QdAnnealingIR#

class qudas.annealing.ir.QdAnnealingIR(qubo: dict | None = None)[source]#

Bases: Mapping

量子アニーリング用の中間表現 (QUBO) を表すクラス。

旧 QuDataInput で担っていた QUBO 変換・演算機能を移植した。 qubo は dict で保持し、キーは変数名のタプル、値は係数。

Any = typing.Any#
class Base#

Bases: pybind11_object

compile(*args, **kwargs)#

Overloaded function.

  1. compile(self: cpp_pyqubo.Base, strength: float = 5) -> pyqubo::model

  2. compile(self: cpp_pyqubo.Base, strength: cpp_pyqubo.Base) -> pyqubo::model

Dict#

alias of Dict

class LpProblem(name='NoName', sense=1)#

Bases: object

An LP Problem

add(constraint, name=None)#
addConstraint(constraint, name=None)#
addVariable(variable)#

Adds a variable to the problem before a constraint is added

@param variable: the variable to be added

addVariables(variables)#

Adds variables to the problem before a constraint is added

@param variables: the variables to be added

assignConsPi(values)#
assignConsSlack(values, activity=False)#
assignStatus(status, sol_status=None)#

Sets the status of the model after solving. :param status: code for the status of the model :param sol_status: code for the status of the solution :return:

assignVarsDj(values)#
assignVarsVals(values)#
checkDuplicateVars() → None#

Checks if there are at least two variables with the same name :return: 1 :raises const.PulpError: if there ar duplicates

checkLengthVars(max_length: int) → None#

Checks if variables have names smaller than max_length :param int max_length: max size for variable name :return: :raises const.PulpError: if there is at least one variable that has a long name

coefficients(translation=None)#
copy()#

Make a copy of self. Expressions are copied by reference

deepcopy()#

Make a copy of self. Expressions are copied by value

extend(other, use_objective=True)#

extends an LpProblem by adding constraints either from a dictionary a tuple or another LpProblem object.

@param use_objective: determines whether the objective is imported from the other problem

For dictionaries the constraints will be named with the keys For tuples an unique name will be generated For LpProblems the name of the problem will be added to the constraints name

fixObjective()#
classmethod fromDict(_dict)#

Takes a dictionary with all necessary information to build a model. And returns a dictionary of variables and a problem object

Parameters:

_dict – dictionary with the model stored

Returns:

a tuple with a dictionary of variables and a LpProblem

classmethod fromJson(filename)#

Creates a new Lp Problem from a json file with information

Parameters:

filename (str) – json file name

Returns:

a tuple with a dictionary of variables and an LpProblem

Return type:

(dict, LpProblem)

classmethod fromMPS(filename, sense=1, **kwargs)#
classmethod from_dict(_dict)#

Takes a dictionary with all necessary information to build a model. And returns a dictionary of variables and a problem object

Parameters:

_dict – dictionary with the model stored

Returns:

a tuple with a dictionary of variables and a LpProblem

classmethod from_json(filename)#

Creates a new Lp Problem from a json file with information

Parameters:

filename (str) – json file name

Returns:

a tuple with a dictionary of variables and an LpProblem

Return type:

(dict, LpProblem)

getSense()#
get_dummyVar()#
infeasibilityGap(mip=1)#
isMIP()#
normalisedNames()#
numConstraints()#
Returns:

number of constraints in model

numVariables()#
Returns:

number of variables in model

resolve(solver=None, **kwargs)#

resolves an Problem using the same solver as previously

restoreObjective(wasNone, dummyVar)#
roundSolution(epsInt=1e-05, eps=1e-07)#

Rounds the lp variables

Inputs:

  • none

Side Effects:

  • The lp variables are rounded

sequentialSolve(objectives, absoluteTols=None, relativeTols=None, solver=None, debug=False)#

Solve the given Lp problem with several objective functions.

This function sequentially changes the objective of the problem and then adds the objective function as a constraint

Parameters:

  • objectives – the list of objectives to be used to solve the problem

  • absoluteTols – the list of absolute tolerances to be applied to the constraints should be +ve for a minimise objective

  • relativeTols – the list of relative tolerances applied to the constraints

  • solver – the specific solver to be used, defaults to the default solver.

setObjective(obj)#

Sets the input variable as the objective function. Used in Columnwise Modelling

Parameters:

obj – the objective function of type LpConstraintVar

Side Effects:

  • The objective function is set

setSolver(solver=<pulp.apis.coin_api.PULP_CBC_CMD object>)#

Sets the Solver for this problem useful if you are using resolve

solve(solver=None, **kwargs)#

Solve the given Lp problem.

This function changes the problem to make it suitable for solving then calls the solver.actualSolve() method to find the solution

Parameters:

solver – Optional: the specific solver to be used, defaults to the default solver.

Side Effects:

  • The attributes of the problem object are changed in actualSolve() to reflect the Lp solution

startClock()#

initializes properties with the current time

stopClock()#

updates time wall time and cpu time

toDict()#

creates a dictionary from the model with as much data as possible. It replaces variables by variable names. So it requires to have unique names for variables.

Returns:

dictionary with model data

Return type:

dict

toJson(filename, *args, **kwargs)#

Creates a json file from the LpProblem information

Parameters:

  • filename (str) – filename to write json

  • args – additional arguments for json function

  • kwargs – additional keyword arguments for json function

Returns:

None

to_dict()#

creates a dictionary from the model with as much data as possible. It replaces variables by variable names. So it requires to have unique names for variables.

