ed1ab_0
+symengine=0.11.2=hb6b57cf_0
+sympy=1.12=pypyh9d50eac_103
+tabulate=0.9.0=pypi_0
+tk=8.6.13=h1abcd95_1
+tornado=6.4=pypi_0
+traitlets=5.14.1=pyhd8ed1ab_0
+types-pkg-resources=0.1.3=pypi_0
+typing_extensions=4.9.0=pyha770c72_0
+tzdata=2023d=h0c530f3_0
+urllib3=2.0.7=pypi_0
+wcwidth=0.2.13=pyhd8ed1ab_0
+wheel=0.42.0=pyhd8ed1ab_0
+xorg-libxau=1.0.11=h0dc2134_0
+xorg-libxdmcp=1.1.3=h35c211d_0
+xyzservices=2023.10.1=pypi_0
+xz=5.2.6=h775f41a_0
+zeromq=4.3.5=h93d8f39_0
+zipp=3.17.0=pyhd8ed1ab_0
+zstd=1.5.5=h829000d_0
diff --git a/simulation/ascii.py b/simulation/ascii.py
new file mode 100644
index 0000000..5744524
--- /dev/null
+++ b/simulation/ascii.py
@@ -0,0 +1,63 @@
+import networkx as nx
+from asciicode import graph_to_ascii
+
+class Graph:
+
+    def __init__(self) -> None:
+    
+        '''
+        Creates a graph and returns it
+        '''
+        self.graph = nx.Graph()
+        self.potential_node_dic = {'q0' : ['q1', 'q2'], 'q1' : ['q3', 'q4'], 'q3' : ['q5'], 'q5' : ['q6']}
+        self.graph.add_node("q0")
+        self.graph.add_node("q1")
+        self.graph.add_node("q2")
+
+
+    def logging(self, log = False):
+        '''
+        Logs the graph in ascii art
+        '''
+
+        ascii_art = graph_to_ascii(self.graph)
+
+        if log:
+            print(ascii_art)
+
+
+    def connect(self, q0, qx):
+        '''
+        Connects q0 to qx and updates the potential_node_dic
+        '''
+        self.graph.add_edge(q0, qx)
+        
+        if qx in self.potential_node_dic.keys():   
+            for node in self.potential_node_dic[qx]:
+                self.potential_node_dic[q0].append(node)
+                self.graph.add_node(node)
+
+        self.potential_node_dic[q0].remove(qx)
+
+  
+graphObj = None 
+flag = False
+
+def getPotentialNodeDic():
+    '''
+    Returns the potential node dictionary
+    '''
+    global graphObj, flag
+    if not flag:
+        graphObj = Graph()
+        flag = True
+
+    return graphObj.potential_node_dic
+
+def setConnection(qx):
+    '''
+    Sets a connection from q0 to qx
+    '''
+    graphObj.connect('q0', qx)
+    graphObj.logging( True)
+
diff --git a/simulation/asciicode.py b/simulation/asciicode.py
new file mode 100644
index 0000000..71b3a32
--- /dev/null
+++ b/simulation/asciicode.py
@@ -0,0 +1,108 @@
+#!/usr/bin/env python
+#
+# author: Cosmin Basca
+#
+# Copyright 2010 University of Zurich
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#        http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+from subprocess import Popen, PIPE, call
+import uuid
+from natsort import natsorted
+import networkx as nx
+import threading
+import atexit
+import os
+import requests
+from msgpack import dumps, loads
+from requests.exceptions import ConnectionError, Timeout
+from asciinet._libutil import latest_jar, check_java
+
+__author__ = 'basca'
+
+DEVNULL = open(os.devnull, 'w')
+
+__all__ = ['graph_to_ascii', 'JavaNotFoundException', 'GraphConversionError']
+
+
+class GraphConversionError(Exception):
+    pass
+
+
+class _AsciiGraphProxy(object):
+    @staticmethod
+    def instance():
+        if not hasattr(_AsciiGraphProxy, "_instance"):
+            _AsciiGraphProxy._instance = _AsciiGraphProxy()
+        return _AsciiGraphProxy._instance
+
+    def __init__(self, port=0):
+        check_java("Java is needed to run graph_to_ascii")
+        self._prefix = '{0}='.format(uuid.uuid1())
+        ascii_opts = ['--port', str(port), '--die_on_broken_pipe', '--port_notification_prefix', self._prefix]
+        latest_version, jar_path = latest_jar()
+        self._command = ["java", "-classpath", jar_path] + ['.'.join(['com', 'ascii', 'Server'])] + ascii_opts
+        self._proc = None
+        self._port = None
+        self._url = None
+        self._start()
+
+    def _start(self):
+        self._proc = Popen(self._command, stdout=PIPE, stdin=PIPE)
+        try:
+            line = ''
+            while not line.startswith(self._prefix):
+                line = self._proc.stdout.readline().decode(encoding='UTF-8')
