import random
import numpy as np
from dwave.system import DWaveSampler, EmbeddingComposite
import dwave_networkx as dnx
import networkx as nx
import dimod
# Number of geolocations (nodes)
num_nodes = 6
# Function to generate random geolocations
def generate_geolocations(num_nodes):
geolocations = [(random.uniform(0, 100), random.uniform(0, 100)) for _ in range(num_nodes)]
return geolocations
# Function to calculate the Euclidean distance between two points
def euclidean_distance(p1, p2):
return np.sqrt((p1[0] - p2[0])**2 + (p1[1] - p2[1])**2)
# Generate geolocations
geolocations = generate_geolocations(num_nodes)
# Create a complete graph with nodes representing geolocations
G = nx.complete_graph(num_nodes)
# Add edges with weights as the Euclidean distance between geolocations
for i, p1 in enumerate(geolocations):
for j, p2 in enumerate(geolocations):
if i != j:
G.add_edge(i, j, weight=euclidean_distance(p1, p2))
# Set up the QUBO problem
lagrange = 1.0
qubo = dnx.traveling_salesperson_qubo(G, lagrange)
# Convert the QUBO to a BQM (Binary Quadratic Model)
bqm = dimod.BinaryQuadraticModel.from_qubo(qubo)
# Set up the sampler
sampler = EmbeddingComposite(DWaveSampler())
# Solve the TSP problem using D-Wave's quantum annealer
sampleset = sampler.sample(bqm)
# Find the shortest path from the sampleset
path = dnx.traveling_salesperson(G, sampleset)
print("Shortest path:", path)
print("Total distance:", sampleset.first.energy)