diff --git a/challenges/QOSF challenge/Problema de GATO.pdf b/challenges/QOSF challenge/Problema de GATO.pdf
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diff --git a/challenges/QOSF challenge/README.md b/challenges/QOSF challenge/README.md
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+++ b/challenges/QOSF challenge/README.md	
@@ -0,0 +1,8 @@
+Equipo: Qutips
+
+Integrantes:
+Areli Yesareth Guerrero Estrada
+Janeth De Anda Gil
+Miguel Alejandro Medina Armendariz
+Edgar Omar Mendoza Lopez
+
diff --git a/challenges/QOSF challenge/main.ipynb b/challenges/QOSF challenge/main.ipynb
new file mode 100644
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+++ b/challenges/QOSF challenge/main.ipynb	
@@ -0,0 +1,329 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "132678df",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import qiskit\n",
+    "from qiskit import QuantumCircuit,execute,Aer,transpile,BasicAer\n",
+    "from qiskit.circuit.library import SwapGate,QFT,CZGate,SGate,TGate,GroverOperator\n",
+    "from qiskit.algorithms import Grover\n",
+    "#from qiskit.algorithms import Grover\n",
+    "from qiskit.quantum_info import Statevector\n",
+    "from qiskit.visualization import plot_histogram\n",
+    "import matplotlib as plt\n",
+    "import math"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "id": "15440717",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def inicializador(circuit):\n",
+    "    circuit.x(0)\n",
+    "    circuit.x(3)\n",
+    "    circuit.h(4)\n",
+    "    circuit.h(5)\n",
+    "    circuit.h(7)\n",
+    "    circuit.h(8)\n",
+    "    return circuit\n",
+    "\n",
+    "def contadorqubits():\n",
+    "    \n",
+    "    completo=QuantumCircuit(12,12)\n",
+    "    qft=QFT(3)    \n",
+    "    invqft=QFT(3,0,True,True)\n",
+    "    ctgate = TGate().control(1)\n",
+    "\n",
+    "    completo.barrier()\n",
+    "\n",
+    "    #inicializador de qft\n",
+    "    completo_2=completo.compose(qft,qubits=[9,10,11])    \n",
+    "    \n",
+    "    completo_2.append(ctgate, [4, 9])\n",
+    "    completo_2.cs(4,10)\n",
+    "    completo_2.cz(4,11)\n",
+    "    \n",
+    "    completo_2.barrier()\n",
+    "    completo_2.append(ctgate, [5, 9])\n",
+    "    completo_2.cs(5,10)\n",
+    "    completo_2.cz(5,11)\n",
+    "\n",
+    "    completo_2.barrier()\n",
+    "    completo_2.append(ctgate, [7, 9])\n",
+    "    completo_2.cs(7,10)\n",
+    "    completo_2.cz(7,11)\n",
+    "\n",
+    "    completo_2.barrier()\n",
+    "    completo_2.append(ctgate, [8, 9])\n",
+    "    completo_2.cs(8,10)\n",
+    "    completo_2.cz(8,11)\n",
+    "    #inversion de qft\n",
+    "    completo_3=completo_2.compose(invqft,qubits=[9,10,11])\n",
+    "    completo_3.barrier()\n",
+    "    return completo_3\n",
+    "\n",
+    "#coloca las todos los estados que tengo suma de 2\n",
+    "def prob2():\n",
+    "    completo_1=QuantumCircuit(14,14)\n",
+    "\n",
+    "    completo_1.x(11)\n",
+    "    completo_1.x(9)\n",
+    "    completo_1.ccx(9,10,12)\n",
+    "    completo_1.ccx(11,12,13)\n",
+    "    completo_1.z(13) # recordatorio z\n",
+    "    completo_1.ccx(9,10,12)\n",
+    "    completo_1.ccx(11,12,13)\n",
+    "    completo_1.x(9)\n",
+    "    completo_1.x(11)\n",
+    "    \n",
+    "    return completo_1\n",
+    "\n",
+    "# crea a grover \n",
+    "def instalargrover(aux):\n",
+    "    completo_1=QuantumCircuit(14,14)\n",
+    "    #revertir compuerta\n",
+    "    completo_1.barrier()   \n",
+    "\n",
+    "    completo_1.h(range(4,6))\n",
+    "    completo_1.h(range(7,9))\n",
+    "    completo_1.x(range(4,6))\n",
+    "    completo_1.x(range(7,9))   \n",
+    "    # completo_1.h(aux)\n",
+    "    # completo_1.x(aux)\n",
+    "    completo_1.barrier()\n",
+    "    # completo_1.ccx(4,5,15)\n",
+    "    # completo_1.ccx(7,8,14)\n",
+    "    # completo_1.ccx(15,14,aux)\n",
+    "\n",
+    "    completo_1.ccx(4,5,12)\n",
+    "    completo_1.ccx(7,12,aux)\n",
+    "    \n",
+    "    completo_1.barrier()\n",
+    "    completo_1.x(aux)\n",
+    "    completo_1.z(aux)\n",
+    "    completo_1.x(aux)\n",
+    "    completo_1.barrier()\n",
+    "    \n",
+    "    completo_1.ccx(7,12,8)\n",
+    "    completo_1.ccx(4,5,12)\n",
+    "    # completo_1.ccx(15,14,aux)\n",
+    "    # completo_1.ccx(4,5,15)\n",
+    "    # completo_1.ccx(7,8,14)\n",
+    "    completo_1.barrier()\n",
+    "    completo_1.x(range(4,6))\n",
+    "    completo_1.x(range(7,9))\n",
+    "    completo_1.h(range(4,6))\n",
+    "    completo_1.h(range(7,9))\n",
+    "    # completo_1.x(aux)\n",
+    "    # completo_1.h(aux)\n",
+    "\n",
+    "    return completo_1\n",
+    "\n",
+    "#la condicion que acepta solo a las dos casos 1100 y 1001 ambos casos tiene 1 en a y 0 en c\n",
+    "def ganador():\n",
+    "    completo_1=QuantumCircuit(14,14)\n",
+    "    completo_1.barrier()\n",
+    "    completo_1.x(7)\n",
+    "    # completo_1.h(13)\n",
+    "    # completo_1.z(13)\n",
+    "    # completo_1.x(13)\n",
+    "    completo_1.ccx(4,7,12)\n",
+    "    \n",
+    "    completo_1.z(12)\n",
+    "    \n",
+    "    return completo_1\n",
+    "\n",
+    "#crea las medicione qubtis con cbit\n",
+    "def medicion(circuit,inicio,fin,total_qubits=-1):\n",
+    "    if total_qubits!=-1:\n",
+    "        for i in range(total_qubits):\n",
+    "            circuit.measure(i,i)\n",
+    "    else:\n",
+    "        for i in range(inicio,fin+1):\n",
+    "            circuit.measure(i,i)\n",
+    "    return circuit\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "id": "38de9fc6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bits=qubits=14\n",
+    "\n",
+    "circuit=QuantumCircuit(qubits,bits)         #Crear un circuito con 9 qubits para el primer parametro y 9 bits para el segundo parametro\n",
+    "\n",
+    "circuit=inicializador(circuit)\n",
+    "\n",
+    "# circuit.draw(output='mpl')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "id": "33c10780",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "circuit_1=contadorqubits()\n",
+    "completo1=circuit.compose(circuit_1)\n",
+    "# circuit_1.draw(output='mpl')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "id": "71368768",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# identifica los 2\n",
+    "circuit_2=prob2()\n",
+    "completo2=completo1.compose(circuit_2)\n",
+    "# circuit_2.draw(output='mpl')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "id": "cb3bfc95",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "circuit_3=instalargrover(8)    #crea grover de  4\n",
+    "completo3=completo2.compose(circuit_3)\n",
+    "\n",
+    "# circuit_3.draw(output='mpl')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "7373c303",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "circuit_4=ganador()    #busca solo las dos ultimas opciones 1100 y 1001\n",
+    "completo4=completo3.compose(circuit_4)\n",
+    "\n",
+    "# circuit_4.draw(output='mpl')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "circuit_5=instalargrover(8) #aplifica las dos opciones de arriba\n",
+    "completo5=completo4.compose(circuit_5)\n",
+    "# circuit_5.draw(output='mpl')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "id": "f6e926ac",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "circuit6=medicion(completo3,4,5)\n",
+    "circuit6=medicion(completo3,7,8)\n",
+    "# # circuit6.measure(13,13)\n",
+    "# circuit6.measure(16,16)\n",
+    "# circuit6.draw(output='mpl')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "id": "5bc81104",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'00000110111001': 0.035714285714285594,\n",
+       " '00001110111001': 0.32142857142857145,\n",
+       " '00011110111001': 0.32142857142857145,\n",
+       " '00100110111001': 3.3148190922642085e-33,\n",
+       " '00101110111001': 6.936225887984991e-33,\n",
+       " '00110110111001': 2.8010988534636696e-32,\n",
+       " '00111110111001': 4.278116777314028e-33,\n",
+       " '10010110111001': 0.32142857142857145}"
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "backend = Aer.get_backend('statevector_simulator')\n",
+    "job = execute(circuit6, backend=backend)\n",
+    "job_result = job.result()\n",
+    "vector = job_result.get_statevector(circuit6)\n",
+    "Statevector(vector).probabilities_dict()\n",
+    "# for i in vector:\n",
+    "#     print(i)\n",
+    "# print(job_result.get_statevector(circuit6))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "31dc15e9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 504x360 with 1 Axes>"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "simulacion=Aer.get_backend('qasm_simulator') #crear una instancia de simulacion\n",
+    "job=execute(circuit6,simulacion,shot=1000)\n",
+    "result=job.result()\n",
+    "counts=result.get_counts(circuit6)\n",
+    "plot_histogram(counts,sort='value_desc')"
+   ]
+  }
+ ],
+ "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": "3.9.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/challenges/QOSF challenge/programa_muestra2.py b/challenges/QOSF challenge/programa_muestra2.py
new file mode 100644
index 0000000..f63993e
--- /dev/null
+++ b/challenges/QOSF challenge/programa_muestra2.py	
@@ -0,0 +1,280 @@
+import qiskit
+from qiskit import QuantumCircuit,execute,Aer,transpile,BasicAer
+from qiskit.circuit.library import SwapGate,QFT,CZGate,SGate,TGate,GroverOperator
+from qiskit.algorithms import Grover
+#from qiskit.algorithms import Grover
+from qiskit.quantum_info import Statevector
+from qiskit.visualization import plot_histogram
+import matplotlib as plt
+import math
+
+def inicializador(circuit,total_qubits):
+    circuit.x(0)
+    circuit.x(3)
+    circuit.h(4)
+    circuit.h(5)
+    circuit.h(7)
+    circuit.h(8)
+    return circuit
+
+def inicializador2(circuit,total_qubits): 
+    circuit.x(4)
+    circuit.x(5)
+    circuit.x(7)
+    return circuit
+    #circuit.x(8)
+def contadorqubits(circuit,total_qubits):
+    qubits=total_qubits
+    bits=total_qubits-1
+    contador=QuantumCircuit(qubits,bits)
+    #inicializador de qft
+    '''
+    contador.h(0)
+    contador.cp(math.pi/2,0,1)
+    contador.cp(math.pi/2,0,2)
+    contador.h(1)
+    contador.cp(math.pi/8,0,3)
+    contador.cp(math.pi/2,1,2)
+    contador.cp(math.pi/4,1,4)
+    contador.h(3)
+    contador.cp(math.pi/2,2,3)
+    contador.swap(1,2)
+    contador.h(3)
+    contador.swap(0,3)
+    '''
+    completo=QuantumCircuit(12,12)
+    completoprueba=QuantumCircuit(12,12)
+    qft=QFT(3)
+    #print(completo)
+    #print(qft)
+    #print(completo)
+    invqft=QFT(3,0,True,True)
+    completo_1=completo.compose(circuit)
+    #print(completo_1)
+    #print(qft)
+    completo_2=completo_1.compose(qft,qubits=[9,10,11])
+    ctgate = TGate().control(1)
+    #inversion de qft
+    completo_2.append(ctgate, [4, 9])
+    completo_2.cs(4,10)
+    completo_2.cz(4,11)
+    
+    completo_2.barrier()
+    completo_2.append(ctgate, [5, 9])
+    completo_2.cs(5,10)
+    completo_2.cz(5,11)
+
+    completo_2.barrier()
+    completo_2.append(ctgate, [7, 9])
+    completo_2.cs(7,10)
+    completo_2.cz(7,11)
+
+    completo_2.barrier()
+    completo_2.append(ctgate, [8, 9])
+    completo_2.cs(8,10)
+    completo_2.cz(8,11)
+    
+    
+    
+    
+
+    
+    
+    
+    
+    
+    #completo_2.ct(4,10)
+    #completo_2.ct(5,10)
+    #completo_2.ct(7,10)
+    #completo_2.ct(8,10)
+    completo_3=completo_2.compose(invqft,qubits=[9,10,11])
+    completo_3.barrier()
+    #medicion(completo_3,0,8)
+    #print(completo_3)
+    return completo_3
+#coloca las todos los estados que tengo suma de 2
+def prob2(circuit):
+    completo=QuantumCircuit(14,14)
+    #print("soy compleot 2")
+    completo_1=completo.compose(circuit)
+    #print(completo_1)
+    completo_1.x(11)
+    completo_1.x(9)
+    completo_1.ccx(9,10,12)
+    completo_1.ccx(11,12,13)
+    #completo_1.h(13)
+    completo_1.z(13) # recordatorio z
+    completo_1.h(13)
+    #completo_1.x(9)#dudas
+    #print(completo_1)
+    
+    return completo_1
+'''
+def oraculo(circuit):
+    completo=QuantumCircuit(16,16)
+    completo_1=completo.compose(circuit)
+    completo_1.barrier()
+
+    #completo_1.h(13)
+    #completo_1.x(13)
+    completo_1.barrier()
+    completo_1.ccx(4,5,15)
+    completo_1.ccx(7,8,14)
+    completo_1.ccx(15,14,13)
+    completo_1.barrier()
+    completo_1.x(13)
+    completo_1.z(13)
+    completo_1.x(13)
+    completo_1.barrier()
+    completo_1.ccx(4,5,15)
+    completo_1.ccx(7,8,14)
+    completo_1.ccx(15,14,13)
+
+    return completo_1
+    '''
+# crea a grover 
+def instalargrover(circuit):
+    completo=QuantumCircuit(17,17)
+    completo_1=completo.compose(circuit)
+    #revertir compuerta
+    completo_1.barrier()
+    completo_1.ccx(9,10,12)
+    #completo_1.x(13)
+    #completo_1.h(13)
+    completo_1.h(range(4,6))
+    completo_1.h(range(7,9))
+    completo_1.x(range(4,6))
+    completo_1.x(range(7,9))
+    completo_1.barrier()
+
+    completo_1.h(13)
+    completo_1.x(13)
+    completo_1.barrier()
+    completo_1.ccx(4,5,15)
+    completo_1.ccx(7,8,14)
+    completo_1.ccx(15,14,13)
+    completo_1.barrier()
+    completo_1.x(13)
+    completo_1.z(13)
+    completo_1.x(13)
+    completo_1.barrier()
+    completo_1.ccx(4,5,15)
+    completo_1.ccx(7,8,14)
+    completo_1.ccx(15,14,13)
+
+    completo_1.barrier()
+    completo_1.x(range(4,6))
+    completo_1.x(range(7,9))
+    completo_1.h(range(4,6))
+    completo_1.h(range(7,9))
+    completo_1.x(13)
+    completo_1.h(13)
+
+    return completo_1
+#la condicion que acepta solo a las dos casos 1100 y 1001 ambos casos tiene 1 en a y 0 en c
+def ganador(circuit):
+    completo=QuantumCircuit(17,17)
+    completo_1=completo.compose(circuit)
+    completo_1.x(7)
+    completo_1.h(13)
+    completo_1.z(13)
+    completo_1.x(13)
+    completo_1.ccx(4,7,13)
+    
+    completo_1.z(13)
+    
+    return completo_1 
+#crea las medicione qubtis con cbit
+def medicion(circuit,inicio,fin,total_qubits=-1):
+    if total_qubits!=-1:
+        for i in range(total_qubits):
+            circuit.measure(i,i)
+    else:
+        for i in range(inicio,fin+1):
+            circuit.measure(i,i)
+    return circuit
+'''
+def arregloestados_not(circuit,total_qubits):
+    circuit.x(range(total_qubits))
+    
+def arregloestados_h(circuit,total_qubits):
+    circuit.h(range(total_qubits))
+'''
+#crea una barrera 
+def separador(circuit):
+    circuit.barrier()
+    return circuit
+#imprime el circuito
+def impresion_circuito(circuit):
+    # print(circuit)
+    circuit.draw('mpl').show()
+
+def main():
+    #inicializacion de parametros para qiskit 
+    bits=qubits=9
+    simulacion=Aer.get_backend('qasm_simulator') #crear una instancia de simulacion
+    simulacion2=BasicAer.get_backend('statevector_simulator')
+    circuit=QuantumCircuit(qubits,bits)         #Crear un circuito con 9 qubits para el primer parametro y 9 bits para el segundo parametro
+    circuit=inicializador(circuit,total_qubits=qubits)
+    #inicializador2(circuit,total_qubits=qubits)
+    #arregloestados_h(circuit,total_qubits=qubits)
+    #arregloestados_not(circuit,total_qubits=qubits)
+    
+    circuit=separador(circuit)
+
+    #medicion(circuit,4,5)
+    #medicion(circuit,7,8)
+    circuit_1=contadorqubits(circuit,3)
+    
+    
+    circuit2=prob2(circuit_1)   # identifica los 2
+    # separador(circuit2)
+    #print(circuit2)
+    #print(circuit2)
+    #print(circuit)
+    #circuit=medicion(circuit,0,0,9)
+    #print(circuit)
+    #circuit2=oraculo(circuit2)
+    circuit3=instalargrover(circuit2)    #crea grover de  4
+    circuit3.barrier()
+    
+    
+
+    circuit3=ganador(circuit3)   #busca solo las dos ultimas opciones 1100 y 1001