+            self._port = int(line.replace(self._prefix, '').strip())
+        except Exception as e:
+            self._proc.kill()
+            raise e
+        self._url = 'http://127.0.0.1:{0}/asciiGraph'.format(self._port)
+
+    def _restart(self):
+        self._proc.kill()
+        self._start()
+
+    def graph_to_ascii(self, graph, timeout=10):
+        try:
+            graph_repr = dumps({
+                'vertices': [str(v) for v in graph.nodes()],
+                'edges': [[str(e[0]), str(e[1])] for e in graph.edges()],
+            })
+            response = requests.post(self._url, data=graph_repr, timeout=timeout)
+            if response.status_code == 200:
+                return loads(response.content)
+            else:
+                raise ValueError('internal error: \n{0}'.format(response.content))
+        except (ConnectionError, Timeout):
+            self._restart()
+            raise GraphConversionError('could not convert graph {0} to ascii'.format(graph))
+
+
+    def close(self):
+        self._proc.kill()
+
+
+_asciigraph = _AsciiGraphProxy.instance()
+
+
+@atexit.register
+def _cleanup():
+    _asciigraph.close()
+
+
+def graph_to_ascii(graph, timeout=10):
+    if not isinstance(graph, nx.Graph):
+        raise ValueError('graph must be a networkx.Graph')
+
+    return _asciigraph.graph_to_ascii(graph, timeout=timeout)
diff --git a/simulation/backend_sim.ipynb b/simulation/backend_sim.ipynb
new file mode 100644
index 0000000..9ebde15
--- /dev/null
+++ b/simulation/backend_sim.ipynb
@@ -0,0 +1,416 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/06/7pjfbw510cz2ch1m7lx8vvl80000gn/T/ipykernel_3610/3137930547.py:15: DeprecationWarning: \n",
+      "Pyarrow will become a required dependency of pandas in the next major release of pandas (pandas 3.0),\n",
+      "(to allow more performant data types, such as the Arrow string type, and better interoperability with other libraries)\n",
+      "but was not found to be installed on your system.\n",
+      "If this would cause problems for you,\n",
+      "please provide us feedback at https://github.com/pandas-dev/pandas/issues/54466\n",
+      "        \n",
+      "  import pandas as pd\n"
+     ]
+    }
+   ],
+   "source": [
+    "# import qiskit\n",
+    "from qiskit import QuantumCircuit, QuantumRegister, ClassicalRegister\n",
+    "from qiskit import BasicAer, execute\n",
+    "from qiskit.circuit.library import QFT\n",
+    "from qiskit.quantum_info import Statevector\n",
+    "from qiskit.visualization import plot_bloch_multivector\n",
+    "\n",
+    "from qiskit_ionq import IonQProvider \n",
+    "\n",
+    "#Call provider and set token value\n",
+    "# provider = IonQProvider(token='EDEq7Meo9Re0MIVV2loVBe2hZJCUG4VY')\n",
+    "\n",
+    "# numpy\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "\n",
+    "# plotting\n",
+    "import matplotlib.pyplot as plt\n",
+    "from ascii import getPotentialNodeDic, setConnection\n",
+    "import time;"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def normalize_data(data):\n",
+    "    for d in data:\n",
+    "        d = d / np.linalg.norm(d)\n",
+    "    return data\n",
+    "\n",
+    "# creates random network starting states\n",
+    "def initial_state(n, data):\n",
+    "    temp = QuantumCircuit(n)\n",
+    "    for i in range(n):\n",
+    "        index = np.random.randint(0, n)\n",
+    "        # people_we_consider.append(index)\n",
+    "        # cartesian to spherical\n",
+    "        theta = np.arctan(data[index][1] / data[index][0])\n",
+    "        phi = np.arccos(data[index][2] / np.linalg.norm(data[index]))\n",
+    "        temp.rx(theta, i)\n",
+    "        temp.ry(phi, i)\n",
+    "    return temp\n",
+    "\n",
+    "def core_story(n_interactions, mode='sim'):\n",
+    "    player = QuantumRegister(1, name='player')\n",
+    "    n_people = n_interactions+1\n",
+    "\n",
+    "    network = QuantumRegister(n_people, name='network')\n",
