from __future__ import annotations
from typing import Union
import networkx as nx
import cupy as cp
import copy
import json
from qwak_cupy.Errors import (
StateOutOfBounds,
NonUnitaryState,
UndefinedTimeList,
EmptyProbDistList,
MissingNodeInput,
MissingGraphInput,
)
from qwak_cupy.State import State
from qwak_cupy.Operator import Operator
from qwak_cupy.QuantumWalk import QuantumWalk
from qwak_cupy.ProbabilityDistribution import (
ProbabilityDistribution,
)
[docs]
class QWAK:
def __init__(
self,
graph: nx.Graph,
time: float = 0,
timeList: list = None,
gamma: float = 1,
initStateList: list = None,
customStateList: list = None,
laplacian: bool = False,
markedElements: list = [],
qwakId: str = 'userUndef',
) -> None:
"""Data access class that combines all three components required to
perform a continuous-time quantum walk, given by the multiplication of
an operator (represented by the Operator class) by an initial state
(State class). This multiplication is achieved in the
StaticQuantumwalk class, which returns a final state (State Class)
representing the amplitudes of each state associated with a graph node.
These amplitudes can then be transformed to probability distributions
(ProbabilityDistribution class) suitable for plotting with matplotlib,
or your package of choice.
Default values for the initial state, time and transition rate are a
column vector full of 0s, 0 and 1, respectively. Methods runWalk or
buildWalk must then be used to generate the results of the quantum
walk.
Parameters
----------
graph : nx.Graph
NetworkX graph where the walk takes place. Also used
for defining the dimensions of the quantum walk.
time : float
Time interval for the quantum walk, by default None.
timeList : list
List with time intervals for multiple walks, by default None.
initStateList : list[int], optional
List with chosen initial states for uniform superposition, by default None
customStateList : list[(int,complex)], optional
Custom init state, by default None.
laplacian : bool, optional
Allows the user to choose whether to use the
Laplacian or simple adjacency matrix, by default False.
markedElements : list, optional
List with marked elements for search, by default None.
qwakId : str, optional
User-defined ID for the QWAK instance, by default 'userUndef'.
"""
self._graph = graph
self._n = len(self._graph)
if timeList is not None:
self._timeList = [x for x in timeList]
else:
self._timeList = [0] * self._n
self._qwakId = qwakId
self._operator = Operator(
self._graph,
time=time,
gamma=gamma,
laplacian=laplacian,
markedElements=markedElements)
self._initState = State(
self._n,
nodeList=initStateList,
customStateList=customStateList)
self._quantumWalk = QuantumWalk(self._initState, self._operator)
self._probDist = ProbabilityDistribution(
self._quantumWalk.getFinalState())
self._probDistList = cp.array([])
[docs]
def runWalk(
self,
time: float = 0,
initStateList: list = None,
customStateList: list = None) -> None:
"""Builds class' attributes, runs the walk and calculates the amplitudes
and probability distributions with the given parameters. These can be
accessed with their respective get methods.
Parameters
----------
time : float, optional
Time for which to calculate the quantum walk, by default 0.
initStateList : list[int], optional
List with chosen initial states for uniform superposition, by default None.
customStateList : list[(int,complex)], optional
Custom init state, by default None.
Raises
------
StateOutOfBounds
State out of bounds exception.
NonUnitaryState
State not unitary exception.
"""
try:
self._initState.buildState(
nodeList=initStateList, customStateList=customStateList
)
except StateOutOfBounds as stOBErr:
raise stOBErr
except NonUnitaryState as nUErr:
raise nUErr
self._operator.buildDiagonalOperator(time=time)
self._quantumWalk.buildWalk(self._initState, self._operator)
self._probDist.buildProbDist(self._quantumWalk.getFinalState())
[docs]
def runExpmWalk(
self,
time: float = 0,
initStateList: list = None,
customStateList: list = None) -> None:
"""Builds class' attributes, runs the walk and calculates the amplitudes
and probability distributions with the given parameters. These can be
accessed with their respective get methods.
Parameters
----------
time : float, optional
Time for which to calculate the quantum walk, by default 0.
initStateList : list[int], optional
List with chosen initial states for uniform superposition, by default None.
customStateList : list[(int,complex)], optional
Custom init state, by default None.
Raises
------
StateOutOfBounds
State out of bounds exception.
NonUnitaryState
State not unitary exception.
