'''
Implement the Quantum Optimization Algorithm that solves the Travelling Sales Man problem in Python
'''

pip install numpy networkx qiskit

import numpy as np
import networkx as nx
from qiskit import Aer, execute
from qiskit.aqua.algorithms import QAOA
from qiskit.aqua.components.optimizers import COBYLA
from qiskit.circuit.library import TwoLocal

# Generate a random graph with n nodes
n = 4
graph = nx.Graph()
graph.add_nodes_from(range(n))
edges = [(i, j, np.random.randint(1, 10)) for i in range(n) for j in range(i+1, n)]
graph.add_weighted_edges_from(edges)

# Define the cost function
def cost_function(x, graph):
    cost = 0
    for i in range(len(x)):
        for j in range(i+1, len(x)):
            if x[i] == 1 and x[j] == 1:
                cost += graph[i][j]['weight']
    return cost

# Define the QAOA circuit
p = 1 # number of layers
optimizer = COBYLA()
qaoa = QAOA(optimizer=optimizer, p=p, initial_state=None, mixer=None, cost_fn=cost_function, quantum_instance=Aer.get_backend('statevector_simulator'))

# Run the QAOA algorithm
result = qaoa.compute_minimum_eigenvalue(graph)

# Print the results
print(result)