def CalculateRoute(CostMatrix, solverSetting: SolverSetting, workspace):
##### Initialize solver specificationss -- In all cases we do parameter-free solving because we don't want to do manual tuning
SolverName = solverSetting.SolverName # Name of the solver
HardwareSpec = solverSetting.Hardware # The hardware, CPU or FPGA
Timeout = solverSetting.Timeout # The timeout for the solver, in seconds
##### Raise errors if invalid request - Some solvers require specific user inputs
if (SolverName.lower() == 'quantum monte carlo'):
raise HTTPException(
status_code=400,
detail=
'Can only implement a Quantum Monte Carlo with paramtrization (manual tuning). It has been disabled for this demo. Check the QIO docs.'
)
if (SolverName.lower() == 'tabu search' or SolverName.lower()
== 'quantum monte carlo' or SolverName.lower()
== 'parallel tempering') and (HardwareSpec.lower() == 'fpga'):
raise HTTPException(
status_code=400,
detail=
'Can only implement Tabu search, Quantum Monte Carlo, Parallel Tempering on CPU. Check the QIO docs.'
)
##### The type of hardware the solver will run on
if HardwareSpec.lower() == 'cpu':
HardwareInt = 1
elif HardwareSpec.lower() == 'fpga':
HardwareInt = 2
else:
raise HTTPException(
status_code=400,
detail='Hardware choice not valid - choose from CPU/FPGA')
##### The solver algorithm initialized
if SolverName.lower() == 'simulated annealing':
solver = SimulatedAnnealing(workspace,
timeout=Timeout,
platform=HardwareInt)
elif SolverName.lower() == 'parallel tempering':
solver = ParallelTempering(workspace, timeout=Timeout)
elif SolverName.lower() == 'tabu search':
solver = Tabu(workspace, timeout=Timeout)
elif SolverName.lower() == 'quantum monte carlo':
solver = QuantumMonteCarlo(workspace, timeout=Timeout)
##### Submit the optimization problem to the QIO workspace
OptimizationProblem = OptProblem(
CostMatrix) # Initialize the optimization problem instance
result = solver.submit(
OptimizationProblem
) # We submit because we want to track the status of the optimization - means we do not immediately have a solution, will need to fetch it later
##### Return the optimization result
return result
problem_type=ProblemType.ising,
terms=terms)
# This array contains a list of the weights of the containers
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']:
# This array contains a list of the weights of the containers:
containerWeights = [1, 5, 9, 21, 35, 5, 3, 5, 10, 11]
# Create the Terms for this list of containers:
terms = createProblemForContainerWeights(containerWeights)
# Create the Problem to submit to the solver:
problem = Problem(name="Ship Loading Problem",
problem_type=ProblemType.ising,
terms=terms)
from azure.quantum.optimization import ParallelTempering
# Instantiate a solver instance to solve the problem
solver = ParallelTempering(workspace, timeout=100) # timeout in seconds
# Optimize the problem
result = solver.optimize(problem)
def printResultSummary(result):
# Print a summary of the result
shipAWeight = 0
shipBWeight = 0
for container in result['configuration']:
containerAssignment = result['configuration'][container]
containerWeight = containerWeights[int(container)]
ship = ''
if containerAssignment == 1:
ship = 'A'