container_weights = [1, 5, 9, 21, 35, 5, 3, 5, 10, 11]
# Create a problem for the list of containers:
problem = create_problem_for_container_weights(container_weights)
# Submit problem to Azure Quantum using the ParallelTempering solver:
from azure.quantum.optimization import ParallelTempering
import time
# Instantiate a solver to solve the problem.
solver = ParallelTempering(workspace, timeout=100)
# Optimize the problem
print('Submitting problem...')
start = time.time()
result = solver.optimize(problem)
timeElapsed = time.time() - start
print(f'\nResult in {timeElapsed} seconds:\n{result}\n')
# Print out a summary of the results:
def print_result_summary(result):
# Print a summary of the result
ship_a_weight = 0
ship_b_weight = 0
for container in result['configuration']:
container_assignment = result['configuration'][container]
container_weight = container_weights[int(container)]
ship = ''
if container_assignment == 1:
ship = 'A'
# This allows you to connect to the Workspace you've previously deployed in Azure.
# Be sure to fill in the settings below which can be retrieved by running 'az quantum workspace show' in the terminal.
from azure.quantum import Workspace
# Copy the settings for your workspace below
workspace = Workspace(subscription_id="",
resource_group="",
name="",
location="")
# Define the problem
problem = Problem(name="My First Problem", problem_type=ProblemType.ising)
terms = [
Term(c=-9, indices=[0]),
Term(c=-3, indices=[1, 0]),
Term(c=5, indices=[2, 0]),
Term(c=9, indices=[2, 1]),
Term(c=2, indices=[3, 0]),
Term(c=-4, indices=[3, 1]),
Term(c=4, indices=[3, 2])
]
problem.add_terms(terms=terms)
# Create the solver
solver = ParallelTempering(workspace, timeout=100)
# Solve the problem
result = solver.optimize(problem)
print(result)