+    "    bit = ClassicalRegister(1, name='bit')\n",
+    "\n",
+    "    qc = QuantumCircuit(player, network, bit)  \n",
+    "\n",
+    "    # get the data\n",
+    "    data = pd.read_csv('records.csv').to_numpy()\n",
+    "    data = normalize_data(data)\n",
+    "    all_people = {}\n",
+    "    for i in range(0, len(data)):\n",
+    "        all_people['q' + str(i+1)] = data[i]\n",
+    "    # gives the people in the network the random starting positions\n",
+    "    qc2 = initial_state(n_people, data)\n",
+    "    qc.compose(qc2, np.arange(1, n_people+1), inplace=True)\n",
+    "\n",
+    "    for i in range(n_interactions):\n",
+    "        if (mode == 'sim'):\n",
+    "            connect_to = make_contact(all_people)\n",
+    "            time.sleep(2)\n",
+    "            add_alice_interaction(qc, connect_to, data)\n",
+    "\n",
+    "    # print(qc)\n",
+    "    qc.measure(player, bit)\n",
+    "    return qc\n",
+    "\n",
+    "\n",
+    "# negative theta is representative of sad story depending on magnitude\n",
+    "# positive theta is good\n",
+    "def life_event():\n",
+    "    circ = QuantumCircuit(1)\n",
+    "    event_types = ['rx', 'rz']\n",
+    "    event = np.random.choice(event_types)\n",
+    "    sign = np.random.choice([-1, 1])\n",
+    "    theta = (np.random.normal() % 1) * sign * np.pi/2\n",
+    "    if event == 'rx':\n",
+    "        circ.rx(theta, 0)\n",
+    "    elif event == 'rz':\n",
+    "        circ.ry(theta, 0)\n",
+    "    return circ, sign\n",
+    "\n",
+    "def add_alice_interaction(qc, i, data):\n",
+    "    # fix gate as crx\n",
+    "    if (i != 0):\n",
+    "        theta = (np.sum(data[i]) % 1)  * np.pi\n",
+    "        qc.crx(theta, i, 0)\n",
+    "        print(f'Alice decided to *entangle* with agent {i+1}, as their score was {np.sum(data[i])}')\n",
+    "        le, sign = life_event()\n",
+    "        if (sign < 0):\n",
+    "            print(f'Person {i+1} experienced a sad event.')\n",
+    "        if (sign > 0):\n",
+    "            print(f'Person {i+1} experienced a happy event.')\n",
+    "        qc.compose(le, i, inplace=True)\n",
+    "    time.sleep(2)\n",
+    "\n",
+    "def make_contact(all_people):\n",
+    "    graph_dic = getPotentialNodeDic()\n",
+    "    # print(graph_dic)\n",
+    "    alice_potential_contacts = None\n",
+    "    if (graph_dic.get('q0') != None):\n",
+    "        alice_potential_contacts = graph_dic.get('q0')\n",
+    "    best_score = 0\n",
+    "    best_contact = None\n",
+    "\n",
+    "    # print(alice_potential_contacts)\n",
+    "    # add people with highest gain\n",
+    "    for contact in alice_potential_contacts:\n",
+    "        score = np.sum(all_people[contact])\n",
+    "        if(score > best_score):\n",
+    "            best_score = score\n",
+    "            best_contact = contact\n",
+    "\n",
+    "    if(best_contact != None):\n",
+    "        setConnection(best_contact)\n",
+    "        best_contact_id = int(best_contact[1:]) - 1\n",
+    "        # print(best_contact_id)\n",
+    "        return best_contact_id\n",
+    "    return 0\n",
+    "\n",
+    "def get_user_input(all_people):\n",
+    "    action = str(input('input your choice <person_id  crx|crz>:'))\n",
+    "    action = action.split()\n",
+    "    person_id = int(action[0])\n",
+    "    gate = str((action))\n",
+    "    theta = (np.sum([all_people[i]]) % 1)  * np.pi\n",
+    "    \n",
+    "    #     if gate == 'crx':\n",
+    "    #         circ.crx(theta, network[i], player)\n",
+    "    #     elif gate == 'crz':\n",
+    "    #         circ.crz(theta, network[i], player)\n",
+    "    #     else:\n",
+    "    #         circ.crz(theta, network[i], player)\n",
+    "    \n",
+    "\n",
+    "\n",
+    "    # gate = [np.random.choice(gates_types) for _ in range(n_starting_people)] # list of gates as str\n",
+    "\n",
+    "    # circuits = [qc.copy() for _ in range(n_starting_people)]\n",
+    "\n",
+    "    # for i, circ in enumerate(circuits):\n",