+    circuit3.barrier()
+
+    circuit3=instalargrover(circuit3) #aplifica las dos opciones de arriba
+    circuit3.barrier()
+
+    circuit3=medicion(circuit3,4,8)
+
+    impresion_circuito(circuit3)
+    #circuit3=medicion(circuit3,4,8)
+    #circuit3=medicion(circuit2,9,13)
+    job=execute(circuit3,simulacion,shot=1000)
+    #job2=execute(circuit3,simulacion2)
+    result=job.result()
+    #result2=job2.result()
+    #vector=result2.get_statevector(circuit3)
+    #print(vector)
+    counts=result.get_counts(circuit3)
+    # print(counts)
+    # plot_histogram(counts,sort='value_desc')
+
+
+    return 0
+
+if __name__=='__main__':
+    main()
+'''
+circuit.h(0)
+circuit.cx(0,1)
+circuit.measure([0,1],[0,1])
+job=execute(circuit,simulacion,shot=1000)
+result=job.result()
+counts=result.get_counts(circuit)
+print("\n Total count for 00 and 11 are: ",counts)
+#circuit.draw()
+print(circuit)
+#circuit.draw(output='mpl')
+'''
\ No newline at end of file
diff --git a/challenges/QuantumQuipu challenge/FeatureMapVariacional.ipynb b/challenges/QuantumQuipu challenge/FeatureMapVariacional.ipynb
new file mode 100644
index 0000000..62018f0
--- /dev/null
+++ b/challenges/QuantumQuipu challenge/FeatureMapVariacional.ipynb	
@@ -0,0 +1,383 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "id": "63c81904-9cfe-49d3-ae18-84663fd538af",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 534
+        },
+        "id": "63c81904-9cfe-49d3-ae18-84663fd538af",
+        "outputId": "033dee57-fc0f-4dc1-b132-8decf2153253"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "[-1.00000000e+00 -9.00000000e-01 -8.00000000e-01 -7.00000000e-01\n",
+            " -6.00000000e-01 -5.00000000e-01 -4.00000000e-01 -3.00000000e-01\n",
+            " -2.00000000e-01 -1.00000000e-01 -2.22044605e-16  1.00000000e-01\n",
+            "  2.00000000e-01  3.00000000e-01  4.00000000e-01  5.00000000e-01\n",
+            "  6.00000000e-01  7.00000000e-01  8.00000000e-01  9.00000000e-01]\n"
+          ]
+        },
+        {
+          "data": {
+            "text/plain": [
+              "<matplotlib.collections.PathCollection at 0x7bc09fc0ace0>"
+            ]
+          },
+          "execution_count": 2,
+          "metadata": {},
+          "output_type": "execute_result"
+        },
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "import pennylane as qml\n",
+        "from pennylane import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "# Generamos nuestro dataset\n",
+        "\n",
+        "data = np.arange(-1,1,0.1)\n",
+        "print(data)\n",
+        "labels = np.ones(len(data))\n",
+        "labels[len(data)//4:-len(data)//4] *= -1\n",
+        "\n",
+        "plt.scatter(data, np.zeros(len(data)), color = \"blue\")\n",
+        "plt.scatter(data[:len(data)//4], np.zeros(len(data)//4), color = \"orange\")\n",
+        "plt.scatter(data[-len(data)//4:], np.zeros(len(data)//4), color = \"orange\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "id": "8e71c3fa-3b0d-4a3a-9dfb-a2e3249ff658",
+      "metadata": {
+        "id": "8e71c3fa-3b0d-4a3a-9dfb-a2e3249ff658"
+      },
+      "outputs": [],
+      "source": [
+        "\n",
+        "# Entrenamiento estandar de una red neuronal cuántica\n",
+        "\n",
+        "def feature_map(x):\n",
+        "    qml.Hadamard(wires = 0)\n",
+        "    qml.RZ(x , wires = 0)\n",
+        "\n",
+        "def variational_block(params):\n",
+        "    qml.U3(params[0], params[1], params[2], wires = 0)\n",
+        "\n",
+        "dev = qml.device(\"default.qubit\", wires = 1)\n",
+        "\n",
+        "@qml.qnode(dev)\n",
+        "def circuit(x, params):\n",
+        "\n",
+        "    feature_map(x)\n",
+        "    variational_block(params)\n",
+        "\n",
+        "    return qml.expval(qml.PauliZ(0))\n",
+        "\n",
+        "\n",
+        "def error(data, labels, params):\n",
+        "\n",
+        "    err = 0\n",
+        "    for i in range(len(data)):\n",
+        "        d = circuit(data[i], params)\n",
+        "        err -= labels[i] * d\n",
+        "\n",
+        "    return err / len(data)\n",
+        "\n",
+        "def accuracy(data, labels, params):\n",
+        "\n",
+        "    n = 0\n",
+        "    for i in range(len(data)):\n",
+        "        if labels[i] == np.sign(circuit(data[i], params)):\n",
+        "            n += 1\n",
+        "\n",
+        "    return n / len(data)\n",
+        "\n",
+        "\n",
+        "def train(data, labels, error_function, params, stepsize = 0.1, epochs = 100, plot = True):\n",
+        "    opt = qml.AdagradOptimizer(stepsize = stepsize)\n",
+        "\n",
+        "    costs = []\n",
+        "    for epoch in range(epochs):\n",
+        "        params, cost = opt.step_and_cost(lambda params: error_function(data, labels, params), params)\n",
+        "        costs.append(cost)\n",
+        "\n",
+        "    if plot:\n",
+        "        plt.plot(costs)\n",
+        "\n",
+        "    return params\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "id": "632f44b5-10c2-43a0-96e4-68b8349d9e17",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 502
+        },
+        "id": "632f44b5-10c2-43a0-96e4-68b8349d9e17",
+        "outputId": "97a51fb8-81aa-402b-8c7a-ec0c698aa65a"
+      },
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "/usr/local/lib/python3.10/dist-packages/autograd/numpy/numpy_vjps.py:698: ComplexWarning: Casting complex values to real discards the imaginary part\n",
+            "  onp.add.at(A, idx, x)\n"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "0.5\n"
+          ]
+        },
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "initial_params = np.random.rand(3, requires_grad = True)\n",
+        "#print(initial_params)\n",
+        "params = train(data, labels, error, initial_params)\n",
+        "print(accuracy(data, labels, params))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "b169fbd6-30bd-4f84-afb4-a5ab167a0a41",
+      "metadata": {
+        "id": "b169fbd6-30bd-4f84-afb4-a5ab167a0a41"
+      },
+      "source": [
+        "Recordemos que la formula del error para entrenar nuestro feature map F es:\n",
+        "\n",
+        "\n",
+        "$$\\text{Error} = -\\sum_i\\sum_j y_iy_j\\langle F(x_i)^{\\dagger}F(x_j)\\rangle$$\n",
+        "\n",
+        "Definiremos por tanto un feature map variacional y minimizaremos dicho error."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "id": "2946efd0-869d-4569-a7db-dfcdffbcba87",
+      "metadata": {
+        "id": "2946efd0-869d-4569-a7db-dfcdffbcba87"
+      },
+      "outputs": [],
+      "source": [
+        "def feature_map(x, feature_param):\n",
+        "    qml.Hadamard(wires = 0)\n",
+        "    qml.RZ(x * feature_param, wires = 0)\n",
+        "\n",
+        "def feature_error(data, labels, feature_param):\n",
+        "\n",
+        "    dev = qml.device(\"default.qubit\", wires = 1)\n",
+        "    @qml.qnode(dev)\n",
+        "    def circuit(x1, x2):\n",
+        "        feature_map(x1, feature_param)\n",
+        "        qml.adjoint(feature_map)(x2, feature_param)\n",
+        "        return qml.probs(wires = 0)\n",
+        "\n",
+        "    err = 0\n",
+        "    for i in range(len(data)):\n",
+        "        for j in range(i, len(data)):\n",
+        "            d = circuit(data[i], data[j])\n",
+        "            err -= labels[i] * labels[j] * d[0][0]\n",
+        "    return err\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "id": "3a7d2a68-71b9-4e8a-bbe2-a40f95ac35d5",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 430
+        },
+        "id": "3a7d2a68-71b9-4e8a-bbe2-a40f95ac35d5",
+        "outputId": "2fd2d176-3119-464e-8d15-ce40d29e9525"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "initial_param = np.random.rand(1, requires_grad = True)\n",
+        "param = train(data, labels, feature_error, initial_param)\n",
+        "#param\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "id": "e932af5a-71ea-4ece-9992-dd1466af1951",
+      "metadata": {
+        "id": "e932af5a-71ea-4ece-9992-dd1466af1951"
+      },
+      "outputs": [],
+      "source": [
+        "def feature_map(x):\n",
+        "    qml.Hadamard(wires = 0)\n",
+        "    qml.RZ(x*3.1416 , wires = 0)\n",
+        "\n",
+        "def variational_block(params):\n",
+        "    qml.U3(params[0], params[1], params[2], wires = 0)\n",
+        "\n",
+        "dev = qml.device(\"default.qubit\", wires = 1)\n",
+        "\n",
+        "@qml.qnode(dev)\n",
+        "def circuit(x, params):\n",
+        "\n",
+        "    feature_map(x)\n",
+        "    variational_block(params)\n",
+        "\n",
+        "    return qml.expval(qml.PauliZ(0))\n",
+        "\n",
+        "\n",
+        "def error(data, labels, params):\n",
+        "\n",
+        "    err = 0\n",
+        "    for i in range(len(data)):\n",
+        "        d = circuit(data[i], params)\n",
+        "        err -= labels[i] * d\n",
+        "\n",
+        "    return err / len(data)\n",
+        "\n",
+        "def accuracy(data, labels, params):\n",
+        "\n",
+        "    n = 0\n",
+        "    for i in range(len(data)):\n",
+        "        if labels[i] == np.sign(circuit(data[i], params)):\n",
+        "            n += 1\n",
+        "\n",
+        "    return n / len(data)\n",
+        "\n",
+        "\n",
+        "def train(data, labels, error_function, params, stepsize = 0.1, epochs = 100, plot = True):\n",
+        "    opt = qml.AdagradOptimizer(stepsize = stepsize)\n",
+        "\n",
+        "    costs = []\n",
+        "    for epoch in range(epochs):\n",
+        "        params, cost = opt.step_and_cost(lambda params: error_function(data, labels, params), params)\n",
+        "        costs.append(cost)\n",
+        "\n",
+        "    if plot:\n",
+        "        plt.plot(costs)\n",
+        "\n",
+        "    return params"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "id": "Ykhq74BeDmHP",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 447
+        },
+        "id": "Ykhq74BeDmHP",
+        "outputId": "eadd5384-1f14-4802-90b3-f189449f6040"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "1.0\n"
+          ]
+        },
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "initial_params = np.random.rand(3, requires_grad = True)\n",
+        "#print(initial_params)\n",
+        "params = train(data, labels, error, initial_params)\n",
+        "print(accuracy(data, labels, params))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "id": "rMGgWPfnDmz8",
+      "metadata": {
+        "id": "rMGgWPfnDmz8"
+      },
+      "outputs": [],
+      "source": []
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "PennyLane",
+      "language": "python",
+      "name": "pennylane"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.9.13"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 5
+}
diff --git a/challenges/QuantumQuipu challenge/clasificador.ipynb b/challenges/QuantumQuipu challenge/clasificador.ipynb
new file mode 100644
index 0000000..082ec4b
--- /dev/null
+++ b/challenges/QuantumQuipu challenge/clasificador.ipynb	
@@ -0,0 +1,265 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 465
+        },
+        "id": "t1Hw2njgOBAM",
+        "outputId": "161cb288-77d1-44c4-ad11-db32223ed05e"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "[0.23, -0.88, -0.34, 0.12, 0.13, 0.56, -0.5, -0.4, 0.12, 0.23, 0.98, -0.12, -0.05, 0.91, 0.99, 0.78, 0.87, -0.03, 0.89, 0.97]\n"
+          ]
+        },
+        {
+          "data": {
+            "text/plain": [
+              "<matplotlib.collections.PathCollection at 0x2510e091f00>"
+            ]
+          },
+          "execution_count": 2,
+          "metadata": {},
+          "output_type": "execute_result"
+        },
+        {
+          "data": {
+            "image/png": "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",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "needs_background": "light"
+          },
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "import pennylane as qml\n",
+        "from pennylane import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "# Generamos nuestro dataset\n",
+        "\n",
+        "data = np.arange(-1,1,0.1)\n",
+        "data = [0.23,-0.88,-0.34,.12,.13,.56,-.5,-.4,.12,0.23,0.98,-0.12,-0.05,.91,.99,.78,.87,-.03,.89,0.97]\n",
+        "print(data)\n",
+        "labels = np.ones(len(data))\n",
+        "labels[len(data)//4:-len(data)//4] *= -1\n",
+        "\n",
+        "plt.scatter(data, np.zeros(len(data)), color = \"blue\")\n",
+        "plt.scatter(data[:len(data)//4], np.zeros(len(data)//4), color = \"orange\")\n",
+        "plt.scatter(data[-len(data)//4:], np.zeros(len(data)//4), color = \"orange\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {
+        "id": "BKxHOZ3kZOFP"
+      },
+      "outputs": [],
+      "source": [
+        "# Entrenamiento estandar de una red neuronal cuántica\n",
+        "\n",
+        "def feature_map(x):\n",
+        "    qml.Hadamard(wires = 0)\n",
+        "    qml.RZ(x , wires = 0)\n",
+        "    qml.Hadamard(wires = 1)\n",
+        "    qml.RZ(x , wires = 1)\n",
+        "\n",
+        "def variational_block(params):\n",
+        "    qml.U3(params[0], params[1], params[2], wires = 0)\n",
+        "    qml.U3(params[0], params[1], params[2], wires = 1)\n",
+        "\n",
+        "dev = qml.device(\"default.qubit\", wires = 2)\n",
+        "\n",
+        "@qml.qnode(dev)\n",
+        "def circuit(x, params):\n",
+        "    feature_map(x)\n",
+        "    variational_block(params)\n",
+        "    return qml.expval(qml.PauliZ(0)), qml.expval(qml.PauliZ(1))\n",
+        "\n",
+        "\n",
+        "def error(data, labels, params):\n",
+        "    err = 0\n",
+        "    for i in range(len(data)):\n",
+        "        d0 = circuit(data[i], params)[0]\n",
+        "        d1 = circuit(data[i], params)[1]\n",
+        "        err -= (labels[i] * d0+labels[i] * d1)\n",
+        "    return err / len(data)\n",
+        "\n",
+        "def accuracy(data, labels, params):\n",
+        "    n = 0\n",
+        "    for i in range(len(data)):\n",
+        "        if labels[i] == np.sign(circuit(data[i], params)[0]):\n",
+        "            n += 1\n",
+        "        if labels[i] == np.sign(circuit(data[i], params)[1]):\n",
+        "            n += 1\n",
+        "    return n / len(data)\n",
+        "\n",
+        "\n",
+        "def train(data, labels, error_function, params, stepsize = 0.1, epochs = 100, plot = True):\n",
+        "    opt = qml.AdagradOptimizer(stepsize = stepsize)\n",
+        "\n",
+        "    costs = []\n",
+        "    for epoch in range(epochs):\n",
+        "        params, cost = opt.step_and_cost(lambda params: error_function(data, labels, params), params)\n",
+        "        costs.append(cost)\n",
+        "\n",
+        "    if plot:\n",
+        "        plt.plot(costs)\n",
+        "\n",
+        "    return params\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 502
+        },
+        "id": "yEJLs99lZbyP",
+        "outputId": "071e52df-953f-4615-dd7e-b4dd7c60d031"
+      },
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "C:\\Users\\usuario\\AppData\\Roaming\\Python\\Python310\\site-packages\\autograd\\numpy\\numpy_vjps.py:698: ComplexWarning: Casting complex values to real discards the imaginary part\n",
+            "  onp.add.at(A, idx, x)\n"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "1.0\n"
+          ]
+        },
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "needs_background": "light"
+          },