"""
try:
self._initState.buildState(
nodeList=initStateList, customStateList=customStateList
)
except StateOutOfBounds as stOBErr:
raise stOBErr
except NonUnitaryState as nUErr:
raise nUErr
self._operator.buildExpmOperator(time=time)
self._quantumWalk.buildWalk(self._initState, self._operator)
self._probDist.buildProbDist(self._quantumWalk.getFinalState())
[docs]
def runMultipleWalks(
self,
timeList: list = None,
initStateList: list = None,
customStateList: list = None) -> None:
"""Runs the walk for multiple times and stores the probability distributions
in a list.
Parameters
----------
timeList : list, optional
List of times for which to calculate the quantum walk, by default None.
initStateList : list, optional
List with chosen initial states for uniform superposition, by default None.
customStateList : list, optional
Custom init state, by default None.
Raises
------
UndefinedTimeList
Raised when the timeList is None.
"""
self._probDistList = []
if timeList is not None:
self._timeList = timeList
elif self._timeList is None:
raise UndefinedTimeList(f"TimeList is {self._timeList}.")
for time in self._timeList:
self.runWalk(
time=time,
initStateList=initStateList,
customStateList=customStateList)
self._probDistList.append(copy.deepcopy(self.getProbDist()))
[docs]
def runMultipleExpmWalks(
self,
timeList: list = None,
initStateList: list = None,
customStateList: list = None) -> None:
"""Runs the walk for multiple times and stores the probability distributions
in a list.
Parameters
----------
timeList : list, optional
List of times for which to calculate the quantum walk, by default None.
initStateList : list, optional
List with chosen initial states for uniform superposition, by default None.
customStateList : list, optional
Custom init state, by default None.
Raises
------
UndefinedTimeList
Raised when the timeList is None.
"""
self._probDistList = []
if timeList is not None:
self._timeList = timeList
elif self._timeList is None:
raise UndefinedTimeList(f"TimeList is {self._timeList}.")
for time in self._timeList:
self.runExpmWalk(
time=time,
initStateList=initStateList,
customStateList=customStateList)
self._probDistList.append(copy.deepcopy(self.getProbDist()))
def setProbDist(self, newProbDist: ProbabilityDistribution) -> None:
"""Sets current walk probability distribution to a user defined one.
This might not be needed and removed in the future.
Parameters
----------
newProbDist : ProbabilityDistribution
New probability distribution.
"""
self._probDist.setProbDist(newProbDist)
def getProbDist(self) -> ProbabilityDistribution:
"""Gets the current probability distribution.
Returns
-------
ProbabilityDistribution
ProbabilityDistribution object.
"""
return self._probDist
def getProbDistList(self) -> list:
"""Returns a list of probability distributions in the case of multiple walks.
Returns
-------
list
List of ProbabilityDistribution objects.
"""
return self._probDistList
def setProbDistList(self, newProbDistList: list) -> None:
"""Sets the current probability distribution list to a user defined one.
Parameters
----------
newProbDistList : list
New probability distribution list.
"""
self._probDistList = newProbDistList
def getProbVec(self) -> cp.ndarray:
"""Gets the current probability distribution vector.
Returns
-------
cp.ndarray
Probability Distribution vector.
"""
return self._probDist.getProbVec()
def getProbVecList(self) -> list:
"""Returns a list of probability distribution vectors in the case of multiple walks.
Returns
-------
list
List of probability distribution vectors.
"""
return [probDist.getProbVec()
for probDist in self._probDistList]
def resetWalk(self) -> None:
"""Resets the components of a walk."""
self._initState.resetState()
self._operator.resetOperator()
self._quantumWalk.resetWalk()
self._probDist.resetProbDist()
self._probDistList = []
self._walkList = []
def setDim(
self,
newDim: int,
graphStr: str = None,
graph: nx.Graph = None,
initStateList: list = None) -> None:
"""Sets the current walk dimensions to a user defined one.
Also takes a graph string to be
evaluated and executed as a NetworkX graph generator.
Parameters
----------
newDim : int
New dimension for the quantum walk.
graphStr : str
Graph string to generate the graph with the new dimension.
graph : nx.Graph, optional
Graph with the new dimension.
initStateList : list[int], optional
Init state list with new dimension.
"""
self._n = newDim
if graphStr is not None:
self._graph = eval(f"{graphStr}({self._n})")
self._n = len(self._graph)
elif graph is not None:
self._graph = graph
self._n = len(self._graph)
else:
raise MissingGraphInput(
f"You tried to set QWAK dim without providing a graph with updated dimensions: {self._graph}")
self._initState.setDim(newDim, newNodeList=initStateList)
self._operator.setDim(newDim, self._graph)
self._quantumWalk.setDim(newDim)
self._probDist.setDim(newDim)
def getGraph(self) -> nx.Graph:
"""Gets the current graph.