+    "    #     theta = (np.sum(data[people_we_consider[i]]) % 1)  * np.pi\n",
+    "    #     if gate[i] == 'crx':\n",
+    "    #         circ.crx(theta, network[i], player)\n",
+    "    #     elif gate[i] == 'cry':\n",
+    "    #         circ.cry(theta, network[i], player)\n",
+    "    #     else:\n",
+    "    #         circ.crz(theta, network[i], player)\n",
+    "        \n",
+    "    #     circ.measure(player, bit)\n",
+    "    #     # plt.figure()\n",
+    "    #     # circ.draw()\n",
+    "    #     print(circ)\n",
+    "        \n",
+    "    # return circuits\n",
+    "\n",
+    "# def check_status():\n",
+    "#     # run the circuit\n",
+    "#     better_circuit = circ[0]\n",
+    "#     max_ = -1\n",
+    "\n",
+    "#     index_of_person_who_in_circuit = 0\n",
+    "\n",
+    "#     for i, circ in enumerate(circuits):\n",
+    "#         backend = BasicAer.get_backend('qasm_simulator')\n",
+    "#         job = execute(circ, backend)\n",
+    "#         result = job.result()\n",
+    "#         counts = result.get_counts()\n",
+    "\n",
+    "#         try:\n",
+    "#             if counts['1'] > max_:\n",
+    "#                 max_ = counts['1']\n",
+    "#                 better_circuit = circ\n",
+    "#                 index_of_person_who_in_circuit = i\n",
+    "#         except:\n",
+    "#             pass\n",
+    "        \n",
+    "#         print(counts)\n",
+    "\n",
+    "#     print(better_circuit)\n",
+    "\n",
+    "#     # remove the person who is in the circuit from our data\n",
+    "#     data = np.delete(data, people_we_consider[index_of_person_who_in_circuit], axis=0)\n",
+    "#     people_we_consider = np.delete(people_we_consider, people_we_consider[index_of_person_who_in_circuit], axis=0)\n",
+    "\n",
+    "#     return better_circuit"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "   ┌───┐   \n",
+      "   │q0 │   \n",
+      "   └─┬─┘   \n",
+      "     │     \n",
+      "   ┌─┘     \n",
+      "   │       \n",
+      "   v       \n",
+      " ┌───┐ ┌──┐\n",
+      " │q2 │ │q1│\n",
+      " └───┘ └──┘\n",
+      "Alice decided to *entangle* with agent 2, as their score was 1.8834751250070143\n",
+      "Person 2 experienced a happy event.\n",
+      "       ┌─────┐        \n",
+      "       │ q0  │        \n",
+      "       └─┬┬──┘        \n",
+      "         ││           \n",
+      "   ┌─────┘│           \n",
+      "   │      │           \n",
+      "   v      v           \n",
+      " ┌───┐ ┌───┐ ┌──┐ ┌──┐\n",
+      " │q1 │ │q2 │ │q4│ │q3│\n",
+      " └───┘ └───┘ └──┘ └──┘\n",
+      "       ┌───────┐       \n",
+      "       │  q0   │       \n",
+      "       └─┬┬──┬─┘       \n",
+      "         ││  │         \n",
+      "   ┌─────┘│  └─┐       \n",
+      "   │      │    │       \n",
+      "   v      v    v       \n",
+      " ┌───┐ ┌───┐ ┌───┐ ┌──┐\n",
+      " │q4 │ │q1 │ │q2 │ │q3│\n",
+      " └───┘ └───┘ └───┘ └──┘\n",
+      "Alice decided to *entangle* with agent 4, as their score was 1.1810751637097705\n",
+      "Person 4 experienced a sad event.\n",
+      "         ┌─────────┐         \n",
+      "         │   q0    │         \n",
+      "         └─┬─┬─┬─┬─┘         \n",
+      "           │ │ │ │           \n",
+      "   ┌───────┘ │ │ └───┐       \n",
+      "   │     ┌───┘ │     │       \n",
+      "   │     │     │     │       \n",
+      "   v     v     v     v       \n",
+      " ┌───┐ ┌───┐ ┌───┐ ┌───┐ ┌──┐\n",
+      " │q4 │ │q3 │ │q1 │ │q2 │ │q5│\n",
+      " └───┘ └───┘ └───┘ └───┘ └──┘\n",
+      "Alice decided to *entangle* with agent 3, as their score was 0.5644311419308375\n",
+      "Person 3 experienced a sad event.\n",
+      "           ┌───────────┐           \n",
+      "           │    q0     │           \n",
+      "           └─┬─┬┬────┬┬┘           \n",
+      "             │ ││    ││            \n",
+      "   ┌─────────┘ ││    └┼────┐       \n",
+      "   │     ┌─────┘│     │    │       \n",
+      "   │     │      │     │    │       \n",