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "initial_params = np.random.rand(3, requires_grad = True)\n",
+        "#print(initial_params)\n",
+        "params = train(data, labels, error, initial_params)\n",
+        "print(accuracy(data, labels, params))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {
+        "id": "Kr1K-3oOZfbc"
+      },
+      "outputs": [],
+      "source": [
+        "def feature_map(x, feature_param):\n",
+        "    qml.Hadamard(wires = 0)\n",
+        "    qml.RZ(x * feature_param, wires = 0)\n",
+        "\n",
+        "def feature_error(data, labels, feature_param):\n",
+        "\n",
+        "    dev = qml.device(\"default.qubit\", wires = 1)\n",
+        "    @qml.qnode(dev)\n",
+        "    def circuit(x1, x2):\n",
+        "        feature_map(x1, feature_param)\n",
+        "        qml.adjoint(feature_map)(x2, feature_param)\n",
+        "        return qml.probs(wires = 0)\n",
+        "\n",
+        "    err = 0\n",
+        "    for i in range(len(data)):\n",
+        "        for j in range(i, len(data)):\n",
+        "            d = circuit(data[i], data[j])\n",
+        "            err -= labels[i] * labels[j] * d[0][0]\n",
+        "    return err\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 447
+        },
+        "id": "XUgMOfURZk3o",
+        "outputId": "cd82cf74-5b61-4ed5-b09a-e0a91844df4f"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "tensor([3.36422896], requires_grad=True)"
+            ]
+          },
+          "execution_count": 6,
+          "metadata": {},
+          "output_type": "execute_result"
+        },
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "needs_background": "light"
+          },
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "initial_param = np.random.rand(1, requires_grad = True)\n",
+        "param = train(data, labels, feature_error, initial_param)\n",
+        "param"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "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": "3.10.4"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/challenges/QuantumQuipu challenge/variacionales_2_parametros.ipynb b/challenges/QuantumQuipu challenge/variacionales_2_parametros.ipynb
new file mode 100644
index 0000000..63bffda
--- /dev/null
+++ b/challenges/QuantumQuipu challenge/variacionales_2_parametros.ipynb	
@@ -0,0 +1,287 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "g3C_f1o1x435",
+        "outputId": "73d7f58c-a1d7-41de-bd36-31c3e211fd63"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "[[0.23, -0.88, -0.34, 0.12, 0.13, 0.56, -0.5, -0.4, 0.12, 0.23, 0.98, -0.12, -0.05, 0.91, 0.99, 0.78, 0.87, -0.03, 0.89, 0.97], [-1, -1, -1, -1, -1, 1, -1, -1, -1, -1, 1, -1, -1, 1, 1, 1, 1, -1, 1, 1]]\n"
+          ]
+        }
+      ],
+      "source": [
+        "import pennylane as qml\n",
+        "from pennylane import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "# Generamos nuestro dataset\n",
+        "\n",
+        "# data = [0.23,-0.88,-0.34,.12,.13,.56,-.5,-.4,.12,0.23,0.98,-0.12,-0.05,.91,.99,.78,.87,-.03,.89,0.97]\n",
+        "# data2 =  [-1,-1,-1,-1,-1,1,-1,-1,-1,-1,1,-1,-1,1,1,1,1,-1,1,1]\n",
+        "data = [[0.23,-0.88,-0.34,0.12,0.13,0.56,-0.5,-0.4,0.12,0.23,0.98,-0.12,-0.05,0.91,0.99,0.78,0.87,-0.03,0.89,0.97],\n",
+        "        [-1,-1,-1,-1,-1,1,-1,-1,-1,-1,1,-1,-1,1,1,1,1,-1,1,1]]\n",
+        "print(data)\n",
+        "labels =  [-1,-1,-1,-1,-1,1,-1,-1,-1,-1,1,-1,-1,1,1,1,1,-1,1,1]\n",
+        "\n",
+        "\n",
+        "#plt.scatter(data, np.zeros(len(data)), color = \"blue\")\n",
+        "#plt.scatter(data[:len(data)//4], np.zeros(len(data)//4), color = \"orange\")\n",
+        "#plt.scatter(data[-len(data)//4:], np.zeros(len(data)//4), color = \"orange\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Cargar los datos"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "data = np.genfromtxt(\"challenge_train.csv\", delimiter=\",\", skip_header=1)\n",
+        "datos = data[:,1:-1]\n",
+        "# labels = data[:,-1:]\n",
+        "print(datos.shape)\n",
+        "# print(labels.shape)\n",
+        "\n",
+        "print(len(datos))\n",
+        "print(len(datos[0]))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 17,
+      "metadata": {
+        "id": "Uf08bQYJx8xo"
+      },
+      "outputs": [],
+      "source": [
+        "# Entrenamiento estandar de una red neuronal cuántica\n",
+        "\n",
+        "def feature_map(x, param, i):\n",
+        "    print(i)\n",
+        "    qml.Hadamard(wires = i)\n",
+        "    qml.RZ(x*param , wires = i)\n",
+        "\n",
+        "def variational_block(params):\n",
+        "    for i in range(len(params)-1):\n",
+        "        for j in range(i+1,len(params)):\n",
+        "            print(i,\"-\",j)\n",
+        "            qml.CNOT(wires = [i,j])\n",
+        "    \n",
+        "\n",
+        "\n",
+        "dev = qml.device(\"default.qubit\", wires = 2)\n",
+        "@qml.qnode(dev)\n",
+        "def circuit(x, params):\n",
+        "    print(len(params)-1,\"x:\",x)\n",
+        "    for dato in x:\n",
+        "        print(dato)\n",
+        "        # feature_map(dato,params[i],i)\n",
+        "    # variational_block(params)\n",
+        "    # return qml.expval(qml.PauliZ(len(params)-1))\n",
+        "    return qml.expval(qml.Hermitian(x, wires = [0,1]))\n",
+        "\n",
+        "\n",
+        "def error(qnode, data, labels, params):\n",
+        "    err = 0\n",
+        "    for i,fila in enumerate(data):\n",
+        "        print(i, \"-\", fila)\n",
+        "        print(params)\n",
+        "        d = qnode(fila, params)\n",
+        "        err -= (labels[i] * d[0])\n",
+        "    return err / len(data[0])\n",
+        "\n",
+        "def accuracy(qnode,data, labels, params):\n",
+        "    n = 0\n",
+        "    for i,fila in enumerate(data):\n",
+        "        if labels[i] == np.sign(qnode(fila, params[i])[0]):\n",
+        "            n += 1\n",
+        "        if labels[i] == np.sign(qnode(fila, params[i])[1]):\n",
+        "            n += 1\n",
+        "    return n / (len(data[0])*2)\n",
+        "\n",
+        "\n",
+        "def train(data, labels, error_function, params, stepsize = 0.1, epochs = 100, plot = True):\n",
+        "    print(len(data))\n",
+        "    dev = qml.device(\"default.qubit\", wires = len(data))\n",
+        "    qnode = qml.QNode(circuit, dev)\n",
+        "    \n",
+        "    opt = qml.AdagradOptimizer(stepsize = stepsize)\n",
+        "\n",
+        "    costs = []\n",
+        "    for epoch in range(epochs):\n",
+        "        params, cost = opt.step_and_cost(lambda params: error_function(qnode,data, labels, params), params)\n",
+        "        costs.append(cost)\n",
+        "\n",
+        "    if plot:\n",
+        "        plt.plot(costs)\n",
+        "\n",
+        "    return params"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 18,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 447
+        },
+        "id": "wpKFxT9WyAbg",
+        "outputId": "44833c73-f69e-4c4c-a396-30199902b536"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "[0.53293653]\n",
+            "2\n",
+            "0 - [0.23, -0.88, -0.34, 0.12, 0.13, 0.56, -0.5, -0.4, 0.12, 0.23, 0.98, -0.12, -0.05, 0.91, 0.99, 0.78, 0.87, -0.03, 0.89, 0.97]\n",
+            "Autograd ArrayBox with value [0.53293653]\n",
+            "0 x: [0.23, -0.88, -0.34, 0.12, 0.13, 0.56, -0.5, -0.4, 0.12, 0.23, 0.98, -0.12, -0.05, 0.91, 0.99, 0.78, 0.87, -0.03, 0.89, 0.97]\n",
+            "0.23\n",
+            "-0.88\n",
+            "-0.34\n",
+            "0.12\n",
+            "0.13\n",
+            "0.56\n",
+            "-0.5\n",
+            "-0.4\n",
+            "0.12\n",
+            "0.23\n",
+            "0.98\n",
+            "-0.12\n",
+            "-0.05\n",
+            "0.91\n",
+            "0.99\n",
+            "0.78\n",
+            "0.87\n",
+            "-0.03\n",
+            "0.89\n",
+            "0.97\n"
+          ]
+        },
+        {
+          "ename": "ValueError",
+          "evalue": "Observable must be a square matrix.",
+          "output_type": "error",
+          "traceback": [
+            "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+            "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
+            "\u001b[1;32mc:\\Users\\usuario\\Documents\\Yessi\\EscuelaCuanticaEspañol\\escuela-de-computacion-cuantica-2023\\challenges\\QuantumQuipu challenge\\variacionales_2_parametros.ipynb Cell 5\u001b[0m line \u001b[0;36m<cell line: 3>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/usuario/Documents/Yessi/EscuelaCuanticaEspa%C3%B1ol/escuela-de-computacion-cuantica-2023/challenges/QuantumQuipu%20challenge/variacionales_2_parametros.ipynb#W4sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m initial_params \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39mrandom\u001b[39m.\u001b[39mrand(\u001b[39mlen\u001b[39m(data)\u001b[39m-\u001b[39m\u001b[39m1\u001b[39m, requires_grad \u001b[39m=\u001b[39m \u001b[39mTrue\u001b[39;00m)\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/usuario/Documents/Yessi/EscuelaCuanticaEspa%C3%B1ol/escuela-de-computacion-cuantica-2023/challenges/QuantumQuipu%20challenge/variacionales_2_parametros.ipynb#W4sZmlsZQ%3D%3D?line=1'>2</a>\u001b[0m \u001b[39mprint\u001b[39m(initial_params)\n\u001b[1;32m----> <a href='vscode-notebook-cell:/c%3A/Users/usuario/Documents/Yessi/EscuelaCuanticaEspa%C3%B1ol/escuela-de-computacion-cuantica-2023/challenges/QuantumQuipu%20challenge/variacionales_2_parametros.ipynb#W4sZmlsZQ%3D%3D?line=2'>3</a>\u001b[0m params \u001b[39m=\u001b[39m train(data, labels, error, initial_params)\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/usuario/Documents/Yessi/EscuelaCuanticaEspa%C3%B1ol/escuela-de-computacion-cuantica-2023/challenges/QuantumQuipu%20challenge/variacionales_2_parametros.ipynb#W4sZmlsZQ%3D%3D?line=3'>4</a>\u001b[0m \u001b[39mprint\u001b[39m(accuracy(data, labels, params))\n",
+            "\u001b[1;32mc:\\Users\\usuario\\Documents\\Yessi\\EscuelaCuanticaEspañol\\escuela-de-computacion-cuantica-2023\\challenges\\QuantumQuipu challenge\\variacionales_2_parametros.ipynb Cell 5\u001b[0m line \u001b[0;36mtrain\u001b[1;34m(data, labels, error_function, params, stepsize, epochs, plot)\u001b[0m\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/usuario/Documents/Yessi/EscuelaCuanticaEspa%C3%B1ol/escuela-de-computacion-cuantica-2023/challenges/QuantumQuipu%20challenge/variacionales_2_parametros.ipynb#W4sZmlsZQ%3D%3D?line=53'>54</a>\u001b[0m costs \u001b[39m=\u001b[39m []\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/usuario/Documents/Yessi/EscuelaCuanticaEspa%C3%B1ol/escuela-de-computacion-cuantica-2023/challenges/QuantumQuipu%20challenge/variacionales_2_parametros.ipynb#W4sZmlsZQ%3D%3D?line=54'>55</a>\u001b[0m \u001b[39mfor\u001b[39;00m epoch \u001b[39min\u001b[39;00m \u001b[39mrange\u001b[39m(epochs):\n\u001b[1;32m---> <a href='vscode-notebook-cell:/c%3A/Users/usuario/Documents/Yessi/EscuelaCuanticaEspa%C3%B1ol/escuela-de-computacion-cuantica-2023/challenges/QuantumQuipu%20challenge/variacionales_2_parametros.ipynb#W4sZmlsZQ%3D%3D?line=55'>56</a>\u001b[0m     params, cost \u001b[39m=\u001b[39m opt\u001b[39m.\u001b[39;49mstep_and_cost(\u001b[39mlambda\u001b[39;49;00m params: error_function(qnode,data, labels, params), params)\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/usuario/Documents/Yessi/EscuelaCuanticaEspa%C3%B1ol/escuela-de-computacion-cuantica-2023/challenges/QuantumQuipu%20challenge/variacionales_2_parametros.ipynb#W4sZmlsZQ%3D%3D?line=56'>57</a>\u001b[0m     costs\u001b[39m.\u001b[39mappend(cost)\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/usuario/Documents/Yessi/EscuelaCuanticaEspa%C3%B1ol/escuela-de-computacion-cuantica-2023/challenges/QuantumQuipu%20challenge/variacionales_2_parametros.ipynb#W4sZmlsZQ%3D%3D?line=58'>59</a>\u001b[0m \u001b[39mif\u001b[39;00m plot:\n",
+            "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python310\\site-packages\\pennylane\\optimize\\gradient_descent.py:59\u001b[0m, in \u001b[0;36mGradientDescentOptimizer.step_and_cost\u001b[1;34m(self, objective_fn, grad_fn, *args, **kwargs)\u001b[0m\n\u001b[0;32m     39\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mstep_and_cost\u001b[39m(\u001b[39mself\u001b[39m, objective_fn, \u001b[39m*\u001b[39margs, grad_fn\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs):\n\u001b[0;32m     40\u001b[0m     \u001b[39m\"\"\"Update trainable arguments with one step of the optimizer and return the corresponding\u001b[39;00m\n\u001b[0;32m     41\u001b[0m \u001b[39m    objective function value prior to the step.\u001b[39;00m\n\u001b[0;32m     42\u001b[0m \n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m     56\u001b[0m \u001b[39m        If single arg is provided, list [array] is replaced by array.\u001b[39;00m\n\u001b[0;32m     57\u001b[0m \u001b[39m    \"\"\"\u001b[39;00m\n\u001b[1;32m---> 59\u001b[0m     g, forward \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mcompute_grad(objective_fn, args, kwargs, grad_fn\u001b[39m=\u001b[39;49mgrad_fn)\n\u001b[0;32m     60\u001b[0m     new_args \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mapply_grad(g, args)\n\u001b[0;32m     62\u001b[0m     \u001b[39mif\u001b[39;00m forward \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n",
+            "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python310\\site-packages\\pennylane\\optimize\\gradient_descent.py:117\u001b[0m, in \u001b[0;36mGradientDescentOptimizer.compute_grad\u001b[1;34m(objective_fn, args, kwargs, grad_fn)\u001b[0m\n\u001b[0;32m     99\u001b[0m \u001b[39mr\u001b[39m\u001b[39m\"\"\"Compute gradient of the objective function at the given point and return it along with\u001b[39;00m\n\u001b[0;32m    100\u001b[0m \u001b[39mthe objective function forward pass (if available).\u001b[39;00m\n\u001b[0;32m    101\u001b[0m \n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    114\u001b[0m \u001b[39m    will not be evaluted and instead ``None`` will be returned.\u001b[39;00m\n\u001b[0;32m    115\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[0;32m    116\u001b[0m g \u001b[39m=\u001b[39m get_gradient(objective_fn) \u001b[39mif\u001b[39;00m grad_fn \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m \u001b[39melse\u001b[39;00m grad_fn\n\u001b[1;32m--> 117\u001b[0m grad \u001b[39m=\u001b[39m g(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n\u001b[0;32m    118\u001b[0m forward \u001b[39m=\u001b[39m \u001b[39mgetattr\u001b[39m(g, \u001b[39m\"\u001b[39m\u001b[39mforward\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39mNone\u001b[39;00m)\n\u001b[0;32m    120\u001b[0m num_trainable_args \u001b[39m=\u001b[39m \u001b[39msum\u001b[39m(\u001b[39mgetattr\u001b[39m(arg, \u001b[39m\"\u001b[39m\u001b[39mrequires_grad\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39mFalse\u001b[39;00m) \u001b[39mfor\u001b[39;00m arg \u001b[39min\u001b[39;00m args)\n",
+            "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python310\\site-packages\\pennylane\\_grad.py:118\u001b[0m, in \u001b[0;36mgrad.__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m    115\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_fun(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n\u001b[0;32m    116\u001b[0m     \u001b[39mreturn\u001b[39;00m ()\n\u001b[1;32m--> 118\u001b[0m grad_value, ans \u001b[39m=\u001b[39m grad_fn(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)  \u001b[39m# pylint: disable=not-callable\u001b[39;00m\n\u001b[0;32m    119\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward \u001b[39m=\u001b[39m ans\n\u001b[0;32m    121\u001b[0m \u001b[39mreturn\u001b[39;00m grad_value\n",