Returns:

dictionary with model data

Return type:

dict

to_json(filename, *args, **kwargs)#

Creates a json file from the LpProblem information

Parameters:

  • filename (str) – filename to write json

  • args – additional arguments for json function

  • kwargs – additional keyword arguments for json function

Returns:

None

unusedConstraintName()#
valid(eps=0)#
variables()#

Returns the problem variables

Returns:

A list containing the problem variables

Return type:

(list, LpVariable)

variablesDict()#
writeLP(filename, writeSOS=1, mip=1, max_length=100)#

Write the given Lp problem to a .lp file.

This function writes the specifications (objective function, constraints, variables) of the defined Lp problem to a file.

Parameters:

filename (str) – the name of the file to be created.

Returns:

variables

Side Effects:

  • The file is created

writeMPS(filename, mpsSense=0, rename=0, mip=1, with_objsense: bool = False)#

Writes an mps files from the problem information

Parameters:

  • filename (str) – name of the file to write

  • mpsSense (int) –

  • rename (bool) – if True, normalized names are used for variables and constraints

  • mip – variables and variable renames

Returns:

Side Effects:

  • The file is created

Optional = typing.Optional#
class Poly#

Bases: object

class Iterator#

Bases: object

as_dict#

Returns: dict[tuple[int, …], float]:

as_variable#

Returns: amplify.Variable:

asdict#

alias of as_dict()

Return type:

dict[tuple[int, …], float]

decode#

Overloading:

1. decode(self, values: amplify.Values) -> float

Args:

  • values (amplify.Values):

Returns:

float:

2. decode(self, values: amplify.Values, default: float) -> float

Args:

  • values (amplify.Values):

  • default (float):

Returns:

float:

3. decode(self, values: amplify.Values, default: Optional[None]) -> amplify.Poly

Args:

  • values (amplify.Values):

  • default (None | None):

Returns:

amplify.Poly:

degree#

Returns: int:

evaluate#

Overloading:

1. evaluate(self, values: amplify.Values) -> float

Args:

  • values (amplify.Values):

Returns:

float:

2. evaluate(self, values: amplify.Values, default: float) -> float

Args:

  • values (amplify.Values):

  • default (float):

Returns:

float:

3. evaluate(self, values: amplify.Values, default: Optional[None]) -> amplify.Poly

Args:

  • values (amplify.Values):

  • default (None | None):

Returns:

amplify.Poly:

property id#

id property

Return type:

int

is_linear#

Returns: bool:

is_number#

Returns: bool:

is_quadratic#

Returns: bool:

is_variable#

Returns: bool:

property lower_bound#

lower_bound property

Return type:

float | None

property name#

name property

Return type:

str

substitute#
Parameters:

mapping (dict[amplify.Poly, Union[amplify.Poly, float, int]]) –

Return type:

amplify.Poly

property type#

type property

Return type:

amplify.VariableType

property upper_bound#

upper_bound property

Return type:

float | None

property variables#

variables property

Return type:

List[amplify.Variable]

csv = <module 'csv' from '/Users/kei/opt/anaconda3/envs/quantum/lib/python3.8/csv.py'>#
dimod = <module 'dimod' from '/Users/kei/Desktop/python/quantum programming/nedo/qudas/.venv/lib/python3.8/site-packages/dimod/__init__.py'>#
from_amplify(prob: Poly) → QdAnnealingIR[source]#
from_array(prob: np.ndarray) → QdAnnealingIR[source]#
from_csv(path: str, encoding: str = 'utf-8-sig') → QdAnnealingIR[source]#
classmethod from_dict(data: dict) → QdAnnealingIR[source]#
from_dimod_bqm(prob: dimod.BinaryQuadraticModel) → QdAnnealingIR[source]#
from_json(path: str) → QdAnnealingIR[source]#
from_networkx(prob: nx.Graph) → QdAnnealingIR[source]#
from_pandas(prob: pd.DataFrame) → QdAnnealingIR[source]#
from_pulp(prob: LpProblem) → QdAnnealingIR[source]#
from_pyqubo(prob: Base) → QdAnnealingIR[source]#
from_sympy(prob: sympy.core.expr.Expr) → QdAnnealingIR[source]#
json = <module 'json' from '/Users/kei/opt/anaconda3/envs/quantum/lib/python3.8/json/__init__.py'>#
np = <module 'numpy' from '/Users/kei/Desktop/python/quantum programming/nedo/qudas/.venv/lib/python3.8/site-packages/numpy/__init__.py'>#
nx = <module 'networkx' from '/Users/kei/Desktop/python/quantum programming/nedo/qudas/.venv/lib/python3.8/site-packages/networkx/__init__.py'>#
pd = <module 'pandas' from '/Users/kei/Desktop/python/quantum programming/nedo/qudas/.venv/lib/python3.8/site-packages/pandas/__init__.py'>#
property prob#
sympy = <module 'sympy' from '/Users/kei/Desktop/python/quantum programming/nedo/qudas/.venv/lib/python3.8/site-packages/sympy/__init__.py'>#
to_amplify()[source]#
to_array()[source]#
to_csv(name: str = 'qudata') → None[source]#
to_dict() → dict[source]#

簡易ダンプ

to_dimod_bqm()[source]#
to_json(name: str = 'qudata') → None[source]#
to_networkx()[source]#
to_pandas()[source]#
to_pulp()[source]#
to_pyqubo()[source]#
to_sympy()[source]#