+      "   v     v      v     v    v       \n",
+      " ┌───┐ ┌───┐ ┌───┐ ┌───┐ ┌───┐ ┌──┐\n",
+      " │q5 │ │q4 │ │q3 │ │q1 │ │q2 │ │q6│\n",
+      " └───┘ └───┘ └───┘ └───┘ └───┘ └──┘\n",
+      "Alice decided to *entangle* with agent 5, as their score was 0.9221049587533778\n",
+      "Person 5 experienced a sad event.\n",
+      "          ┌─────────────┐           \n",
+      "          │     q0      │           \n",
+      "          └─┬─┬┬────┬┬┬─┘           \n",
+      "            │ ││    │││             \n",
+      "   ┌────────┘ ││    ││└──────────┐  \n",
+      "   │     ┌────┘│    └┼─────┐     │  \n",
+      "   │     │     │     │     │     │  \n",
+      "   v     v     v     v     v     v  \n",
+      " ┌───┐ ┌───┐ ┌───┐ ┌───┐ ┌───┐ ┌───┐\n",
+      " │q6 │ │q5 │ │q4 │ │q3 │ │q1 │ │q2 │\n",
+      " └───┘ └───┘ └───┘ └───┘ └───┘ └───┘\n",
+      "Alice decided to *entangle* with agent 6, as their score was 0.6025804991707758\n",
+      "Person 6 experienced a sad event.\n",
+      "{'0': 598, '1': 426}\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/opt/homebrew/anaconda3/envs/iqh/lib/python3.11/site-packages/qiskit/visualization/circuit/matplotlib.py:266: FutureWarning: The default matplotlib drawer scheme will be changed to \"iqp\" in a following release. To silence this warning, specify the current default explicitly as style=\"clifford\", or the new default as style=\"iqp\".\n",
+      "  self._style, def_font_ratio = load_style(self._style)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 1222.56x785.944 with 1 Axes>"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "qc = core_story(6)\n",
+    "backend = BasicAer.get_backend('qasm_simulator')\n",
+    "job = execute(qc, backend)\n",
+    "result = job.result()\n",
+    "counts = result.get_counts()\n",
+    "print(counts)\n",
+    "qc.draw(output='mpl')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "n_shots = 50\n",
+    "label = np.arange(20)\n",
+    "overall = []\n",
+    "for n in range(20):\n",
+    "    qc = core_story(n)\n",
+    "    backend = BasicAer.get_backend('qasm_simulator')\n",
+    "    result_arr = []\n",
+    "    for j in range(n_shots):\n",
+    "        job = execute(qc, backend)\n",
+    "        result = job.result()\n",
+    "        counts = result.get_counts()\n",
+    "        if (counts.get('0') == None):\n",
+    "            counts['0'] = 0\n",
+    "        if (counts.get('1') == None):\n",
+    "            counts['1'] = 0\n",
+    "        p0 = counts.get('1')/(counts.get('0') + counts.get('1'))\n",
+    "        result_arr.append(p0)\n",
+    "    overall.append(result_arr)\n",
+    "    result_arr = []\n",
+    "\n",
+    "plt.violinplot(overall)\n",
+    "plt.xlabel('Number of connections (n)')\n",
+    "plt.ylabel('Expectation values')\n",
+    "\n",
+    "\n",
+    "\n",
+    "    \n",
+    "\n",
+    "# print(counts)\n",
+    "# qc.draw(output='mpl')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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": "3.11.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/simulation/constants.py b/simulation/constants.py
new file mode 100644
index 0000000..fa1ee08
--- /dev/null
+++ b/simulation/constants.py
@@ -0,0 +1,63 @@
+
+START = '''
+    ------------------- Welcome to the interactive ascii art graph creator! -------------------\n
+    ------------------------------- The Tale of Alice's Choices -------------------------------\n
+    -------------------------------------------------------------------------------------------\n
+    Alice, residing in a bustling city known for its vibrant community and endless opportunities, has established a solid network of potential connections. These connections span across various sectors including technology, arts, and social activism, providing her with a rich tapestry of relationships that could propel her career and personal growth. However, Alice finds herself at a crossroads, where each choice leads her down vastly different paths.