+            "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python310\\site-packages\\autograd\\wrap_util.py:20\u001b[0m, in \u001b[0;36munary_to_nary.<locals>.nary_operator.<locals>.nary_f\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     18\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m     19\u001b[0m     x \u001b[39m=\u001b[39m \u001b[39mtuple\u001b[39m(args[i] \u001b[39mfor\u001b[39;00m i \u001b[39min\u001b[39;00m argnum)\n\u001b[1;32m---> 20\u001b[0m \u001b[39mreturn\u001b[39;00m unary_operator(unary_f, x, \u001b[39m*\u001b[39mnary_op_args, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mnary_op_kwargs)\n",
+            "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python310\\site-packages\\pennylane\\_grad.py:136\u001b[0m, in \u001b[0;36mgrad._grad_with_forward\u001b[1;34m(fun, x)\u001b[0m\n\u001b[0;32m    130\u001b[0m \u001b[39m@staticmethod\u001b[39m\n\u001b[0;32m    131\u001b[0m \u001b[39m@unary_to_nary\u001b[39m\n\u001b[0;32m    132\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m_grad_with_forward\u001b[39m(fun, x):\n\u001b[0;32m    133\u001b[0m     \u001b[39m\"\"\"This function is a replica of ``autograd.grad``, with the only\u001b[39;00m\n\u001b[0;32m    134\u001b[0m \u001b[39m    difference being that it returns both the gradient *and* the forward pass\u001b[39;00m\n\u001b[0;32m    135\u001b[0m \u001b[39m    value.\"\"\"\u001b[39;00m\n\u001b[1;32m--> 136\u001b[0m     vjp, ans \u001b[39m=\u001b[39m _make_vjp(fun, x)\n\u001b[0;32m    138\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m vspace(ans)\u001b[39m.\u001b[39msize \u001b[39m==\u001b[39m \u001b[39m1\u001b[39m:\n\u001b[0;32m    139\u001b[0m         \u001b[39mraise\u001b[39;00m \u001b[39mTypeError\u001b[39;00m(\n\u001b[0;32m    140\u001b[0m             \u001b[39m\"\u001b[39m\u001b[39mGrad only applies to real scalar-output functions. \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m    141\u001b[0m             \u001b[39m\"\u001b[39m\u001b[39mTry jacobian, elementwise_grad or holomorphic_grad.\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m    142\u001b[0m         )\n",
+            "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python310\\site-packages\\autograd\\core.py:10\u001b[0m, in \u001b[0;36mmake_vjp\u001b[1;34m(fun, x)\u001b[0m\n\u001b[0;32m      8\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mmake_vjp\u001b[39m(fun, x):\n\u001b[0;32m      9\u001b[0m     start_node \u001b[39m=\u001b[39m VJPNode\u001b[39m.\u001b[39mnew_root()\n\u001b[1;32m---> 10\u001b[0m     end_value, end_node \u001b[39m=\u001b[39m  trace(start_node, fun, x)\n\u001b[0;32m     11\u001b[0m     \u001b[39mif\u001b[39;00m end_node \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m     12\u001b[0m         \u001b[39mdef\u001b[39;00m \u001b[39mvjp\u001b[39m(g): \u001b[39mreturn\u001b[39;00m vspace(x)\u001b[39m.\u001b[39mzeros()\n",
+            "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python310\\site-packages\\autograd\\tracer.py:10\u001b[0m, in \u001b[0;36mtrace\u001b[1;34m(start_node, fun, x)\u001b[0m\n\u001b[0;32m      8\u001b[0m \u001b[39mwith\u001b[39;00m trace_stack\u001b[39m.\u001b[39mnew_trace() \u001b[39mas\u001b[39;00m t:\n\u001b[0;32m      9\u001b[0m     start_box \u001b[39m=\u001b[39m new_box(x, t, start_node)\n\u001b[1;32m---> 10\u001b[0m     end_box \u001b[39m=\u001b[39m fun(start_box)\n\u001b[0;32m     11\u001b[0m     \u001b[39mif\u001b[39;00m isbox(end_box) \u001b[39mand\u001b[39;00m end_box\u001b[39m.\u001b[39m_trace \u001b[39m==\u001b[39m start_box\u001b[39m.\u001b[39m_trace:\n\u001b[0;32m     12\u001b[0m         \u001b[39mreturn\u001b[39;00m end_box\u001b[39m.\u001b[39m_value, end_box\u001b[39m.\u001b[39m_node\n",
+            "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python310\\site-packages\\autograd\\wrap_util.py:15\u001b[0m, in \u001b[0;36munary_to_nary.<locals>.nary_operator.<locals>.nary_f.<locals>.unary_f\u001b[1;34m(x)\u001b[0m\n\u001b[0;32m     13\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m     14\u001b[0m     subargs \u001b[39m=\u001b[39m subvals(args, \u001b[39mzip\u001b[39m(argnum, x))\n\u001b[1;32m---> 15\u001b[0m \u001b[39mreturn\u001b[39;00m fun(\u001b[39m*\u001b[39msubargs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n",
+            "\u001b[1;32mc:\\Users\\usuario\\Documents\\Yessi\\EscuelaCuanticaEspañol\\escuela-de-computacion-cuantica-2023\\challenges\\QuantumQuipu challenge\\variacionales_2_parametros.ipynb Cell 5\u001b[0m line \u001b[0;36mtrain.<locals>.<lambda>\u001b[1;34m(params)\u001b[0m\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/usuario/Documents/Yessi/EscuelaCuanticaEspa%C3%B1ol/escuela-de-computacion-cuantica-2023/challenges/QuantumQuipu%20challenge/variacionales_2_parametros.ipynb#W4sZmlsZQ%3D%3D?line=53'>54</a>\u001b[0m costs \u001b[39m=\u001b[39m []\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/usuario/Documents/Yessi/EscuelaCuanticaEspa%C3%B1ol/escuela-de-computacion-cuantica-2023/challenges/QuantumQuipu%20challenge/variacionales_2_parametros.ipynb#W4sZmlsZQ%3D%3D?line=54'>55</a>\u001b[0m \u001b[39mfor\u001b[39;00m epoch \u001b[39min\u001b[39;00m \u001b[39mrange\u001b[39m(epochs):\n\u001b[1;32m---> <a href='vscode-notebook-cell:/c%3A/Users/usuario/Documents/Yessi/EscuelaCuanticaEspa%C3%B1ol/escuela-de-computacion-cuantica-2023/challenges/QuantumQuipu%20challenge/variacionales_2_parametros.ipynb#W4sZmlsZQ%3D%3D?line=55'>56</a>\u001b[0m     params, cost \u001b[39m=\u001b[39m opt\u001b[39m.\u001b[39mstep_and_cost(\u001b[39mlambda\u001b[39;00m params: error_function(qnode,data, labels, params), params)\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/usuario/Documents/Yessi/EscuelaCuanticaEspa%C3%B1ol/escuela-de-computacion-cuantica-2023/challenges/QuantumQuipu%20challenge/variacionales_2_parametros.ipynb#W4sZmlsZQ%3D%3D?line=56'>57</a>\u001b[0m     costs\u001b[39m.\u001b[39mappend(cost)\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/usuario/Documents/Yessi/EscuelaCuanticaEspa%C3%B1ol/escuela-de-computacion-cuantica-2023/challenges/QuantumQuipu%20challenge/variacionales_2_parametros.ipynb#W4sZmlsZQ%3D%3D?line=58'>59</a>\u001b[0m \u001b[39mif\u001b[39;00m plot:\n",
+            "\u001b[1;32mc:\\Users\\usuario\\Documents\\Yessi\\EscuelaCuanticaEspañol\\escuela-de-computacion-cuantica-2023\\challenges\\QuantumQuipu challenge\\variacionales_2_parametros.ipynb Cell 5\u001b[0m line \u001b[0;36merror\u001b[1;34m(qnode, data, labels, params)\u001b[0m\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/usuario/Documents/Yessi/EscuelaCuanticaEspa%C3%B1ol/escuela-de-computacion-cuantica-2023/challenges/QuantumQuipu%20challenge/variacionales_2_parametros.ipynb#W4sZmlsZQ%3D%3D?line=30'>31</a>\u001b[0m     \u001b[39mprint\u001b[39m(i, \u001b[39m\"\u001b[39m\u001b[39m-\u001b[39m\u001b[39m\"\u001b[39m, fila)\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/usuario/Documents/Yessi/EscuelaCuanticaEspa%C3%B1ol/escuela-de-computacion-cuantica-2023/challenges/QuantumQuipu%20challenge/variacionales_2_parametros.ipynb#W4sZmlsZQ%3D%3D?line=31'>32</a>\u001b[0m     \u001b[39mprint\u001b[39m(params)\n\u001b[1;32m---> <a href='vscode-notebook-cell:/c%3A/Users/usuario/Documents/Yessi/EscuelaCuanticaEspa%C3%B1ol/escuela-de-computacion-cuantica-2023/challenges/QuantumQuipu%20challenge/variacionales_2_parametros.ipynb#W4sZmlsZQ%3D%3D?line=32'>33</a>\u001b[0m     d \u001b[39m=\u001b[39m circuit(fila, params)\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/usuario/Documents/Yessi/EscuelaCuanticaEspa%C3%B1ol/escuela-de-computacion-cuantica-2023/challenges/QuantumQuipu%20challenge/variacionales_2_parametros.ipynb#W4sZmlsZQ%3D%3D?line=33'>34</a>\u001b[0m     err \u001b[39m-\u001b[39m\u001b[39m=\u001b[39m (labels[i] \u001b[39m*\u001b[39m d[\u001b[39m0\u001b[39m])\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/usuario/Documents/Yessi/EscuelaCuanticaEspa%C3%B1ol/escuela-de-computacion-cuantica-2023/challenges/QuantumQuipu%20challenge/variacionales_2_parametros.ipynb#W4sZmlsZQ%3D%3D?line=34'>35</a>\u001b[0m \u001b[39mreturn\u001b[39;00m err \u001b[39m/\u001b[39m \u001b[39mlen\u001b[39m(data[\u001b[39m0\u001b[39m])\n",
+            "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python310\\site-packages\\pennylane\\qnode.py:970\u001b[0m, in \u001b[0;36mQNode.__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m    967\u001b[0m         kwargs[\u001b[39m\"\u001b[39m\u001b[39mshots\u001b[39m\u001b[39m\"\u001b[39m] \u001b[39m=\u001b[39m _get_device_shots(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_original_device)\n\u001b[0;32m    969\u001b[0m \u001b[39m# construct the tape\u001b[39;00m\n\u001b[1;32m--> 970\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mconstruct(args, kwargs)\n\u001b[0;32m    972\u001b[0m cache \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mexecute_kwargs\u001b[39m.\u001b[39mget(\u001b[39m\"\u001b[39m\u001b[39mcache\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39mFalse\u001b[39;00m)\n\u001b[0;32m    973\u001b[0m using_custom_cache \u001b[39m=\u001b[39m (\n\u001b[0;32m    974\u001b[0m     \u001b[39mhasattr\u001b[39m(cache, \u001b[39m\"\u001b[39m\u001b[39m__getitem__\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[0;32m    975\u001b[0m     \u001b[39mand\u001b[39;00m \u001b[39mhasattr\u001b[39m(cache, \u001b[39m\"\u001b[39m\u001b[39m__setitem__\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[0;32m    976\u001b[0m     \u001b[39mand\u001b[39;00m \u001b[39mhasattr\u001b[39m(cache, \u001b[39m\"\u001b[39m\u001b[39m__delitem__\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[0;32m    977\u001b[0m )\n",
+            "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python310\\site-packages\\pennylane\\qnode.py:856\u001b[0m, in \u001b[0;36mQNode.construct\u001b[1;34m(self, args, kwargs)\u001b[0m\n\u001b[0;32m    853\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39minterface \u001b[39m=\u001b[39m qml\u001b[39m.\u001b[39mmath\u001b[39m.\u001b[39mget_interface(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39mlist\u001b[39m(kwargs\u001b[39m.\u001b[39mvalues()))\n\u001b[0;32m    855\u001b[0m \u001b[39mwith\u001b[39;00m qml\u001b[39m.\u001b[39mqueuing\u001b[39m.\u001b[39mAnnotatedQueue() \u001b[39mas\u001b[39;00m q:\n\u001b[1;32m--> 856\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_qfunc_output \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mfunc(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n\u001b[0;32m    858\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_tape \u001b[39m=\u001b[39m QuantumScript\u001b[39m.\u001b[39mfrom_queue(q, shots)\n\u001b[0;32m    860\u001b[0m params \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mtape\u001b[39m.\u001b[39mget_parameters(trainable_only\u001b[39m=\u001b[39m\u001b[39mFalse\u001b[39;00m)\n",
+            "\u001b[1;32mc:\\Users\\usuario\\Documents\\Yessi\\EscuelaCuanticaEspañol\\escuela-de-computacion-cuantica-2023\\challenges\\QuantumQuipu challenge\\variacionales_2_parametros.ipynb Cell 5\u001b[0m line \u001b[0;36mcircuit\u001b[1;34m(x, params)\u001b[0m\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/usuario/Documents/Yessi/EscuelaCuanticaEspa%C3%B1ol/escuela-de-computacion-cuantica-2023/challenges/QuantumQuipu%20challenge/variacionales_2_parametros.ipynb#W4sZmlsZQ%3D%3D?line=20'>21</a>\u001b[0m     \u001b[39mprint\u001b[39m(dato)\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/usuario/Documents/Yessi/EscuelaCuanticaEspa%C3%B1ol/escuela-de-computacion-cuantica-2023/challenges/QuantumQuipu%20challenge/variacionales_2_parametros.ipynb#W4sZmlsZQ%3D%3D?line=21'>22</a>\u001b[0m     \u001b[39m# feature_map(dato,params[i],i)\u001b[39;00m\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/usuario/Documents/Yessi/EscuelaCuanticaEspa%C3%B1ol/escuela-de-computacion-cuantica-2023/challenges/QuantumQuipu%20challenge/variacionales_2_parametros.ipynb#W4sZmlsZQ%3D%3D?line=22'>23</a>\u001b[0m \u001b[39m# variational_block(params)\u001b[39;00m\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/usuario/Documents/Yessi/EscuelaCuanticaEspa%C3%B1ol/escuela-de-computacion-cuantica-2023/challenges/QuantumQuipu%20challenge/variacionales_2_parametros.ipynb#W4sZmlsZQ%3D%3D?line=23'>24</a>\u001b[0m \u001b[39m# return qml.expval(qml.PauliZ(len(params)-1))\u001b[39;00m\n\u001b[1;32m---> <a href='vscode-notebook-cell:/c%3A/Users/usuario/Documents/Yessi/EscuelaCuanticaEspa%C3%B1ol/escuela-de-computacion-cuantica-2023/challenges/QuantumQuipu%20challenge/variacionales_2_parametros.ipynb#W4sZmlsZQ%3D%3D?line=24'>25</a>\u001b[0m \u001b[39mreturn\u001b[39;00m qml\u001b[39m.\u001b[39mexpval(qml\u001b[39m.\u001b[39;49mHermitian(x, wires \u001b[39m=\u001b[39;49m [\u001b[39m0\u001b[39;49m,\u001b[39m1\u001b[39;49m]))\n",
+            "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python310\\site-packages\\pennylane\\ops\\qubit\\observables.py:80\u001b[0m, in \u001b[0;36mHermitian.__init__\u001b[1;34m(self, A, wires, id)\u001b[0m\n\u001b[0;32m     76\u001b[0m     \u001b[39melse\u001b[39;00m:\n\u001b[0;32m     77\u001b[0m         \u001b[39m# Assumably wires is an int; further validation checks are performed by calling super().__init__\u001b[39;00m\n\u001b[0;32m     78\u001b[0m         expected_mx_shape \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_num_basis_states\n\u001b[1;32m---> 80\u001b[0m     Hermitian\u001b[39m.\u001b[39;49m_validate_input(A, expected_mx_shape)\n\u001b[0;32m     82\u001b[0m \u001b[39msuper\u001b[39m()\u001b[39m.\u001b[39m\u001b[39m__init__\u001b[39m(A, wires\u001b[39m=\u001b[39mwires, \u001b[39mid\u001b[39m\u001b[39m=\u001b[39m\u001b[39mid\u001b[39m)\n",
+            "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python310\\site-packages\\pennylane\\ops\\qubit\\observables.py:88\u001b[0m, in \u001b[0;36mHermitian._validate_input\u001b[1;34m(A, expected_mx_shape)\u001b[0m\n\u001b[0;32m     86\u001b[0m \u001b[39m\"\"\"Validate the input matrix.\"\"\"\u001b[39;00m\n\u001b[0;32m     87\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mlen\u001b[39m(A\u001b[39m.\u001b[39mshape) \u001b[39m!=\u001b[39m \u001b[39m2\u001b[39m \u001b[39mor\u001b[39;00m A\u001b[39m.\u001b[39mshape[\u001b[39m0\u001b[39m] \u001b[39m!=\u001b[39m A\u001b[39m.\u001b[39mshape[\u001b[39m1\u001b[39m]:\n\u001b[1;32m---> 88\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\u001b[39m\"\u001b[39m\u001b[39mObservable must be a square matrix.\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[0;32m     90\u001b[0m \u001b[39mif\u001b[39;00m expected_mx_shape \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m \u001b[39mand\u001b[39;00m A\u001b[39m.\u001b[39mshape[\u001b[39m0\u001b[39m] \u001b[39m!=\u001b[39m expected_mx_shape:\n\u001b[0;32m     91\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\n\u001b[0;32m     92\u001b[0m         \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mExpected input matrix to have shape \u001b[39m\u001b[39m{\u001b[39;00mexpected_mx_shape\u001b[39m}\u001b[39;00m\u001b[39mx\u001b[39m\u001b[39m{\u001b[39;00mexpected_mx_shape\u001b[39m}\u001b[39;00m\u001b[39m, but \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m     93\u001b[0m         \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39ma matrix with shape \u001b[39m\u001b[39m{\u001b[39;00mA\u001b[39m.\u001b[39mshape[\u001b[39m0\u001b[39m]\u001b[39m}\u001b[39;00m\u001b[39mx\u001b[39m\u001b[39m{\u001b[39;00mA\u001b[39m.\u001b[39mshape[\u001b[39m0\u001b[39m]\u001b[39m}\u001b[39;00m\u001b[39m was passed.\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m     94\u001b[0m     )\n",
+            "\u001b[1;31mValueError\u001b[0m: Observable must be a square matrix."