Returns
-------
nx.Graph
Current graph.
"""
return self._graph
def getDim(self) -> int:
"""Gets the current graph dimension.
Returns
-------
int
Dimension of graph.
"""
return self._n
def setGraph(self, newGraph: nx.Graph, initStateList=None) -> None:
"""Sets the current graph to a user defined one.
Also recalculates the current operator and walk dimension.
Parameters
----------
newGraph : nx.Graph
New NetworkX graph.
"""
self._graph = newGraph
self._n = len(self._graph)
def setCustomGraph(self, customAdjMatrix: cp.ndarray) -> None:
"""Sets the current graph to a user defined one.
Parameters
----------
customAdjMatrix : cp.ndarray
Adjacency matrix of the new graph.
"""
self._graph = nx.from_numpy_array(customAdjMatrix)
self.setGraph(newGraph=self._graph)
self._initStateList = [self._n // 2]
self.setDim(
self._n,
graph=self._graph,
initStateList=self._initStateList)
def setInitState(self, newInitState: State) -> None:
"""Sets the current initial state to a user defined one.
Parameters
----------
newInitState : State
New initial state.
"""
self._initState.setState(newInitState)
self._initStateList = self._initState.getNodeList()
def setTime(self, newTime: float) -> None:
"""Sets the current walk time to a user defined one.
Parameters
----------
newTime : float
New time.
"""
self._operator.setTime(newTime)
def setTimeList(self, newTimeList: list) -> None:
"""Sets the current walk time to a user defined one.
Parameters
----------
newTimeList : list
New time list.
"""
timeList = cp.linspace(
newTimeList[0], newTimeList[1], int(
newTimeList[1]))
self._timeList = timeList.tolist()
def getTime(self) -> float:
"""Gets the current walk time.
Returns
-------
float
Current value of time.
"""
return self._operator.getTime()
def getTimeList(self) -> float:
"""Gets the current walk time.
Returns
-------
float
Current value of time.
"""
return self._timeList
def setAdjacencyMatrix(
self, newAdjMatrix: cp.ndarray, initStateList: list = None
) -> None:
"""Sets the current adjacency matrix to a user defined one.
Parameters
----------
newAdjMatrix : cp.ndarray
New adjacency matrix.
initStateList : list, optional
New initial state list, by default None.
"""
newAdjMatrix = cp.array(newAdjMatrix)
self._n = len(self._operator.getAdjacencyMatrix())
self._operator.setAdjacencyMatrix(newAdjMatrix)
self._initState = State(self._n, initStateList)
self._quantumWalk = QuantumWalk(self._initState, self._operator)
self._probDist = ProbabilityDistribution(
self._quantumWalk.getFinalState())
def getAdjacencyMatrix(self) -> cp.ndarray:
"""Gets the current adjacency matrix.
Returns
-------
cp.ndarray
Current adjacency matrix.
"""
return self._operator.getAdjacencyMatrix()
def setHamiltonian(self, newHamiltonian: cp.ndarray) -> None:
"""Sets the current Hamiltonian to a user defined one.
Parameters
----------
newHamiltonian : cp.ndarray
New Hamiltonian.
"""
self._operator.setHamiltonian(newHamiltonian)
def getHamiltonian(self) -> cp.ndarray:
"""Gets the current Hamiltonian.
Returns
-------
cp.ndarray
Current Hamiltonian.
"""
return self._operator.getHamiltonian()
def setOperator(self, newOperator: Operator) -> None:
"""Sets the current walk operator a user defined one.
Parameters
----------
newOperator : Operator
New operator object.
"""
self._operator.setOperator(newOperator)
def getOperator(self) -> Operator:
"""Gets the current walk operator.
Returns
-------
Operator
Current operator object.
"""
return self._operator
def setWalk(self, newWalk: State) -> None:
"""Sets current walk amplitudes to a user defined state.
This might not be needed and removed in the future.
Parameters
----------
newWalk : State
New walk amplitudes.
"""
self._quantumWalk.setWalk(newWalk)
def getWalk(self) -> QuantumWalk:
"""Gets current QuantumWalk object
Returns
-------
QuantumWalk
Current state amplitudes.
"""
return self._quantumWalk
def getFinalState(self) -> State:
"""Gets current QuantumWalk State.
Returns
-------
State
State of the QuantumWalk.