+    \n
+    REMEMBER! Once Alice makes a choice, she cannot go back or be able to select for another 9 months. Her decision will shape her future and the connections she will make. 
+'''
+
+
+SCENARIOS = [
+    
+    "Alice hears of a tech conference in Silicon Valley where Elon Musk is speaking. She can either stay and deepen ties with her local tech network or venture to the conference, hoping to gain insights and possibly meet Elon.",
+    
+    "A prestigious art residency in Paris offers Alice a chance to work alongside the renowned artist, Claude Monet. Staying would mean building her local art community, but Paris offers a once-in-a-lifetime creative mentorship.",
+
+    "An opportunity arises for Alice to attend a summit in Nairobi, with Malala Yousafzai as a keynote speaker. She can either continue her local social activism projects or seek global perspectives and personal advice from Malala.",
+
+    "Quentin Tarantino will be mentoring young filmmakers at a festival in Cannes. Alice could stay and work on her documentary with local filmmakers or try her luck in Cannes, potentially collaborating with Tarantino.",
+
+    "Alice has the chance to pitch her startup idea in Singapore, in front of tech mogul, Jack Ma. She could stay and refine her pitch with her current network or take a leap and present her idea on an international stage.",
+
+    "Attending Milan Fashion Week, Alice could potentially meet and learn from Giorgio Armani. She can either stay and cultivate her fashion startup at home or dive into the heart of global fashion trends and contacts.", 
+
+    "A conference in Iceland offers Alice the chance to meet Greta Thunberg and discuss climate initiatives. She can either further her local environmental efforts or seek inspiration and global partnerships in Iceland.",
+
+    "A music production workshop in Nashville promises a meeting with Dolly Parton. Alice could stay and strengthen her local music connections or pursue a dream mentorship in Nashville.",
+
+    "Renowned chef, Jiro Ono, is offering a selective sushi-making course in Tokyo. Alice could remain and grow her culinary startup locally or learn from a master and expand her culinary horizons.",
+
+    "J.K. Rowling is hosting a writers' retreat in Edinburgh. Alice can either continue her novel with the support of her local writers' group or seek Rowling's mentorship and inspiration in the historic city.",
+
+    "An opportunity to work with Kofi Annan’s foundation in Geneva presents itself. Alice can either stay and impact her community through local NGOs or gain international exposure and guidance in Geneva.",
+
+    "A symposium in Chile offers Alice the chance to meet Neil deGrasse Tyson at the world's largest observatory. She can either stay engaged with her local science community or embark on a journey to explore the cosmos with a leading astronomer.",
+
+    "Peter Jackson is offering a workshop for aspiring filmmakers in New Zealand. Alice could stay and produce her current project or take on the challenge of learning from a master in the breathtaking landscapes of New Zealand.",
+
+    "Bjarke Ingels is speaking at a summit on sustainable architecture. Alice can either continue her urban development projects at home or seek innovative insights and connections in Copenhagen.",
+
+    "Tony Robbins is hosting a retreat for entrepreneurs in Bali. Alice has the choice to stay and solidify her current business plans or attend the retreat, hoping to gain transformative strategies and personal advice from Robbins."