+          ]
+        }
+      ],
+      "source": [
+        "initial_params = np.random.rand(len(data)-1, requires_grad = True)\n",
+        "print(initial_params)\n",
+        "params = train(data, labels, error, initial_params)\n",
+        "print(accuracy(data, labels, params))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "p187sShLyCxQ"
+      },
+      "outputs": [],
+      "source": [
+        "def feature_map(x, feature_param):\n",
+        "    qml.Hadamard(wires = 0)\n",
+        "    qml.RZ(x * feature_param, wires = 0)\n",
+        "\n",
+        "def feature_error(data, labels, feature_param):\n",
+        "\n",
+        "    dev = qml.device(\"default.qubit\", wires = 1)\n",
+        "    @qml.qnode(dev)\n",
+        "    def circuit(x1, x2):\n",
+        "        feature_map(x1, feature_param)\n",
+        "        qml.adjoint(feature_map)(x2, feature_param)\n",
+        "        return qml.probs(wires = 0)\n",
+        "\n",
+        "    err = 0\n",
+        "    for i in range(len(data)):\n",
+        "        for j in range(i, len(data)):\n",
+        "            d = circuit(data[i], data[j])\n",
+        "            err -= labels[i] * labels[j] * d[0][0]\n",
+        "    return err\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "initial_param = np.random.rand(1, requires_grad = True)\n",
+        "param = train(data, labels, feature_error, initial_param)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "print(accuracy(data, labels, params))"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "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": "3.10.4"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/challenges/openqaoa challenge/README.md b/challenges/openqaoa challenge/README.md
new file mode 100644
index 0000000..26cf843
--- /dev/null
+++ b/challenges/openqaoa challenge/README.md	
@@ -0,0 +1,8 @@
+Equipo: Qutips
+
+Integrantes:
+Areli Yesareth Guerrero Estrada
+Janeth De Anda Gil
+Miguel Alejandro Medina Armendariz
+Edgar Omar Mendoza Lopez
+
diff --git a/challenges/openqaoa challenge/challenge-openqaoa.ipynb b/challenges/openqaoa challenge/challenge-openqaoa.ipynb
index 5bfead4..ca96083 100644
--- a/challenges/openqaoa challenge/challenge-openqaoa.ipynb	
+++ b/challenges/openqaoa challenge/challenge-openqaoa.ipynb	
@@ -53,7 +53,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -87,56 +87,195 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [],
    "source": [
     "# inputs\n",
+    "values = [2,6,8,7,3,4,6]\n",
+    "weights = [7,3,3,5,4,7,5]\n",
+    "max_weight = 25\n",
     "\n",
     "\n",
-    "def Problem(values,weights, max_weight):\n",
+    "def Problem(values, weights, max_weight):\n",
     "    \n",
     "    # initialize a model\n",
-    "\n",
+    "    mdl = Model(name=\"Knaspack\")\n",
     "    # indicate the binary variables \n",
-    "\n",
+    "    x = {i: mdl.binary_var(name=f\"x_{i}\") for i in range(len(values))}  \n",
     "    # define the objective function\n",
-    "\n",
+    "    mdl.minimize(mdl.sum(values[i] * x[i] for i in x))    \n",
     "    # add the constraints\n",
-    "    return  #return model, check FromDocplex2IsingModel"
+    "    mdl.add_constraint(mdl.sum(weights[i] * x[i] for i in x) <= max_weight)\n",
+    "    return  FromDocplex2IsingModel(mdl) #return model, check FromDocplex2IsingModel"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 31,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "// This file has been generated by DOcplex\n",
+      "// model name is: Binpacking\n",
+      "// var contrainer section\n",
+      "dvar bool slack_C0[5];\n",
+      "\n",
+      "// single vars section\n",
+      "dvar bool x_0;\n",
+      "dvar bool x_1;\n",
+      "dvar bool x_2;\n",
+      "dvar bool x_3;\n",
+      "dvar bool x_4;\n",
+      "dvar bool x_5;\n",
+      "dvar bool x_6;\n",
+      "dvar bool slack_C0_0;\n",
+      "dvar bool slack_C0_1;\n",
+      "dvar bool slack_C0_2;\n",
+      "dvar bool slack_C0_3;\n",
+      "dvar bool slack_C0_4;\n",
+      "\n",
+      "minimize\n",
+      " - 12948 x_0 - 5544 x_1 - 5542 x_2 - 9243 x_3 - 7397 x_4 - 12946 x_5\n",
+      " - 9244 x_6 - 1850 slack_C0_0 - 3700 slack_C0_1 - 7400 slack_C0_2\n",
+      " - 14800 slack_C0_3 - 18500 slack_C0_4 [ 1813 x_0^2 + 1554 x_0*x_1\n",
+      " + 1554 x_0*x_2 + 2590 x_0*x_3 + 2072 x_0*x_4 + 3626 x_0*x_5 + 2590 x_0*x_6\n",
+      " + 518 x_0*slack_C0_0 + 1036 x_0*slack_C0_1 + 2072 x_0*slack_C0_2\n",
+      " + 4144 x_0*slack_C0_3 + 5180 x_0*slack_C0_4 + 333 x_1^2 + 666 x_1*x_2\n",
+      " + 1110 x_1*x_3 + 888 x_1*x_4 + 1554 x_1*x_5 + 1110 x_1*x_6\n",
+      " + 222 x_1*slack_C0_0 + 444 x_1*slack_C0_1 + 888 x_1*slack_C0_2\n",
+      " + 1776 x_1*slack_C0_3 + 2220 x_1*slack_C0_4 + 333 x_2^2 + 1110 x_2*x_3\n",
+      " + 888 x_2*x_4 + 1554 x_2*x_5 + 1110 x_2*x_6 + 222 x_2*slack_C0_0\n",
+      " + 444 x_2*slack_C0_1 + 888 x_2*slack_C0_2 + 1776 x_2*slack_C0_3\n",
+      " + 2220 x_2*slack_C0_4 + 925 x_3^2 + 1480 x_3*x_4 + 2590 x_3*x_5\n",
+      " + 1850 x_3*x_6 + 370 x_3*slack_C0_0 + 740 x_3*slack_C0_1\n",
+      " + 1480 x_3*slack_C0_2 + 2960 x_3*slack_C0_3 + 3700 x_3*slack_C0_4 + 592 x_4^2\n",
+      " + 2072 x_4*x_5 + 1480 x_4*x_6 + 296 x_4*slack_C0_0 + 592 x_4*slack_C0_1\n",
+      " + 1184 x_4*slack_C0_2 + 2368 x_4*slack_C0_3 + 2960 x_4*slack_C0_4\n",
+      " + 1813 x_5^2 + 2590 x_5*x_6 + 518 x_5*slack_C0_0 + 1036 x_5*slack_C0_1\n",
+      " + 2072 x_5*slack_C0_2 + 4144 x_5*slack_C0_3 + 5180 x_5*slack_C0_4 + 925 x_6^2\n",
+      " + 370 x_6*slack_C0_0 + 740 x_6*slack_C0_1 + 1480 x_6*slack_C0_2\n",
+      " + 2960 x_6*slack_C0_3 + 3700 x_6*slack_C0_4 + 37 slack_C0_0^2\n",
+      " + 148 slack_C0_0*slack_C0_1 + 296 slack_C0_0*slack_C0_2\n",
+      " + 592 slack_C0_0*slack_C0_3 + 740 slack_C0_0*slack_C0_4 + 148 slack_C0_1^2\n",
+      " + 592 slack_C0_1*slack_C0_2 + 1184 slack_C0_1*slack_C0_3\n",
+      " + 1480 slack_C0_1*slack_C0_4 + 592 slack_C0_2^2 + 2368 slack_C0_2*slack_C0_3\n",
+      " + 2960 slack_C0_2*slack_C0_4 + 2368 slack_C0_3^2 + 5920 slack_C0_3*slack_C0_4\n",
+      " + 3700 slack_C0_4^2 ] + 23125;\n",
+      " \n",
+      "subject to {\n",
+      "\n",
+      "}\n"
+     ]
+    }
+   ],
    "source": [
     "problem =  Problem(values, weights, max_weight)\n",
     "\n",
     "# Ising encoding of the QUBO problem for binpacking problem\n",
-    "\n",
+    "qubo = problem.ising_model\n",
     "\n",
     "# Docplex encoding of the QUBO problem for binpacking problem\n",
+    "mdl_qubo_docplex = problem.qubo_docplex\n",
     "\n",
-    "\n",
-    "mdl_qubo_docplex.prettyprint()"
+    "mdl_qubo_docplex.prettyprint()\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<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>solutions_bitstrings</th>\n",
+       "      <th>bitstrings_energies</th>\n",
+       "      <th>probabilities</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0001111</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.012695</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0100111</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>0.005859</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0010111</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>0.007812</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1000111</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>0.000977</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1101011</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>0.004883</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  solutions_bitstrings  bitstrings_energies  probabilities\n",
+       "0              0001111                  0.0       0.012695\n",
+       "1              0100111                  4.0       0.005859\n",
+       "2              0010111                  7.0       0.007812\n",
+       "3              1000111                  7.0       0.000977\n",
+       "4              1101011                 11.0       0.004883"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# Initialize the QAOA object\n",
     "qaoa = QAOA()\n",
     "\n",
+    "qiskit_device = create_device(location='local', name='qiskit.qasm_simulator')\n",
+    "qaoa.set_device(qiskit_device)\n",
+    "\n",
     "# Set the parameters to use the QAOA algorithm\n",
     "# where n_shots=1024 and  seed_simulator=1\n",
-    "\n",
-    "\n",
+    "qaoa.set_backend_properties(n_shots=1024, seed_simulator=1)\n",
     "# p=1, a custom type and range from 0 to pi\n",
+    "qaoa.set_circuit_properties(p=1, param_type='standard', init_type='ramp', mixer_hamiltonian='xy')\n",
     "\n",
     "qaoa.compile(qubo)\n",
     "\n",
@@ -148,13 +287,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(0.0, ['0001111'])\n"
+     ]
+    }
+   ],
    "source": [
     "# To find the correct answer using ground_state_hamiltonian\n",
     "# and the parameter is a cost_hamiltonian\n",
-    "correct_solution = "
+    "correct_solution = ground_state_hamiltonian(qaoa.cost_hamil)\n",
+    "print(correct_solution)"
    ]
   },
   {
@@ -166,9 +314,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "objective: 0.000\n",
+      "status: OPTIMAL_SOLUTION(2)\n",
+      "  x_0=0\n",
+      "  x_1=0\n",
+      "  x_2=0\n",
+      "  slack_C0_0=1\n",
+      "  slack_C0_1=1\n",
+      "  slack_C0_2=1\n",
+      "  slack_C0_3=1\n"
+     ]
+    }
+   ],
    "source": [
     "## docplex solution\n",
     "sol = mdl_qubo_docplex.solve()\n",
@@ -186,69 +350,124 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [],
    "source": [
     "## Implementation\n",
+    "def funcionesQAOA(device,tipo,ham):\n",
+    "    qaoa = QAOA()\n",
+    "    qaoa.set_device(device)\n",
     "\n",
-    "# Initialize the QAOA object and use a device\n",
-    "device = create_device(\"local\", 'qiskit.qasm_simulator')\n",
-    "qaoa.set\n",
-    "\n",
-    "qaoa = QAOA()\n",
-    "\n",
-    "# Set the parameters to work the QAOA algorithm\n",
-    "# play with the parameters values\n",
+    "    # Set the parameters to use the QAOA algorithm\n",
+    "    qaoa.set_backend_properties(n_shots=1024, seed_simulator=1)\n",
+    "    qaoa.set_circuit_properties(p=1, param_type='standard', init_type=tipo, mixer_hamiltonian=ham)\n",
     "\n",
+    "    qaoa.compile(qubo)\n",
     "\n",
-    "# Run the QAOA algorithm\n",
-    "qaoa.optimize()\n",
-    "\n",
-    "pd.DataFrame(qaoa.result.lowest_cost_bitstrings(5))"
+    "    # Run the QAOA algorithm\n",
+    "    qaoa.optimize()\n",
+    "    return qaoa.result\n"
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": 33,
    "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Dispositivo:  qiskit.statevector_simulator  Tipo:  rand  Mixer:  xy\n",
+      "  solutions_bitstrings  bitstrings_energies  probabilities\n",
+      "0         000000011111                  0.0       0.000552\n",
+      "1         100000000011                  2.0       0.000032\n",
+      "2         000010011011                  3.0       0.000177\n",
+      "3         000001000011                  4.0       0.000184\n",
+      "4         100010000101                  5.0       0.000067\n",
+      "Dispositivo:  qiskit.statevector_simulator  Tipo:  rand  Mixer:  x\n",
+      "  solutions_bitstrings  bitstrings_energies  probabilities\n",
+      "0         000000011111                  0.0       0.000767\n",
+      "1         100000000011                  2.0       0.000077\n",
+      "2         000010011011                  3.0       0.000257\n",
+      "3         000001000011                  4.0       0.000006\n",
+      "4         100010000101                  5.0       0.000024\n",
+      "Dispositivo:  qiskit.statevector_simulator  Tipo:  ramp  Mixer:  xy\n",
+      "  solutions_bitstrings  bitstrings_energies  probabilities\n",
+      "0         000000011111                  0.0       0.000006\n",
+      "1         100000000011                  2.0       0.000118\n",
+      "2         000010011011                  3.0       0.000236\n",
+      "3         000001000011                  4.0       0.000346\n",
+      "4         100010000101                  5.0       0.000110\n",
+      "Dispositivo:  qiskit.statevector_simulator  Tipo:  ramp  Mixer:  x\n",
+      "  solutions_bitstrings  bitstrings_energies  probabilities\n",
+      "0         000000011111                  0.0       0.000091\n",
+      "1         100000000011                  2.0       0.000005\n",
+      "2         000010011011                  3.0       0.000016\n",
+      "3         000001000011                  4.0       0.000169\n",
+      "4         100010000101                  5.0       0.002884\n",
+      "Dispositivo:  qiskit.qasm_simulator  Tipo:  rand  Mixer:  xy\n",
+      "  solutions_bitstrings  bitstrings_energies  probabilities\n",
+      "0         000000011111                  0.0       0.000977\n",
+      "1         000001000011                  4.0       0.001953\n",
+      "2         100001010001                  6.0       0.000977\n",
+      "3         100100010110                  9.0       0.000977\n",
+      "4         010001011110                 10.0       0.000977\n",
+      "Dispositivo:  qiskit.qasm_simulator  Tipo:  rand  Mixer:  x\n",
+      "  solutions_bitstrings  bitstrings_energies  probabilities\n",
+      "0         000011001110                  7.0       0.000977\n",
+      "1         001001011110                 12.0       0.002930\n",
+      "2         000111010010                 14.0       0.000977\n",
+      "3         001011011010                 15.0       0.006836\n",
+      "4         001011010001                 15.0       0.006836\n",
+      "Dispositivo:  qiskit.qasm_simulator  Tipo:  ramp  Mixer:  xy\n",
+      "  solutions_bitstrings  bitstrings_energies  probabilities\n",
+      "0         100010000101                  5.0       0.002930\n",
+      "1         010000000111                  6.0       0.000977\n",
+      "2         000011000101                  7.0       0.001953\n",
+      "3         000010101101                  9.0       0.000977\n",
+      "4         100011011100                  9.0       0.000977\n",
+      "Dispositivo:  qiskit.qasm_simulator  Tipo:  ramp  Mixer:  x\n",
+      "  solutions_bitstrings  bitstrings_energies  probabilities\n",
+      "0         000000011111                  0.0       0.001953\n",
+      "1         100000000011                  2.0       0.000977\n",
+      "2         000011001110                  7.0       0.000977\n",
+      "3         101000010101                 10.0       0.001953\n",
+      "4         001001011110                 12.0       0.004883\n"
+     ]
+    }
+   ],
    "source": [
-    "## Part 3: Noise Model\n",
-    "\n",
-    "The optimal combination that you found with the best optimizer, the lowest number of $p$'s and the correct answer, can give the same answer with noise, use the circuit with a noise model and identify if it gives the same answer."
