diff --git a/challenge-2022.10-oct/ramaibot/Amplitude_encoding_task3.ipynb b/challenge-2022.10-oct/ramaibot/Amplitude_encoding_task3.ipynb
new file mode 100644
index 0000000..9f0b6fb
--- /dev/null
+++ b/challenge-2022.10-oct/ramaibot/Amplitude_encoding_task3.ipynb
@@ -0,0 +1,665 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "43f4fb71-ab64-4c0f-96be-456136e1e83b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "<frozen importlib._bootstrap>:219: RuntimeWarning: scipy._lib.messagestream.MessageStream size changed, may indicate binary incompatibility. Expected 56 from C header, got 64 from PyObject\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "# Importing standard Qiskit libraries\n",
+    "from qiskit import QuantumCircuit, transpile, Aer, IBMQ\n",
+    "from qiskit.tools.jupyter import *\n",
+    "from qiskit.visualization import *\n",
+    "from ibm_quantum_widgets import *\n",
+    "from qiskit.providers.aer import QasmSimulator\n",
+    "\n",
+    "# Loading your IBM Quantum account(s)\n",
+    "provider = IBMQ.load_account()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "5bcae3bc-5a88-40c9-a57e-3fc593750443",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_angles(num_qubits,amp_arr):\n",
+    "    out_arr = []\n",
+    "    for k in range(1,num_qubits+1):\n",
+    "        val = 2**(num_qubits - k)\n",
+    "        for j in range(1,val+1):\n",
+    "            #print(j,k)\n",
+    "            new_val = 2**(k-1)\n",
+    "            numerator = 0\n",
+    "            denominator = 0\n",
+    "            \n",
+    "            for l in range(1,new_val + 1):\n",
+    "                pos = (2*j - 1) * (2**(k - 1)) + l\n",
+    "                #print(pos - 1)\n",
+    "                numerator_new = amp_arr[pos - 1]**2\n",
+    "                numerator += numerator_new\n",
+    "            \n",
+    "            for dl in range(1, (2**k)+1):\n",
+    "                pos = (j - 1) * (2**k) + dl\n",
+    "                #print(pos - 1)\n",
+    "                denominator_new = amp_arr[pos - 1]**2\n",
+    "                denominator += denominator_new\n",
+    "                \n",
+    "            final = np.sqrt(numerator) / np.sqrt(denominator + (10**-12))\n",
+    "            final_arcsin = 2 * np.arcsin(final)\n",
+    "            \n",
+    "            #print(j,k,final_arcsin)\n",
+    "            out_arr.append(final_arcsin)\n",
+    "            \n",
+    "    return out_arr\n",
+    "        "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "1a3e6683-a8a3-4e83-91a4-07fe4e3c4908",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[1.9529690593527242, 0.0, 0.5628396364478989]"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "get_angles(2,[0.53896774, 0.797503606, 0.27826503, 0.])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "b1f0fe7d-08aa-4b35-abdb-ae0453ff1729",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[0.0,\n",
+       " 0.0,\n",
+       " 0.0,\n",
+       " 1.2309438433181639,\n",
+       " 2.013705970177991,\n",
+       " 3.1415890019843085,\n",
+       " 1.159275449106933]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "get_angles(3,[0.44721, 0, 0.7071, 0, 0, 0, 0.44721, 0.31622])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "ae3b498b-a697-4e60-a05b-ad3e2e7c265e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[0.0,\n",
+       " 0.0,\n",
+       " 0.0,\n",
+       " 1.2309438433181639,\n",
+       " 0.0,\n",
+       " 0.0,\n",
+       " 0.0,\n",
+       " 1.2309438433181639,\n",
+       " 2.013705970177991,\n",
+       " 3.1415890019843085,\n",
+       " 2.013705970177991,\n",
+       " 3.1415890019843085,\n",
+       " 1.159275449106933,\n",
+       " 1.159275449106933,\n",
+       " 1.5707963267943965]"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "get_angles(4,[0.44721, 0, 0.7071, 0, 0, 0, 0.44721, 0.31622,0.44721, 0, 0.7071, 0, 0, 0, 0.44721, 0.31622])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "85729803-2a9b-468f-8172-e1b2e776b0c3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_binary(x,max_pad):\n",
+    "    if x == 0: \n",
+    "        arr = []\n",
+    "        for u in range(max_pad):\n",
+    "            arr.append(0)\n",
+    "        return arr\n",
+    "    bit = []\n",
+    "    new_arr = []\n",
+    "    while x:\n",
+    "        bit.append(x % 2)\n",
+    "        x >>= 1\n",
+    "        \n",
+    "    for u in range(max_pad - len(bit)):\n",
+    "        new_arr.append(0)\n",
+    "        \n",
+    "    new_arr.extend(bit[::-1])\n",
+    "    return new_arr"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "ba1cbda9-147f-4658-b848-262efd8c473c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1, 1, 0]\n"
+     ]
+    }
+   ],
+   "source": [
+    "arr = get_binary(6,3)\n",
+    "print(arr)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "777e2aa7-dead-427c-b146-4620d715968a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from qiskit.circuit.library.standard_gates import RYGate\n",
+    "from qiskit.circuit import Parameter\n",
+    "import matplotlib.pyplot as plt\n",