+
+]
+
+SCENARIOS_NAMES = [
+    "Elon Musk",
+    "Claude Monet",
+    "Malala Yousafzai",
+    "Quentin Tarantino",
+    "Jack Ma",
+    "Giorgio Armani",
+    "Greta Thunberg",
+    "Dolly Parton",
+    "Jiro Ono",
+    "J.K. Rowling",
+    "Kofi Annan",
+    "Neil deGrasse Tyson",
+    "Peter Jackson",
+    "Bjarke Ingels",
+    "Tony Robbins"
+]
+
diff --git a/simulation/interactiveAscii.py b/simulation/interactiveAscii.py
new file mode 100644
index 0000000..adbb429
--- /dev/null
+++ b/simulation/interactiveAscii.py
@@ -0,0 +1,73 @@
+
+from constants import *
+from ascii import *
+import networkx as nx
+from asciicode import graph_to_ascii
+import random
+
+
+def startScreen():
+    '''
+    Prints the start screen of the game
+    '''
+
+    print(START)
+    print("\n\n------------------------------------------------------------------------------------ \n")
+    print("------------------------------------------------------------------------------------ \n")
+
+
+def printScenarios(list_of_scenarios, list_of_names):
+    '''
+    Prints the scenarios and removes the chosen scenario from the list
+    '''
+
+    rand = random.randint(0, len(list_of_scenarios)-1)
+    ret_scenario, ret_name = list_of_scenarios[rand], list_of_names[rand]
+    list_of_scenarios.pop(rand)
+    list_of_names.pop(rand)
+    return ret_scenario, ret_name
+
+
+
+def askUser(list_of_scenarios, list_of_names, n_times = 1, cooldown = 0):
+    '''
+    Asks the user for input
+    '''
+
+    userInput = ""
+
+    for i in range(n_times):
+
+        if cooldown == 0:
+            scenario, name = printScenarios(list_of_scenarios, list_of_names)
+            potential_node_dic = getPotentialNodeDic()
+            print("Current potential connections: ", potential_node_dic["q0"])
+            print("\n\n")
+            print(scenario)
+            print("\n\n")
+                
+            userInput = input("Enter your choice (y/n): ")
+
+        if (userInput == "y" and cooldown == 0):
+            potential_node_dic = getPotentialNodeDic()
+            potential_node_dic["q0"].append(name)
+            logging(graph, True)
+            cooldown = 3
+            
+        elif (userInput == "n"):
+            print("You chose to stay")
+            logging(graph, True)
+            cooldown = cooldown - 1 if cooldown > 0 else 0
+
+        else:
+            print("You cannot make a choice yet")
+            logging(graph, True)
+            cooldown = cooldown - 1 if cooldown > 0 else 0
+
+            
+
+list_of_scenarios = SCENARIOS[:]
+list_of_names = SCENARIOS_NAMES[:]
+cooldown = 0
+startScreen()
+askUser(list_of_scenarios, list_of_names, 13)
diff --git a/simulation/records.csv b/simulation/records.csv
new file mode 100644
index 0000000..a03cd5a
--- /dev/null
+++ b/simulation/records.csv
@@ -0,0 +1,21 @@
+stock_price,role,mood
+0.3745401188473625,0.06613141580159124,0.05808361216819946
+0.8661761457749352,0.30922640143603375,0.7080725777960455
+0.13118910790805946,0.1289997910632404,0.3042422429595377
+0.43194501864211576,0.13727725034527527,0.6118528947223795
+0.049918445539589934,0.08701055182077448,0.7851759613930136
+0.5142344384136116,0.041895648037166514,0.046450412719997725
+0.6075448519014384,0.12620080066776382,0.6842330265121569
+0.4401524937396013,0.5036789471024806,0.4951769101112702
+0.1300170052944527,0.187193360515561,0.31171107608941095
+0.5467102793432796,0.3279532546747701,0.9695846277645586
+0.7751328233611146,0.43197762972720694,0.1959828624191452
+0.06783725794347398,0.2723454321123816,0.388677289689482
+0.8287375091519293,0.046571587637386884,0.14092422497476265
+0.8021969807540397,0.09011703172483441,0.9868869366005173
+0.7722447692966574,0.3830301689763141,0.7712703466859457
+0.21577585646889838,0.48244676714311874,0.11586905952512971
+0.6232981268275579,0.14437253957433874,0.32518332202674705
+0.7296061783380641,0.1954804733530948,0.8872127425763265
+0.4722149251619493,0.32049869667428815,0.5612771975694962
+0.770967179954561,0.11987558355667216,0.5227328293819941
diff --git a/team_falqon_readme.md b/team_falqon_readme.md
new file mode 100644
index 0000000..d563d57
--- /dev/null
+++ b/team_falqon_readme.md
@@ -0,0 +1,73 @@
+# Your Wonderland, Alice
+
+## Introduction
+What if you could figure out exactly who you needed to meet to move up the career ladder?