+    "# Initialize the QAOA object and use a device\n",
+    "tipos = ['rand','ramp']\n",
+    "ham = ['xy','x']\n",
+    "dispositivos = ['qiskit.statevector_simulator','qiskit.qasm_simulator']\n",
+    "for k in dispositivos:\n",
+    "    for i in tipos:\n",
+    "        for j in ham:\n",
+    "            device = create_device(\"local\", k)\n",
+    "            results_sv = funcionesQAOA(device,i,j)\n",
+    "            print(\"Dispositivo: \",k, \" Tipo: \",i, \" Mixer: \",j)\n",
+    "            print(pd.DataFrame(results_sv.lowest_cost_bitstrings(5)))\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 17,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(0.0, ['0001111'])\n"
+     ]
+    }
+   ],
    "source": [
-    "# implementation using a noise model using qiskit \n",
-    "\n",
-    "# initialize the QAOA object\n",
-    "q = QAOA()\n",
-    "\n",
-    "## real hardware\n",
-    "from qiskit.providers.fake_provider import FakeVigo\n",
-    "from qiskit.providers.aer.noise import NoiseModel\n",
-    "from qiskit.providers.aer import QasmSimulator\n",
-    "\n",
-    "\n",
-    "device = create_device(\"local\", 'qiskit.qasm_simulator')\n",
-    "# choose the noise model\n",
-    "\n",
-    "# set your device\n",
-    "q.set_device(device)\n",
-    "\n",
-    "# set the parameters to work the QAOA algorithm\n",
-    "\n",
-    "\n",
-    "qaoa.compile(ising_encoding)\n",
-    "\n",
-    "# run the QAOA algorithm\n",
-    "qaoa.optimize()\n",
-    "\n",
-    "pd.DataFrame(qaoa.result.lowest_cost_bitstrings(5))"
+    "correct_solution = ground_state_hamiltonian(qaoa.cost_hamil)\n",
+    "print(correct_solution)"
    ]
   },
   {
@@ -267,9 +486,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "mitiq-qaoa",
+   "display_name": "Python 3",
    "language": "python",
-   "name": "mitiq-qaoa"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -281,7 +500,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.8"
+   "version": "3.10.4"
   }
  },
  "nbformat": 4,
diff --git a/challenges/openqaoa challenge/main.ipynb b/challenges/openqaoa challenge/main.ipynb
new file mode 100644
index 0000000..83a474c
--- /dev/null
+++ b/challenges/openqaoa challenge/main.ipynb	
@@ -0,0 +1,280 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib notebook\n",
+    "\n",
+    "# Import external libraries to present an manipulate the data\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "\n",
+    "# Import docplex model to generate the problem to optimize\n",
+    "from docplex.mp.model import Model\n",
+    "\n",
+    "# Import the libraries needed to employ the QAOA quantum algorithm using OpenQAOA\n",
+    "from openqaoa import QAOA\n",
+    "\n",
+    "# method to covnert a docplex model to a qubo problem\n",
+    "from openqaoa.problems.converters import FromDocplex2IsingModel #check this method and properties\n",
+    "from openqaoa.backends import create_device\n",
+    "\n",
+    "# method to find the correct states for the QAOA object \n",
+    "from openqaoa.utilities import ground_state_hamiltonian"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## El problema de la mochila \n",
+    "\n",
+    "El problema de la mochila es un problema combinatorio que considera un conjunto de objetos que tiene un valor peso/costo, el objetivo consiste en seleccionar un subconjunto de elementos que permita maximizar el peso/costo. Este problema esta restringido a una capacidad máxima específica.\n",
+    "\n",
+    "Para ello se debe cumplir que el peso máximo debe ser mayor al menor de los pesos de los elementos, en el caso trivial la suma de todos los elementos es menor al peso máximo.\n",
+    "\n",
+    "Para la transformación a un problema QUBO se deben considerar las restricciones de la desigualdad. \n",
+    "\n",
+    "La función puede describirse fomo:\n",
+    "\n",
+    "$$\n",
+    "\n",
+    "max \\left(  \\sum_{i=0}^n v_i x_i \\right) \n",
+    "\n",
+    "$$\n",
+    "\n",
+    "La restricción es:\n",
+    "\n",
+    "$$\n",
+    "\n",
+    "\\sum_{i=1}^n w_i x_i \\leq W\n",
+    "$$\n",
+    "\n",
+    "$$\n",
+    "\n",
+    "x_i = \\{0,1\\}\n",
+    "\n",
+    "$$\n",
+    "\n",
+    "### Problema de optimización\n",
+    "\n",
+    "Existe una gran variedad de problemas que pueden resolverde con ese planteamiento, por ejemplo:\n",
+    "-Si se desea maximizar la producción agricola en un invernadero dados la cantidad de sustratos y el costo de los productos a coshechar.\n",
+    "-Se desea maximizar la contratación de profesores dado un presupuesto definido que permita ofrecer la mayor cantidad de áreas de conocimiento.\n",
+    "-Para maximizar el almacenamiento de productos para su translado, la etensión de este caso se proyecta al uso de múltiples mochilas en donde se pueden almacenar, la misma o diferentes cantidad de peso.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[2 5 5 8 4 5 5 2 9 1]\n",
+      "[3 7 4 9 7 1 7 8 1 5]\n",
+      "24\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Datos del problema de Prueba\n",
+    "values = np.random.randint(10, size=10)\n",
+    "weights = np.random.randint(10, size=10)\n",
+    "max_weight = np.random.randint(10,50)\n",
+    "\n",
+    "print(values)\n",
+    "print(weights)\n",
+    "print(max_weight)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Definir el problema, la función objetivo y las restricciones."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def Problem(values, weights, max_weight):\n",
+    "    \n",
+    "    # initialize a model\n",
+    "    mdl = Model(name=\"Knaspack\")\n",
+    "    \n",
+    "    x = {i: mdl.binary_var(name=f\"x_{i}\") for i in range(len(values))}  \n",
+    "    # define the objective function\n",
+    "    mdl.minimize(mdl.sum(values[i] * x[i] for i in x))    \n",
+    "    # add the constraints\n",
+    "    mdl.add_constraint(mdl.sum(weights[i] * x[i] for i in x) <= max_weight)\n",
+    "    return  FromDocplex2IsingModel(mdl)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "// This file has been generated by DOcplex\n",
+      "// model name is: Knaspack\n",
+      "// var contrainer section\n",
+      "dvar bool slack_C0[5];\n",
+      "\n",
+      "// single vars section\n",
+      "dvar bool x_0;\n",
+      "dvar bool x_1;\n",
+      "dvar bool x_2;\n",
+      "dvar bool x_3;\n",
+      "dvar bool x_4;\n",
+      "dvar bool x_5;\n",
+      "dvar bool x_6;\n",
+      "dvar bool x_7;\n",
+      "dvar bool x_8;\n",
+      "dvar bool x_9;\n",
+      "dvar bool slack_C0_0;\n",
+      "dvar bool slack_C0_1;\n",
+      "dvar bool slack_C0_2;\n",
+      "dvar bool slack_C0_3;\n",
+      "dvar bool slack_C0_4;\n",
+      "\n",
+      "minimize\n",
+      " - 6766 x_0 - 15787 x_1 - 9019 x_2 - 20296 x_3 - 15788 x_4 - 2251 x_5\n",
+      " - 15787 x_6 - 18046 x_7 - 2247 x_8 - 11279 x_9 - 2256 slack_C0_0\n",
+      " - 4512 slack_C0_1 - 9024 slack_C0_2 - 18048 slack_C0_3 - 20304 slack_C0_4 [\n",
+      " 423 x_0^2 + 1974 x_0*x_1 + 1128 x_0*x_2 + 2538 x_0*x_3 + 1974 x_0*x_4\n",
+      " + 282 x_0*x_5 + 1974 x_0*x_6 + 2256 x_0*x_7 + 282 x_0*x_8 + 1410 x_0*x_9\n",
+      " + 282 x_0*slack_C0_0 + 564 x_0*slack_C0_1 + 1128 x_0*slack_C0_2\n",
+      " + 2256 x_0*slack_C0_3 + 2538 x_0*slack_C0_4 + 2303 x_1^2 + 2632 x_1*x_2\n",
+      " + 5922 x_1*x_3 + 4606 x_1*x_4 + 658 x_1*x_5 + 4606 x_1*x_6 + 5264 x_1*x_7\n",
+      " + 658 x_1*x_8 + 3290 x_1*x_9 + 658 x_1*slack_C0_0 + 1316 x_1*slack_C0_1\n",
+      " + 2632 x_1*slack_C0_2 + 5264 x_1*slack_C0_3 + 5922 x_1*slack_C0_4 + 752 x_2^2\n",
+      " + 3384 x_2*x_3 + 2632 x_2*x_4 + 376 x_2*x_5 + 2632 x_2*x_6 + 3008 x_2*x_7\n",
+      " + 376 x_2*x_8 + 1880 x_2*x_9 + 376 x_2*slack_C0_0 + 752 x_2*slack_C0_1\n",
+      " + 1504 x_2*slack_C0_2 + 3008 x_2*slack_C0_3 + 3384 x_2*slack_C0_4\n",
+      " + 3807 x_3^2 + 5922 x_3*x_4 + 846 x_3*x_5 + 5922 x_3*x_6 + 6768 x_3*x_7\n",
+      " + 846 x_3*x_8 + 4230 x_3*x_9 + 846 x_3*slack_C0_0 + 1692 x_3*slack_C0_1\n",
+      " + 3384 x_3*slack_C0_2 + 6768 x_3*slack_C0_3 + 7614 x_3*slack_C0_4\n",
+      " + 2303 x_4^2 + 658 x_4*x_5 + 4606 x_4*x_6 + 5264 x_4*x_7 + 658 x_4*x_8\n",
+      " + 3290 x_4*x_9 + 658 x_4*slack_C0_0 + 1316 x_4*slack_C0_1\n",
+      " + 2632 x_4*slack_C0_2 + 5264 x_4*slack_C0_3 + 5922 x_4*slack_C0_4 + 47 x_5^2\n",
+      " + 658 x_5*x_6 + 752 x_5*x_7 + 94 x_5*x_8 + 470 x_5*x_9 + 94 x_5*slack_C0_0\n",
+      " + 188 x_5*slack_C0_1 + 376 x_5*slack_C0_2 + 752 x_5*slack_C0_3\n",
+      " + 846 x_5*slack_C0_4 + 2303 x_6^2 + 5264 x_6*x_7 + 658 x_6*x_8 + 3290 x_6*x_9\n",
+      " + 658 x_6*slack_C0_0 + 1316 x_6*slack_C0_1 + 2632 x_6*slack_C0_2\n",
+      " + 5264 x_6*slack_C0_3 + 5922 x_6*slack_C0_4 + 3008 x_7^2 + 752 x_7*x_8\n",
+      " + 3760 x_7*x_9 + 752 x_7*slack_C0_0 + 1504 x_7*slack_C0_1\n",
+      " + 3008 x_7*slack_C0_2 + 6016 x_7*slack_C0_3 + 6768 x_7*slack_C0_4 + 47 x_8^2\n",
+      " + 470 x_8*x_9 + 94 x_8*slack_C0_0 + 188 x_8*slack_C0_1 + 376 x_8*slack_C0_2\n",
+      " + 752 x_8*slack_C0_3 + 846 x_8*slack_C0_4 + 1175 x_9^2 + 470 x_9*slack_C0_0\n",
+      " + 940 x_9*slack_C0_1 + 1880 x_9*slack_C0_2 + 3760 x_9*slack_C0_3\n",
+      " + 4230 x_9*slack_C0_4 + 47 slack_C0_0^2 + 188 slack_C0_0*slack_C0_1\n",
+      " + 376 slack_C0_0*slack_C0_2 + 752 slack_C0_0*slack_C0_3\n",
+      " + 846 slack_C0_0*slack_C0_4 + 188 slack_C0_1^2 + 752 slack_C0_1*slack_C0_2\n",
+      " + 1504 slack_C0_1*slack_C0_3 + 1692 slack_C0_1*slack_C0_4 + 752 slack_C0_2^2\n",
+      " + 3008 slack_C0_2*slack_C0_3 + 3384 slack_C0_2*slack_C0_4 + 3008 slack_C0_3^2\n",
+      " + 6768 slack_C0_3*slack_C0_4 + 3807 slack_C0_4^2 ] + 27072;\n",
+      " \n",
+      "subject to {\n",
+      "\n",
+      "}\n"
+     ]
+    }
+   ],
+   "source": [
+    "problem =  Problem(values, weights, max_weight)\n",
+    "\n",
+    "# Ising encoding of the QUBO problem for binpacking problem\n",
+    "qubo = problem.ising_model\n",
+    "\n",
+    "# Docplex encoding of the QUBO problem for binpacking problem\n",
+    "mdl_qubo_docplex = problem.qubo_docplex\n",
+    "\n",
+    "mdl_qubo_docplex.prettyprint()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Probar los parámetros para obtener los mejores resultados de la optimización"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def funcionesQAOA(device,tipo,ham):\n",
+    "    qaoa = QAOA()\n",
+    "    qaoa.set_device(device)\n",
+    "\n",
+    "    # Set the parameters to use the QAOA algorithm\n",
+    "    qaoa.set_backend_properties(n_shots=1024, seed_simulator=1)\n",
+    "    qaoa.set_circuit_properties(p=1, param_type='standard', init_type=tipo, mixer_hamiltonian=ham)\n",
+    "\n",
+    "    qaoa.compile(qubo)\n",
+    "\n",
+    "    # Run the QAOA algorithm\n",
+    "    qaoa.optimize()\n",
+    "    \n",
+    "    qaoa.cost_hamil\n",
+    "    return qaoa.result"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Initialize the QAOA object and use a device\n",
+    "tipos = ['rand','ramp']\n",
+    "ham = ['xy','x']\n",
+    "dispositivos = ['qiskit.statevector_simulator','qiskit.qasm_simulator']\n",
+    "for k in dispositivos:\n",
+    "    for i in tipos:\n",
+    "        for j in ham:\n",
+    "            device = create_device(\"local\", k)\n",
+    "            results_sv = funcionesQAOA(device,i,j)\n",
+    "            print(\"Dispositivo: \",k, \" Tipo: \",i, \" Mixer: \",j)\n",
+    "            print(pd.DataFrame(results_sv.lowest_cost_bitstrings(5)))"
+   ]
+  }
+ ],
+ "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": "3.10.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/challenges/xanadu challenge/README.md b/challenges/xanadu challenge/README.md
new file mode 100644
index 0000000..2735993
--- /dev/null
+++ b/challenges/xanadu challenge/README.md	
@@ -0,0 +1,21 @@
+Equipo: Qutips
+
+Integrantes:
+Areli Yesareth Guerrero Estrada
+Janeth De Anda Gil
+Miguel Alejandro Medina Armendariz
+Edgar Omar Mendoza Lopez
+
+
+PennyLane es una plataforma versátil que integra algoritmos clásicos y cuánticos. Permite a los usuarios codificar circuitos cuánticos, y realizar diferentes operaciones. Además cuenta con algoritmos implementados de optimización y calculo de gradientes los cuales se pueden integrar de manera hibrida. También cuenta con la integración de herramientas como NumPy, PyTorch, JAX y TensorFlow, los cuales facilitan las implementaciones de circuitos cuánticos dentro de estas bibliotecas.
+
+PennyLane admite varios optimizadores adaptados a diferentes escenarios de optimización cuántica, incluidos QNGOptimizer, RiemannianGradientOptimizer, RotosolveOptimizer, RotoselectOptimizer, ShotAdaptiveOptimizer y QNSPSAOptimizer cuando se usa NumPy.
+
+El AdagradIptimizer es un algortitmo de optimización con una tasa de aprendizaje el cual ajusta la tasa de aprendizaje para cada parámetro considerando los valores previos, por lo tanto considera la actualización de parámetros individualmente.
+
+En resumen, PennyLane ofrece un conjunto de herramientas integral para desarrollar y optimizar modelos híbridos de aprendizaje automático cuántico-clásico, brindando flexibilidad en la elección de marcos de diferenciación, optimizadores y métodos de cálculo de gradientes.
+
+
+Referencias:
+Koch, D., Patel, S., Wessing, L., & Alsing, P. M. (2020). Fundamentals in quantum algorithms: A tutorial series using Qiskit continued. arXiv preprint arXiv:2008.10647.
+Documentacion: https://docs.pennylane.ai/en/stable/code/api/pennylane.AdagradOptimizer.html
\ No newline at end of file
diff --git a/challenges/xanadu challenge/challenge_template.py b/challenges/xanadu challenge/challenge_template.py
index 7018c54..a8ad165 100644
--- a/challenges/xanadu challenge/challenge_template.py	
+++ b/challenges/xanadu challenge/challenge_template.py	
@@ -2,6 +2,7 @@
 
 import pennylane as qml
 from pennylane import numpy as np
+import random
 
 WIRES = 2
 LAYERS = 5
@@ -25,6 +26,7 @@ def variational_circuit(params,hamiltonian):
         (float): The expectation value of the Hamiltonian
     """
     parameters = params.reshape((LAYERS, WIRES, 3))
+    # print(parameters)
     qml.templates.StronglyEntanglingLayers(parameters, wires=range(WIRES))
     return qml.expval(qml.Hermitian(hamiltonian, wires = [0,1]))
 
@@ -52,11 +54,58 @@ def optimize_circuit(hamiltonian):
     
     ### Solution Template
 
-    dev = # Initialize the device.
-
-    circuit = # Instantiate the QNode from variational_circuit.
+    dev = qml.device("default.qubit",wires=WIRES)# Initialize the device.
+     
+    circuit = variational_circuit # Instantiate the QNode from variational_circuit.