+    "from qiskit import QuantumCircuit,QuantumRegister\n",
+    "\n",
+    "def generate_circuit(num_qubits,angles):\n",
+    "    qr=QuantumRegister(num_qubits)\n",
+    "    circ=QuantumCircuit(qr) \n",
+    "    \n",
+    "    count = -1\n",
+    "    for i in range(0,num_qubits):\n",
+    "        control_qubits = []\n",
+    "        for u in range(0,i):\n",
+    "            control_qubits.append(u)\n",
+    "        \n",
+    "        val = int(2**i)\n",
+    "        for j in range(0,val):\n",
+    "            bin_arr = get_binary(j,i)\n",
+    "            if(i == 0):\n",
+    "                circ.ry(angles[count],i)\n",
+    "                count -= 1\n",
+    "                \n",
+    "            elif(i == 1):\n",
+    "                for x in range(len(bin_arr)):\n",
+    "                    if(bin_arr[x] == 1):\n",
+    "                        circ.x(x)\n",
+    "                        \n",
+    "                circ.cry(angles[count],0,i)\n",
+    "                count -= 1\n",
+    "                \n",
+    "                for x in range(len(bin_arr)):\n",
+    "                    if(bin_arr[x] == 1):\n",
+    "                        circ.x(x)\n",
+    "            \n",
+    "            else:\n",
+    "                for x in range(len(bin_arr)):\n",
+    "                    if(bin_arr[x] == 1):\n",
+    "                        circ.x(x)\n",
+    "                        \n",
+    "                circ.mcry(angles[count],control_qubits,i,q_ancillae=None)\n",
+    "                count -= 1\n",
+    "                \n",
+    "                for x in range(len(bin_arr)):\n",
+    "                    if(bin_arr[x] == 1):\n",
+    "                        circ.x(x)\n",
+    "                \n",
+    "        \n",
+    "        circ.barrier()\n",
+    "        \n",
+    "    circ.measure_all()\n",
+    "    return circ"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "e78cc9ba-fa20-4a68-9e79-2a6ae54d9a82",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 718.879x204.68 with 1 Axes>"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "circ = generate_circuit(2,get_angles(2,[0.53896774, 0.797503606, 0.27826503, 0.]))\n",
+    "circ.draw(output='mpl')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "2ed7c3b4-5ea4-4385-b7c1-b3154f205a1d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1621.88x565.88 with 1 Axes>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "circ = generate_circuit(3,get_angles(3,[0.44721, 0, 0.7071, 0, 0, 0, 0.44721, 0.31622]))\n",
+    "circ.draw(output='mpl')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "59b168e6-b8f2-41ed-a2cb-9cf8179d1440",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1621.88x2492.28 with 1 Axes>"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "circ = generate_circuit(4,get_angles(4,[0.44721, 0, 0.7071, 0, 0, 0, 0.44721, 0.31622,0.44721, 0, 0.7071, 0, 0, 0, 0.44721, 0.31622]))\n",
+    "circ.draw(output='mpl')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "b51e3c6b-7da5-4a31-9cc3-90473dd6695a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def testing(num_qubits,amp_sqrt):\n",
+    "    \n",
+    "    circ = generate_circuit(num_qubits,get_angles(num_qubits,amp_sqrt))\n",
+    "    circ.draw(output='mpl')\n",
+    "    \n",
+    "    backend_sim = Aer.get_backend('qasm_simulator')\n",
+    "\n",
+    "    # Execute the circuit on the qasm simulator.\n",
+    "    # We've set the number of repeats of the circuit\n",
+    "    # to be 1024, which is the default.\n",
+    "    job_sim = backend_sim.run(transpile(circ, backend_sim), shots=8192)\n",
+    "\n",
+    "    # Grab the results from the job.\n",
+    "    result_sim = job_sim.result()\n",
+    "    counts = result_sim.get_counts(circ)\n",
+    "    #print(counts)\n",
+    "    counts_new = {}\n",
+    "    for x in counts:\n",
+    "        counts_new[x] = (counts[x]/8192)\n",
+    "\n",
+    "    print(counts_new)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "b99b12ca-b606-4eaa-b344-4ea0702e0fb6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'111': 0.0980224609375, '011': 0.1988525390625, '010': 0.5062255859375, '000': 0.1968994140625}\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Amplitudes\n",
+    "# 000 -> 0.2\n",
+    "# 001 -> 0 \n",
+    "# 010 -> 0.5\n",
+    "# 011 -> 0\n",
+    "# 100 -> 0\n",
+    "# 101 -> 0\n",
+    "# 110 -> 0.2\n",
+    "# 111 -> 0.1\n",
+    "# We pass the Sqrt of these to get the origina amplitudes\n",
+    "testing(3, [0.44721, 0, 0.7071, 0, 0, 0, 0.44721, 0.31622])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "88be5572-0da3-4257-b0b5-4f96ac63d610",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'01': 0.0784912109375, '10': 0.63671875, '00': 0.2847900390625}\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Amplitudes\n",
+    "#00 -> 0.2783203125\n",
+    "#01 -> 0.0791015625\n",
+    "#10 -> 0.642578125\n",
+    "#11 -> 0\n",
+    "# We pass the Sqrt of these to get the original amplitudes\n",
+    "testing(2, [0.53896774, 0.797503606, 0.27826503, 0.])"
+   ]
+  }
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