+What if you could network with colleagues, without the actual hassle of having to touch grass?
+What if, PROFESSIONAL NETWORKING was quantum??
+Welcome to "Your Wonderland, Alice" – a unique storytelling experience that merges the fascinating world of quantum mechanics with the intricacies of everyday life.
+
+## The Quantum Conundrum
+
+In this project, we invite you to explore a world where the fabric of reality is woven with quantum threads, and the act of networking is a quantum phenomenon. Picture this: What if you could figure out who exactly you need to connect to and meet to climb up the career ladder? What if you choose your best-fit peers that lead you to success?
+
+### Problem Statement
+
+Dive into the realm of quantum mechanics with a twist of science fiction. Imagine a world where common experiences, such as friendship, exhibit quantum interference and entanglement. Your mission is to craft a narrative rooted in this quantum reality, blending creativity with a solid scientific simulation using a quantum computer.
+
+### Solution Overview
+
+### Project Name: Your Wonderland, Alice
+
+### Synopsis
+
+In "Your Wonderland, Alice," we present a compelling narrative that transforms the act of networking into a quantum phenomenon. Using quantum algorithms and circuits, we simulate the dynamics of building connections in the professional world. The story unfolds as the player navigates through a quantum-inspired reality, making connections with individuals whose personalities, career positions, and even home-company stock prices influence the quantum entanglement between them.
+
+### Gameplay
+
+1. **Simulation of Networking**: Each potential network candidate is represented as a qubit, with their personality encoded using random parametrized r-gates (yellow gates in the circuit).
+
+2. **Quantum Entanglement**: The entangling gates (green gates) simulate the connections between individuals. We use one entangling gate per time step, gradually increasing the number of connections the player makes over time.
+
+3. **Weighted Connections**: The entanglement between two qubits is generalized into two people connecting with each other. Each connection is weighted based on the player's perception of the other person, considering factors such as personality, career position, and home-company stock price.
+
+4. **Success Criteria**: The player's success in the future is determined by the average state of their qubits. A state of '0' indicates failure, while '1' signifies success. The circuit is designed to stabilize over time, influencing the player's success through strategic networking.
+
+5. **Dynamic Events**: Each person in the quantum narrative may undergo events that alter their personality or social influence, introducing unpredictability and excitement to the storyline.
+
+### Circuit Construction
+
+<p align="center">
+  <img src="./images/circuit.png" />
+</p>
+
+1. **Phase 1 - Personality Encoding**: Random parametrized r-gates (yellow gates) encode the personality of each potential network candidate into their respective qubits.
+
+2. **Phase 2 - Entangling Connections**: Entangling gates (green gates) represent the influence and connection between individuals. The circuit evolves over multiple time steps, increasing the number of connections and their impact on the player's quantum state.
+
+## Getting Started
+
+Follow these steps to experience "Your Wonderland, Alice":
+
+To run a simulation of possible interactions:
+1. Navigate to the 'simulation' folder and run `backend_sim.ipynb`.
+2. You should be able to see Alice's social interaction graphs grow with time!
+
+To run a game with possible interactions:
+1. Navigate to the 'game' folder and run `backend_game.py`.
+2. You should be able to make decisions from the perspective of Alice, running on a quantum simulator backend!
+
+See PDF for the full storyline of Alice!
+
+## Contributors
+
+- [Andrea Miramontes Serrano](https://github.com/Andrea-MiramonSerr)
+- [Nelson Ooi](https://github.com/NelsonOoi)
+- [Samyam Lamichhane](https://github.com/declansam)
+- [Sarthak Prasad Malla](https://github.com/Sarthak-Malla/)
+- [Sasha Malik](https://github.com/Sasha-Malik)
+
+Enjoy your adventure in Wonderland!
+Professional networking, now made quantum. (TM)
+
+Slides link: https://docs.google.com/presentation/d/19Labb4im_o9X8df9W6Xwyb8P2xyy7DJsnExNSNEOs30/edit?usp=share_link
+