 
     # Write your code to minimize the circuit
+    qnode = qml.QNode(circuit, dev)
+    
+    
+    
+    #########################################################
+    # AdagradOptimizer
+    # Se selecciona por dar el mejor resultado con menos pasos en menos tiempo
+    # Para mejorar el resultado se puede cambiar el valor de 'stepsize' al reducirlo
+    # se limita la busqueda, por lo cual requeriria incrementar el número de pasos
+    #########################################################
+    optGDO = qml.AdagradOptimizer(stepsize=0.1, eps=1e-08)  
+    steps = 200
+
+    #Se genera un arrelgo de parametros iniciales aleatorios
+    init_paramsGDO = [[random.random() for _ in range(30)]]
+    #Se declaran los arreglos para almacenar la información del valor optimizado y los parámetros
+    gdo_cost = []
+    gdo_params = []
+    for _ in range(steps):
+        paramsGDO = np.array(init_paramsGDO[0], requires_grad=True)
+        minimoGDO = qnode(paramsGDO, hamiltonian)
+        gdo_cost.append(minimoGDO)
+        gdo_params.append(paramsGDO)
+        init_paramsGDO = optGDO.step(qnode, paramsGDO, hamiltonian)
+    
+    # Seselecciona el minimo y los parámetros correspondientes a este valor
+    minimo = min(gdo_cost)
+    indice = gdo_cost.index(minimo)
+    parametros = gdo_params[indice]
+    print("Resultado: ",minimo,", parámetros: ",parametros)
+    
+    return minimo# Return the value of the minimized QNode
+
+
+# Valores de prueba
+H1 = [0.863327072347624,0.0167108057202516,0.07991447085492759,0.0854049026262154,0.0167108057202516,0.8237963773906136,-0.07695947154193797,0.03131548733285282,0.07991447085492759,-0.07695947154193795,0.8355417021014687,-0.11345916130631205,0.08540490262621539,0.03131548733285283,-0.11345916130631205,0.758156886827099]
+R1 = 0.61745341
+H2 = [0.32158897156285354,-0.20689268438270836,0.12366748295758379,-0.11737425017261123,-0.20689268438270836,0.7747346055276305,-0.05159966365446514,0.08215539696259792,0.12366748295758379,-0.05159966365446514,0.5769050487087416,0.3853362904758938,-0.11737425017261123,0.08215539696259792,0.3853362904758938,0.3986256655167206]
+R2 = 0.00246488
+
+
+# Resultado empleando el primer valor de prueba
+minimo = optimize_circuit(H1)
+print("Error: ", abs((minimo-R1)))
+
 
-    return # Return the value of the minimized QNode
+# Resultado empleando el segundo valor de prueba
+minimo = optimize_circuit(H2)
+print("Error: ", abs((minimo-R2)))
 
diff --git a/hackathon/README.md b/hackathon/README.md
new file mode 100644
index 0000000..fd96440
--- /dev/null
+++ b/hackathon/README.md
@@ -0,0 +1,54 @@
+Equipo: Qutips
+
+Integrantes:
+Areli Yesareth Guerrero Estrada
+Janeth De Anda Gil
+Miguel Alejandro Medina Armendariz
+Edgar Omar Mendoza Lopez
+
+En el archivo main.ipynb se encuentra la propuesta desarrollada
+
+
+Las redes neuronales hibridas funcionan combinando capas de redes neuronales clásicas con circuitos cuánticos. Las redes neuronales híbridas nos permiten aprovechar las fortalezas de la computación cuántica y clásica. Al integrar elementos cuánticos en redes neuronales, podemos aprovechar la ventaja cuántica para tareas específicas. Esta característica es crucial para abordar desafíos complejos del mundo real.
+
+Aprendizaje automático: Las redes neuronales híbridas pueden mejorar los modelos clásicos de aprendizaje automático, haciéndolos más capaces de manejar tareas complejas y grandes conjuntos de datos.
+
+Qiskit cuenta con una biblioteca que permite integrar los circuitos cuánticos dentro de las capas de las redes neuronales. 
+
+Para poder implementar se debe tomar en cuenta:
+
+1. Importar las bibliotecas necesarias:
+- Importar Qiskit para creación y ejecución de circuitos cuánticos.
+- Importar bibliotecas de PyTorch para operaciones clásicas de redes neuronales.
+
+2. Definir el circuito cuántico:
+- Crear un circuito cuántico en Qiskit.
+- Definir lo elementos a parametrizar que desean optimizar.
+
+3. Crear la red neuronal clásica:
+- Utilizando una librería PyTorch, diseñar la parte clásica de la red neuronal.
+- Define la arquitectura de la red neuronal, incluidas capas, funciones de activación y objetivos
+de optimización.
+
+4. Combinar redes clásicas y cuánticas:
+- Dependiendo del modelo específico, se requiere definir la capa en la que se introduce el circuito cuántico.
+
+5. Establecer una función de costo:
+- Es necesario definir una función de costo que cuantifique el error entre las predicciones de la
+red y los datos reales.
+- Esta función de costos se utiliza para guiar el proceso de optimización.
+
+6. Entrenar la red híbrida:
+- Optimizar los parámetros en el circuito cuántico (y potencialmente en la red clásica) utilizando
+técnicas de optimización.
+
+7. Evaluar y utilizar el modelo:
+- Después del entrenamiento, puede evaluar el rendimiento del modelo híbrido en datos de
+prueba.
+- Utilice el modelo para diversas tareas de aprendizaje automático, como clasificación,
+regresión u optimización.
+
+Referencias:
+https://qiskit.org/ecosystem/machine-learning/index.html
+Rieffel, E. G., & Polak, W. H. (2011). Quantum computing: A gentle introduction. MIT Press.
+Koch, D., Patel, S., Wessing, L. y Alsing, P. Fundamentals In Quantum Algorithms: A tutorial seriesl using Qiskit continued
\ No newline at end of file
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diff --git a/hackathon/main.ipynb b/hackathon/main.ipynb
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+++ b/hackathon/main.ipynb
@@ -0,0 +1,686 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "id": "N8V4_EIW8ZMM",
+      "metadata": {
+        "id": "N8V4_EIW8ZMM"
+      },
+      "source": [
+        "**Clasificación de pacientes con covid, usando imágenes de radiografía y algoritmos cuánticos híbridos**\n",
+        "\n",
+        "\n",
+        "\n",
+        "Para la clasificación de imágenes se propone crear una red neuronal híbrida cuántica-clásica que utiliza TorchConnector. Que consta de una primera parte con redes neuronales clasicas con diferentes capas, junto con una etapa del algoritmo cuántico QNN.\n",
+        "\n",
+        "Más adelante se mostraran los diferentes experimentos que se hicieron con diferente número de capas, diferente número de kernels y diferente tamaño de las imágenes."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "i_oqiCWV-7T6",
+      "metadata": {
+        "id": "i_oqiCWV-7T6"
+      },
+      "source": [
+        "Se instalan los paquetes necesarios.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 9,
+      "id": "ibROZQMq2j5B",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "ibROZQMq2j5B",
+        "outputId": "9a8f1602-5d08-4918-a991-c43737ff67e4"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Requirement already satisfied: qiskit in /usr/local/lib/python3.10/dist-packages (0.45.0)\n",
+            "Requirement already satisfied: qiskit-terra==0.45.0 in /usr/local/lib/python3.10/dist-packages (from qiskit) (0.45.0)\n",
+            "Requirement already satisfied: rustworkx>=0.13.0 in /usr/local/lib/python3.10/dist-packages (from qiskit-terra==0.45.0->qiskit) (0.13.2)\n",
+            "Requirement already satisfied: numpy<2,>=1.17 in /usr/local/lib/python3.10/dist-packages (from qiskit-terra==0.45.0->qiskit) (1.23.5)\n",
+            "Requirement already satisfied: ply>=3.10 in /usr/local/lib/python3.10/dist-packages (from qiskit-terra==0.45.0->qiskit) (3.11)\n",
+            "Requirement already satisfied: psutil>=5 in /usr/local/lib/python3.10/dist-packages (from qiskit-terra==0.45.0->qiskit) (5.9.5)\n",
+            "Requirement already satisfied: scipy>=1.5 in /usr/local/lib/python3.10/dist-packages (from qiskit-terra==0.45.0->qiskit) (1.11.3)\n",
+            "Requirement already satisfied: sympy>=1.3 in /usr/local/lib/python3.10/dist-packages (from qiskit-terra==0.45.0->qiskit) (1.12)\n",
+            "Requirement already satisfied: dill>=0.3 in /usr/local/lib/python3.10/dist-packages (from qiskit-terra==0.45.0->qiskit) (0.3.7)\n",
+            "Requirement already satisfied: python-dateutil>=2.8.0 in /usr/local/lib/python3.10/dist-packages (from qiskit-terra==0.45.0->qiskit) (2.8.2)\n",
+            "Requirement already satisfied: stevedore>=3.0.0 in /usr/local/lib/python3.10/dist-packages (from qiskit-terra==0.45.0->qiskit) (5.1.0)\n",
+            "Requirement already satisfied: symengine<0.10,>=0.9 in /usr/local/lib/python3.10/dist-packages (from qiskit-terra==0.45.0->qiskit) (0.9.2)\n",
+            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.10/dist-packages (from qiskit-terra==0.45.0->qiskit) (4.5.0)\n",
+            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.0->qiskit-terra==0.45.0->qiskit) (1.16.0)\n",
+            "Requirement already satisfied: pbr!=2.1.0,>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from stevedore>=3.0.0->qiskit-terra==0.45.0->qiskit) (6.0.0)\n",
+            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy>=1.3->qiskit-terra==0.45.0->qiskit) (1.3.0)\n"
+          ]
+        }
+      ],
+      "source": [
+        "pip install qiskit"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 10,
+      "id": "nqCfgacn27NR",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "nqCfgacn27NR",
+        "outputId": "fee19e6b-7849-4bc8-9df7-15b3f5208232"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Requirement already satisfied: qiskit_machine_learning in /usr/local/lib/python3.10/dist-packages (0.6.1)\n",
+            "Requirement already satisfied: qiskit-terra>=0.22.2 in /usr/local/lib/python3.10/dist-packages (from qiskit_machine_learning) (0.45.0)\n",
+            "Requirement already satisfied: scipy>=1.4 in /usr/local/lib/python3.10/dist-packages (from qiskit_machine_learning) (1.11.3)\n",
+            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from qiskit_machine_learning) (1.23.5)\n",
+            "Requirement already satisfied: psutil>=5 in /usr/local/lib/python3.10/dist-packages (from qiskit_machine_learning) (5.9.5)\n",
+            "Requirement already satisfied: scikit-learn>=0.20.0 in /usr/local/lib/python3.10/dist-packages (from qiskit_machine_learning) (1.2.2)\n",
+            "Requirement already satisfied: fastdtw in /usr/local/lib/python3.10/dist-packages (from qiskit_machine_learning) (0.3.4)\n",
+            "Requirement already satisfied: setuptools>=40.1.0 in /usr/local/lib/python3.10/dist-packages (from qiskit_machine_learning) (67.7.2)\n",
+            "Requirement already satisfied: dill>=0.3.4 in /usr/local/lib/python3.10/dist-packages (from qiskit_machine_learning) (0.3.7)\n",
+            "Requirement already satisfied: rustworkx>=0.13.0 in /usr/local/lib/python3.10/dist-packages (from qiskit-terra>=0.22.2->qiskit_machine_learning) (0.13.2)\n",
+            "Requirement already satisfied: ply>=3.10 in /usr/local/lib/python3.10/dist-packages (from qiskit-terra>=0.22.2->qiskit_machine_learning) (3.11)\n",
+            "Requirement already satisfied: sympy>=1.3 in /usr/local/lib/python3.10/dist-packages (from qiskit-terra>=0.22.2->qiskit_machine_learning) (1.12)\n",
+            "Requirement already satisfied: python-dateutil>=2.8.0 in /usr/local/lib/python3.10/dist-packages (from qiskit-terra>=0.22.2->qiskit_machine_learning) (2.8.2)\n",
+            "Requirement already satisfied: stevedore>=3.0.0 in /usr/local/lib/python3.10/dist-packages (from qiskit-terra>=0.22.2->qiskit_machine_learning) (5.1.0)\n",
+            "Requirement already satisfied: symengine<0.10,>=0.9 in /usr/local/lib/python3.10/dist-packages (from qiskit-terra>=0.22.2->qiskit_machine_learning) (0.9.2)\n",
+            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.10/dist-packages (from qiskit-terra>=0.22.2->qiskit_machine_learning) (4.5.0)\n",
+            "Requirement already satisfied: joblib>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from scikit-learn>=0.20.0->qiskit_machine_learning) (1.3.2)\n",
+            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn>=0.20.0->qiskit_machine_learning) (3.2.0)\n",
+            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.0->qiskit-terra>=0.22.2->qiskit_machine_learning) (1.16.0)\n",
+            "Requirement already satisfied: pbr!=2.1.0,>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from stevedore>=3.0.0->qiskit-terra>=0.22.2->qiskit_machine_learning) (6.0.0)\n",
+            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy>=1.3->qiskit-terra>=0.22.2->qiskit_machine_learning) (1.3.0)\n"
+          ]
+        }
+      ],
+      "source": [
+        "pip install qiskit_machine_learning"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "eTewLQo6_TES",
+      "metadata": {
+        "id": "eTewLQo6_TES"
+      },
+      "source": [
+        "Se importan las librerias necesarias"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 11,
+      "id": "3AsnwtSP2iKX",
+      "metadata": {
+        "id": "3AsnwtSP2iKX"
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np\n",
+        "import cv2\n",
+        "import matplotlib.pyplot as plt\n",
+        "import torch\n",
+        "from torch import cat, no_grad, manual_seed\n",
+        "from torch.utils.data import DataLoader\n",
+        "from torchvision import datasets, transforms\n",
+        "import torch.optim as optim\n",
+        "from torch.nn import (\n",
+        "    Module,\n",
+        "    Conv2d,\n",
+        "    Linear,\n",
+        "    Dropout2d,\n",
+        "    NLLLoss,\n",
+        "    MaxPool2d,\n",
+        "    Flatten,\n",
+        "    Sequential,\n",
+        "    ReLU,\n",
+        ")\n",
+        "import torch.nn.functional as F\n",
+        "\n",
+        "from torch import Tensor\n",
+        "from torchvision.io import read_image, image\n",
+        "import os\n",
+        "from torchvision.utils import make_grid\n",
+        "from pathlib import Path\n",
+        "from torchvision import transforms\n",
+        "from PIL import Image as imagePIL\n",
+        "from torch.nn import Linear, CrossEntropyLoss, MSELoss\n",
+        "from torch.optim import LBFGS\n",
+        "\n",
+        "from qiskit import QuantumCircuit\n",
+        "from qiskit.utils import algorithm_globals\n",
+        "from qiskit.circuit import Parameter\n",
+        "from qiskit.circuit.library import RealAmplitudes, ZZFeatureMap\n",
+        "from qiskit_machine_learning.neural_networks import SamplerQNN, EstimatorQNN\n",
+        "from qiskit_machine_learning.connectors import TorchConnector\n",
+        "\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "Tx7dSF_x7KlZ",
+      "metadata": {
+        "id": "Tx7dSF_x7KlZ"
+      },
+      "source": [
+        "**Definiendo dataset de imágenes para entrenamiento y test.**\n",
+        "\n",
+        "Se creo un dataset con con radiografías de tórax de personas con covid y personas sanas.de 251 kb.\n",
+        "El dataset esta dividido de la siguiente manera para entrenamiento:\n",
+        "- 78 imágenes de radiografías de tórax de pacientes con COVID.\n",
+        "- 498 imágenes de radiografías de tórax de pacientes sanos.\n",
+        "\n",
+        "\n",
+        "Para test:\n",
+        "- 12 imágenes de radiografías de tórax de pacientes con COVID.\n",
+        "- 69 imágenes de radiografías de tórax de pacientes sanos.\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 12,
+      "id": "tbeknWXf2iKb",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "tbeknWXf2iKb",
+        "outputId": "250c1944-786b-4598-94e3-743b0bc5d28c"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "rm: cannot remove 'bancos/train/.ipynb_checkpoints': No such file or directory\n",
+            "rm: cannot remove 'bancos/test/.ipynb_checkpoints': No such file or directory\n",
+            "<torch.utils.data.dataloader.DataLoader object at 0x7d28b52d8a30>\n",
+            "<torch.utils.data.dataloader.DataLoader object at 0x7d28b52db880>\n"
+          ]
+        }
+      ],
+      "source": [
+        "\n",
+        "batch_size = 1\n",
+        "transformar = transforms.Compose([\n",
+        "    transforms.Resize((28,28)),\n",
+        "    transforms.Grayscale(num_output_channels=1),\n",
+        "    transforms.ToTensor()\n",
+        "])\n",
+        "!rm -R bancos/train/.ipynb_checkpoints #necesario si corres tu programa en colab\n",
+        "\n",
+        "train = datasets.ImageFolder('bancos/train/',transform=transformar)\n",
+        "\n",
+        "!rm -R bancos/test/.ipynb_checkpoints #necesario si corres tu programa en colab\n",
+        "test = datasets.ImageFolder('bancos/test/',transform=transformar)\n",
+        "\n",
+        "\n",
+        "train_data = DataLoader(train, batch_size=1, shuffle=True)\n",
+        "test_data = DataLoader(test, batch_size=1, shuffle=True)\n",
+        "print(train_data)\n",
+        "print(test_data)\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "URbsOlxuAnW_",
+      "metadata": {
+        "id": "URbsOlxuAnW_"
+      },
+      "source": [
+        "Si realizamos una visualización rápida, podemos ver que el conjunto de datos consta de las imagenes del torax de pacientes sanos y enfermos"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 13,
+      "id": "vG_t54w92iKc",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 290
+        },
+        "id": "vG_t54w92iKc",
