# Source code for qiskit_optimization.converters.quadratic_program_to_qubo

```
# This code is part of Qiskit.
#
# (C) Copyright IBM 2020, 2021.
#
# This code is licensed under the Apache License, Version 2.0. You may
# obtain a copy of this license in the LICENSE.txt file in the root directory
# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
#
# Any modifications or derivative works of this code must retain this
# copyright notice, and modified files need to carry a notice indicating
# that they have been altered from the originals.
"""A converter from quadratic program to a QUBO."""
from typing import List, Optional, Union, cast
import numpy as np
from ..converters.flip_problem_sense import MaximizeToMinimize
from ..converters.inequality_to_equality import InequalityToEquality
from ..converters.integer_to_binary import IntegerToBinary
from ..converters.linear_equality_to_penalty import LinearEqualityToPenalty
from ..converters.linear_inequality_to_penalty import LinearInequalityToPenalty
from ..exceptions import QiskitOptimizationError
from ..problems.quadratic_program import QuadraticProgram
from .quadratic_program_converter import QuadraticProgramConverter
[docs]class QuadraticProgramToQubo(QuadraticProgramConverter):
"""Convert a given optimization problem to a new problem that is a QUBO.
Examples:
>>> from qiskit_optimization.problems import QuadraticProgram
>>> from qiskit_optimization.converters import QuadraticProgramToQubo
>>> problem = QuadraticProgram()
>>> # define a problem
>>> conv = QuadraticProgramToQubo()
>>> problem2 = conv.convert(problem)
"""
def __init__(self, penalty: Optional[float] = None) -> None:
"""
Args:
penalty: Penalty factor to scale equality constraints that are added to objective.
If None is passed, a penalty factor will be automatically calculated on every
conversion.
"""
self._penalize_lin_eq_constraints = LinearEqualityToPenalty(penalty=penalty)
self._penalize_lin_ineq_constraints = LinearInequalityToPenalty(penalty=penalty)
self._converters = [
self._penalize_lin_ineq_constraints,
InequalityToEquality(mode="integer"),
IntegerToBinary(),
self._penalize_lin_eq_constraints,
MaximizeToMinimize(),
]
[docs] def convert(self, problem: QuadraticProgram) -> QuadraticProgram:
"""Convert a problem with linear constraints into new one with a QUBO form.
Args:
problem: The problem with linear constraints to be solved.
Returns:
The problem converted in QUBO format as minimization problem.
Raises:
QiskitOptimizationError: In case of an incompatible problem.
"""
# analyze compatibility of problem
msg = self.get_compatibility_msg(problem)
if len(msg) > 0:
raise QiskitOptimizationError("Incompatible problem: {}".format(msg))
for conv in self._converters:
problem = conv.convert(problem)
return problem
[docs] def interpret(self, x: Union[np.ndarray, List[float]]) -> np.ndarray:
"""Convert a result of a converted problem into that of the original problem.
Args:
x: The result of the converted problem.
Returns:
The result of the original problem.
"""
for conv in self._converters[::-1]:
x = conv.interpret(x)
return cast(np.ndarray, x)
[docs] @staticmethod
def get_compatibility_msg(problem: QuadraticProgram) -> str:
"""Checks whether a given problem can be solved with this optimizer.
Checks whether the given problem is compatible, i.e., whether the problem can be converted
to a QUBO, and otherwise, returns a message explaining the incompatibility.
Args:
problem: The optimization problem to check compatibility.
Returns:
A message describing the incompatibility.
"""
# initialize message
msg = ""
# check whether there are incompatible variable types
if problem.get_num_continuous_vars() > 0:
msg += "Continuous variables are not supported! "
# check whether there are incompatible constraint types
if len(problem.quadratic_constraints) > 0:
msg += "Quadratic constraints are not supported. "
# check whether there are float coefficients in constraints
compatible_with_integer_slack = True
for l_constraint in problem.linear_constraints:
linear = l_constraint.linear.to_dict()
if any(isinstance(coef, float) and not coef.is_integer() for coef in linear.values()):
compatible_with_integer_slack = False
for q_constraint in problem.quadratic_constraints:
linear = q_constraint.linear.to_dict()
quadratic = q_constraint.quadratic.to_dict()
if any(
isinstance(coef, float) and not coef.is_integer() for coef in quadratic.values()
) or any(isinstance(coef, float) and not coef.is_integer() for coef in linear.values()):
compatible_with_integer_slack = False
if not compatible_with_integer_slack:
msg += "Can not convert inequality constraints to equality constraint because \
float coefficients are in constraints. "
# if an error occurred, return error message, otherwise, return None
return msg
[docs] def is_compatible(self, problem: QuadraticProgram) -> bool:
"""Checks whether a given problem can be solved with the optimizer implementing this method.
Args:
problem: The optimization problem to check compatibility.
Returns:
Returns True if the problem is compatible, False otherwise.
"""
return len(self.get_compatibility_msg(problem)) == 0
@property
def penalty(self) -> Optional[float]:
"""Returns the penalty factor used in conversion.
Returns:
The penalty factor used in conversion.
"""
return self._penalize_lin_eq_constraints.penalty
@penalty.setter
def penalty(self, penalty: Optional[float]) -> None:
"""Set a new penalty factor.
Args:
penalty: The new penalty factor.
If None is passed, a penalty factor will be automatically calculated on every
conversion.
"""
self._penalize_lin_ineq_constraints.penalty = penalty
self._penalize_lin_eq_constraints.penalty = penalty
```