+        "outputId": "33218007-d452-4443-8844-da1272c7d59a"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 1000x300 with 2 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "n_samples_show = 2\n",
+        "\n",
+        "fig, axes = plt.subplots(nrows=1, ncols=n_samples_show, figsize=(10, 3))\n",
+        "\n",
+        "for ind, (i,c) in enumerate(train_data):\n",
+        "    axes[ind].imshow(i[0,0], cmap=\"gray\")\n",
+        "    axes[ind].set_xticks([])\n",
+        "    axes[ind].set_yticks([])\n",
+        "    axes[ind].set_title(\"Labeled: {}\".format(i.shape))\n",
+        "    n_samples_show -= 1\n",
+        "    if n_samples_show == 0:\n",
+        "        break\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "YCDeFqRpBT67",
+      "metadata": {
+        "id": "YCDeFqRpBT67"
+      },
+      "source": [
+        "**Modelo hibrido**\n",
+        "\n",
+        "Primero se define una capa de de red neuronal cuántica, en este caso, un EstimatorQNN. Esta se puede incrustar inicializando un TorchConnector(qnn).\n",
+        "\n",
+        "Para tener una retropropagación de gradiente adecuada en modelos híbridos, se establecera el parámetro inicial input_gradients en TRUE durante la inicialización de qnn."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 14,
+      "id": "spORW8XD2iKd",
+      "metadata": {
+        "id": "spORW8XD2iKd"
+      },
+      "outputs": [],
+      "source": [
+        "def create_qnn():\n",
+        "    feature_map = ZZFeatureMap(2)\n",
+        "    ansatz = RealAmplitudes(2, reps=1)\n",
+        "    qc = QuantumCircuit(2)\n",
+        "    qc.compose(feature_map, inplace=True)\n",
+        "    qc.compose(ansatz, inplace=True)\n",
+        "\n",
+        "    # REMEMBER TO SET input_gradients=True FOR ENABLING HYBRID GRADIENT BACKPROP\n",
+        "    qnn = EstimatorQNN(\n",
+        "        circuit=qc,\n",
+        "        input_params=feature_map.parameters,\n",
+        "        weight_params=ansatz.parameters,\n",
+        "        input_gradients=True,\n",
+        "    )\n",
+        "    return qnn\n",
+        "\n",
+        "\n",
+        "qnn4 = create_qnn()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "OnVbYOSoDPpk",
+      "metadata": {
+        "id": "OnVbYOSoDPpk"
+      },
+      "source": [
+        "En este punto se pueden crear diferentes combinaciones de capas y kernels. Mas adelante se mostrara una tabla con las combinaciones correspondientes.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 19,
+      "id": "L9bxtfhq2iKd",
+      "metadata": {
+        "id": "L9bxtfhq2iKd"
+      },
+      "outputs": [],
+      "source": [
+        "class Net(Module):\n",
+        "    def __init__(self, qnn):\n",
+        "        super().__init__()\n",
+        "        self.conv1 = Conv2d(1, 2, kernel_size=3)\n",
+        "        self.conv2 = Conv2d(2, 16, kernel_size=3)\n",
+        "        self.conv3 = Conv2d(16, 64, kernel_size=3)\n",
+        "        self.dropout = Dropout2d()\n",
+        "        self.fc1 = Linear(64, 128)\n",
+        "        self.fc2 = Linear(128, 64)  # 2-dimensional input to QNN\n",
+        "        self.fc3 = Linear(64, 2)  # 2-dimensional input to QNN\n",
+        "        self.qnn = TorchConnector(qnn)  # Apply torch connector, weights chosen\n",
+        "        # uniformly at random from interval [-1,1].\n",
+        "        self.fc4 = Linear(1, 1)  # 1-dimensional output from QNN\n",
+        "\n",
+        "    def forward(self, x):\n",
+        "        x = F.relu(self.conv1(x))\n",
+        "        x = F.max_pool2d(x, 2)\n",
+        "        x = F.relu(self.conv2(x))\n",
+        "        x = F.max_pool2d(x, 2)\n",
+        "        x = F.relu(self.conv3(x))\n",
+        "        x = F.max_pool2d(x, 2)\n",
+        "        x = self.dropout(x)\n",
+        "        x = x.view(x.shape[0], -1)\n",
+        "        x = F.relu(self.fc1(x))\n",
+        "        x = self.fc2(x)\n",
+        "        x = self.fc3(x)\n",
+        "        x = self.qnn(x)  # apply QNN\n",
+        "        x = self.fc4(x)\n",
+        "        return cat((x, 1 - x), -1)\n",
+        "\n",
+        "\n",
+        "model4 = Net(qnn4)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "CvelV5ZkEU2w",
+      "metadata": {
+        "id": "CvelV5ZkEU2w"
+      },
+      "source": [
+        "**Manda llamar el modelo para entrenar**\n",
+        "\n",
+        "El entrenamiento varia dependiendo de el número de epocas o iteraciones que se coloquen para el mismo. Para conocer el error que se va teniendo en la iteración con el fin de mejorar el algoritmo, se ocupa la función NLLLOSS, la cual obtiene la pérdida de probabilidad logarítmica negativa. Esto es útil entrenar un problema de clasificación donde el dataset esta desbalanceado."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 20,
+      "id": "_nTc2vyZ2iKe",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "_nTc2vyZ2iKe",
+        "outputId": "1a9d70f6-26a6-44b6-e886-ba3ba68cabf4"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Training [5%]\tLoss: -1.7011\n",
+            "Training [10%]\tLoss: -2.4804\n",
+            "Training [15%]\tLoss: -3.1281\n",
+            "Training [20%]\tLoss: -3.7477\n",
+            "Training [25%]\tLoss: -4.3693\n",
+            "Training [30%]\tLoss: -4.9791\n",
+            "Training [35%]\tLoss: -5.5980\n",
+            "Training [40%]\tLoss: -6.2114\n",
+            "Training [45%]\tLoss: -6.8245\n",
+            "Training [50%]\tLoss: -7.4322\n",
+            "Training [55%]\tLoss: -8.0498\n",
+            "Training [60%]\tLoss: -8.6626\n",
+            "Training [65%]\tLoss: -9.2751\n",
+            "Training [70%]\tLoss: -9.8824\n",
+            "Training [75%]\tLoss: -10.5001\n",
+            "Training [80%]\tLoss: -11.1126\n",
+            "Training [85%]\tLoss: -11.7215\n",
+            "Training [90%]\tLoss: -12.3376\n",
+            "Training [95%]\tLoss: -12.9502\n",
+            "Training [100%]\tLoss: -13.5605\n"
+          ]
+        }
+      ],
+      "source": [
+        "optimizer = optim.Adam(model4.parameters(), lr=0.001)\n",
+        "loss_func = NLLLoss()\n",
+        "\n",
+        "# Start training\n",
+        "epochs = 20  # Set number of epochs\n",
+        "loss_list = []  # Store loss history\n",
+        "model4.train()  # Set model to training mode\n",
+        "\n",
+        "for epoch in range(epochs):\n",
+        "    total_loss = []\n",
+        "    for batch_idx, (data,label) in enumerate(train_data):\n",
+        "        optimizer.zero_grad(set_to_none=True)  # Initialize gradient\n",
+        "        output = model4(data)  # Forward pass\n",
+        "        loss = loss_func(output, label)  # Calculate loss\n",
+        "        loss.backward()  # Backward pass\n",
+        "        optimizer.step()  # Optimize weights\n",
+        "        total_loss.append(loss.item())  # Store loss\n",
+        "    loss_list.append(sum(total_loss) / len(total_loss))\n",
+        "    print(\"Training [{:.0f}%]\\tLoss: {:.4f}\".format(100.0 * (epoch + 1) / epochs, loss_list[-1]))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 21,
+      "id": "_fFpK5cv2iKe",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 472
+        },
+        "id": "_fFpK5cv2iKe",
+        "outputId": "1f8b0612-5843-43eb-cccb-ba59f180ff6c"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "plt.plot(loss_list)\n",
+        "plt.title(\"Hybrid NN Training Convergence\")\n",
+        "plt.xlabel(\"Training Iterations\")\n",
+        "plt.ylabel(\"Neg. Log Likelihood Loss\")\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "W9zV_hajGSM2",
+      "metadata": {
+        "id": "W9zV_hajGSM2"
+      },
+      "source": [
+        "Se guarda el modelo entrenado, para reutilizarlo más adelante y realizar inferencias."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 23,
+      "id": "aZjv8jaZ2iKf",
+      "metadata": {
+        "id": "aZjv8jaZ2iKf"
+      },
+      "outputs": [],
+      "source": [
+        "torch.save(model4.state_dict(), \"model4.pt\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "HV-2qD1EHJru",
+      "metadata": {
+        "id": "HV-2qD1EHJru"
+      },
+      "source": [
+        "**Evaluación**\n",
+        "\n",
+        "\n",
+        "Se recrea el modelo y se carga el estado del archivo previamente guardado. Se crea una capa QNN usando otro simulador"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 24,
+      "id": "MYI6hX0k2iKf",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "MYI6hX0k2iKf",
+        "outputId": "327e9661-5578-4bf9-95a4-d12b06c11703"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "<All keys matched successfully>"
+            ]
+          },
+          "execution_count": 24,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "qnn5 = create_qnn()\n",
+        "model5 = Net(qnn5)\n",
+        "model5.load_state_dict(torch.load(\"model4.pt\"))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 25,
+      "id": "4JbIxKxV2iKg",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "4JbIxKxV2iKg",
+        "outputId": "7ec03178-dfe3-4059-ccb1-9aaeb75c5a72"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Performance on test data:\n",
+            "\tLoss: -13.5411\n",
+            "\tAccuracy: 85.2%\n"
+          ]
+        }
+      ],
+      "source": [
+        "model5.eval()  # set model to evaluation mode\n",
+        "with no_grad():\n",
+        "\n",
+        "    correct = 0\n",
+        "    for batch_idx, (data, target) in enumerate(test_data):\n",
+        "        output = model5(data)\n",
+        "        if len(output.shape) == 1:\n",
+        "            output = output.reshape(1, *output.shape)\n",
+        "\n",
+        "        pred = output.argmax(dim=1, keepdim=True)\n",
+        "        correct += pred.eq(target.view_as(pred)).sum().item()\n",
+        "\n",
+        "        loss = loss_func(output, target)\n",
+        "        total_loss.append(loss.item())\n",
+        "\n",
+        "    print(\n",
+        "        \"Performance on test data:\\n\\tLoss: {:.4f}\\n\\tAccuracy: {:.1f}%\".format(\n",
+        "            sum(total_loss) / len(total_loss), correct / len(test_data) / 1 * 100\n",
+        "        )\n",
+        "    )"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 26,
+      "id": "nU8ko0T02iKg",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 170
+        },
+        "id": "nU8ko0T02iKg",
+        "outputId": "05f2b18b-32a6-4648-cd9f-91b2c4ca4812"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 1000x300 with 6 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "n_samples_show = 6\n",
+        "count = 0\n",
+        "fig, axes = plt.subplots(nrows=1, ncols=n_samples_show, figsize=(10, 3))\n",
+        "\n",
+        "model5.eval()\n",
+        "with no_grad():\n",
+        "    for batch_idx, (data, target) in enumerate(test_data):\n",
+        "        if count == n_samples_show:\n",
+        "            break\n",
+        "        output = model5(data[0:1])\n",
+        "        if len(output.shape) == 1:\n",
+        "            output = output.reshape(1, *output.shape)\n",
+        "\n",
+        "        pred = output.argmax(dim=1, keepdim=True)\n",
+        "\n",
+        "        axes[count].imshow(data[0].numpy().squeeze(), cmap=\"gray\")\n",
+        "\n",
+        "        axes[count].set_xticks([])\n",
+        "        axes[count].set_yticks([])\n",
+        "        titulo = \"R: \"+str(target.item()) + \", P: \"+ str(pred.item())\n",
+        "        axes[count].set_title(titulo)\n",
+        "\n",
+        "        count += 1"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "6HIyTAoSH2Ly",
+      "metadata": {
+        "id": "6HIyTAoSH2Ly"
+      },
+      "source": [
+        "Se realizaron las siguientes combinaciones del modelo:\n",
+        "![photo_2023-11-10_01-55-35.jpg](data:image/jpeg;base64,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)\n",
+        "\n",
+        "En este caso nuestro dataset, funciona muy bien si redimensionamos las imágenes a 28 * 28, ya que incluso todos los resultados  son mejores con este tamaño independiente de la combinación de capas o kernels. El resultado es muy bueno si se dejan 2 capas conv2d y 2 lineales y comienza a mejorar si se incrementa el número de épocas"
+      ]
+    }
+  ],
+  "metadata": {
+    "accelerator": "GPU",
+    "colab": {
+      "gpuType": "T4",
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "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": "3.10.4"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 5
+}
diff --git a/hackathon/model4.pt b/hackathon/model4.pt
new file mode 100644
index 0000000..37204bc
Binary files /dev/null and b/hackathon/model4.pt differ
diff --git a/hackathon/torch_prueba1.py b/hackathon/torch_prueba1.py
new file mode 100644
index 0000000..9bdcb93
--- /dev/null
+++ b/hackathon/torch_prueba1.py
@@ -0,0 +1,132 @@
+import numpy as np
+import matplotlib.pyplot as plt
+
+from torch import Tensor
+from torch.nn import Linear, CrossEntropyLoss, MSELoss
+from torch.optim import LBFGS
+
+from qiskit import QuantumCircuit
+from qiskit.utils import algorithm_globals
+from qiskit.circuit import Parameter
+from qiskit.circuit.library import RealAmplitudes, ZZFeatureMap
+from qiskit_machine_learning.neural_networks import SamplerQNN, EstimatorQNN
+from qiskit_machine_learning.connectors import TorchConnector
+
+# Set seed for random generators
+algorithm_globals.random_seed = 42
+
+num_inputs = 2
+num_samples = 20
+
+# Generate random input coordinates (X) and binary labels (y)
+X = 2 * algorithm_globals.random.random([num_samples, num_inputs]) - 1
+y01 = 1 * (np.sum(X, axis=1) >= 0)  # in { 0,  1}, y01 will be used for SamplerQNN example
+y = 2 * y01 - 1  # in {-1, +1}, y will be used for EstimatorQNN example
+
+# Convert to torch Tensors
+X_ = Tensor(X)
+y01_ = Tensor(y01).reshape(len(y)).long()
+y_ = Tensor(y).reshape(len(y), 1)
+
+# Plot dataset
+for x, y_target in zip(X, y):
+    if y_target == 1:
+        plt.plot(x[0], x[1], "bo")
+    else:
+        plt.plot(x[0], x[1], "go")
+plt.plot([-1, 1], [1, -1], "--", color="black")
+# plt.show()
+
+print("====================EstimatorQNN===============================")
+
+feature_map = ZZFeatureMap(num_inputs)
+ansatz = RealAmplitudes(num_inputs)
+qc = QuantumCircuit(num_inputs)
+qc.compose(feature_map, inplace=True)
+qc.compose(ansatz, inplace=True)
+
+qnn1 = EstimatorQNN(
+    circuit=qc, input_params=feature_map.parameters, weight_params=ansatz.parameters
+)
+
+# Set up PyTorch module
+# Note: If we don't explicitly declare the initial weights
+# they are chosen uniformly at random from [-1, 1].
+initial_weights = 0.1 * (2 * algorithm_globals.random.random(qnn1.num_weights) - 1)
+model1 = TorchConnector(qnn1, initial_weights=initial_weights)
+# print("Initial weights: ", initial_weights)
+
+
+
+optimizer = LBFGS(model1.parameters())
+f_loss = MSELoss(reduction="sum")
+
+# Start training
+model1.train()  # set model to training mode
+
+
+# Note from (https://pytorch.org/docs/stable/optim.html):
+# Some optimization algorithms such as LBFGS need to
+# reevaluate the function multiple times, so you have to
+# pass in a closure that allows them to recompute your model.
+# The closure should clear the gradients, compute the loss,
+# and return it.
+def closure():
+    optimizer.zero_grad()  # Initialize/clear gradients
+    loss = f_loss(model1(X_), y_)  # Evaluate loss function
+    loss.backward()  # Backward pass
+    print(loss.item())  # Print loss
+    return loss
+
+
+# Run optimizer step4
+optimizer.step(closure)
+
+print("====================SamplerQNN===============================")
+
+feature_map = ZZFeatureMap(num_inputs)
+ansatz = RealAmplitudes(num_inputs, entanglement="linear", reps=1)
+
+# Define quantum circuit of num_qubits = input dim
+# Append feature map and ansatz
+qc = QuantumCircuit(num_inputs)
+qc.compose(feature_map, inplace=True)
+qc.compose(ansatz, inplace=True)
+
+# Define SamplerQNN and initial setup
+parity = lambda x: "{:b}".format(x).count("1") % 2  # optional interpret function
+output_shape = 2  # parity = 0, 1
+qnn2 = SamplerQNN(
+    circuit=qc,
+    input_params=feature_map.parameters,
+    weight_params=ansatz.parameters,
+    interpret=parity,
+    output_shape=output_shape,
+)
+
+# Set up PyTorch module
+# Reminder: If we don't explicitly declare the initial weights
+# they are chosen uniformly at random from [-1, 1].
+initial_weights = 0.1 * (2 * algorithm_globals.random.random(qnn2.num_weights) - 1)
+print("Initial weights: ", initial_weights)
+model2 = TorchConnector(qnn2, initial_weights)
+
+# Define model, optimizer, and loss
+optimizer = LBFGS(model2.parameters())
+f_loss = CrossEntropyLoss()  # Our output will be in the [0,1] range
+
+# Start training
+model2.train()
+
+# Define LBFGS closure method (explained in previous section)
+def closure():
+    optimizer.zero_grad(set_to_none=True)  # Initialize gradient
+    loss = f_loss(model2(X_), y01_)  # Calculate loss
+    loss.backward()  # Backward pass
+
+    print(loss.item())  # Print loss
+    return loss
+
+
+# Run optimizer (LBFGS requires closure)
+optimizer.step(closure)
\ No newline at end of file