"""
return self._quantumWalk.getFinalState()
def getAmpVec(self) -> cp.ndarray:
"""Gets the array of the QuantumWalk state.
Returns
-------
cp.ndarray
Array of the QuantumWalk state.
"""
return self._quantumWalk.getAmpVec()
def setProbDist(self, newProbDist: ProbabilityDistribution) -> None:
"""Sets current walk probability distribution to a user defined one.
This might not be needed and removed in the future.
Parameters
----------
newProbDist : ProbabilityDistribution
New probability distribution.
"""
self._probDist.setProbDist(newProbDist)
def getProbDist(self) -> ProbabilityDistribution:
"""Gets the current probability distribution.
Returns
-------
ProbabilityDistribution
ProbabilityDistribution object.
"""
return self._probDist
def getProbDistList(self) -> list:
"""Returns a list of probability distributions in the case of multiple walks.
Returns
-------
list
List of ProbabilityDistribution objects.
"""
return self._probDistList
def setProbDistList(self, newProbDistList: list) -> None:
"""Sets the current probability distribution list to a user defined one.
Parameters
----------
newProbDistList : list
New probability distribution list.
"""
self._probDistList = newProbDistList
def getProbVec(self) -> cp.ndarray:
"""Gets the current probability distribution vector.
Returns
-------
cp.ndarray
Probability Distribution vector.
"""
return self._probDist.getProbVec()
def getProbVecList(self) -> list:
"""Returns a list of probability distribution vectors in the case of multiple walks.
Returns
-------
list
List of probability distribution vectors.
"""
return [probDist.getProbVec()
for probDist in self._probDistList]
[docs]
def resetWalk(self) -> None:
"""Resets the components of a walk."""
self._initState.resetState()
self._operator.resetOperator()
self._quantumWalk.resetWalk()
self._probDist.resetProbDist()
self._probDistList = []
self._walkList = []
[docs]
def setDim(
self,
newDim: int,
graphStr: str = None,
graph: nx.Graph = None,
initStateList: list = None) -> None:
"""Sets the current walk dimensions to a user defined one.
Also takes a graph string to be
evaluated and executed as a NetworkX graph generator.
Parameters
----------
newDim : int
New dimension for the quantum walk.
graphStr : str
Graph string to generate the graph with the new dimension.
graph : nx.Graph, optional
Graph with the new dimension.
initStateList : list[int], optional
Init state list with new dimension.
"""
self._n = newDim
if graphStr is not None:
self._graph = eval(f"{graphStr}({self._n})")
self._n = len(self._graph)
elif graph is not None:
self._graph = graph
self._n = len(self._graph)
else:
raise MissingGraphInput(
f"You tried to set QWAK dim without providing a graph with updated dimensions: {self._graph}")
self._initState.setDim(newDim, newNodeList=initStateList)
self._operator.setDim(newDim, self._graph)
self._quantumWalk.setDim(newDim)
self._probDist.setDim(newDim)
[docs]
def getGraph(self) -> nx.Graph:
"""Gets the current graph.
Returns
-------
nx.Graph
Current graph.
"""
return self._graph
[docs]
def getDim(self) -> int:
"""Gets the current graph dimension.
Returns
-------
int
Dimension of graph.
"""
return self._n
[docs]
def setGraph(self, newGraph: nx.Graph, initStateList=None) -> None:
"""Sets the current graph to a user defined one.
Also recalculates the current operator and walk dimension.
Parameters
----------
newGraph : nx.Graph
New NetworkX graph.
"""
self._graph = newGraph
self._n = len(self._graph)
[docs]
def setCustomGraph(self, customAdjMatrix: cp.ndarray) -> None:
"""Sets the current graph to a user defined one.
Parameters
----------
customAdjMatrix : cp.ndarray
Adjacency matrix of the new graph.
"""
self._graph = nx.from_numpy_array(customAdjMatrix)
self.setGraph(newGraph=self._graph)
self._initStateList = [self._n // 2]
self.setDim(
self._n,
graph=self._graph,
initStateList=self._initStateList)
[docs]
def setInitState(self, newInitState: State) -> None:
"""Sets the current initial state to a user defined one.
Parameters
----------
newInitState : State
New initial state.
"""
self._initState.setState(newInitState)
self._initStateList = self._initState.getNodeList()
[docs]
def setTime(self, newTime: float) -> None:
"""Sets the current walk time to a user defined one.
Parameters
----------
newTime : float
New time.
"""
self._operator.setTime(newTime)
[docs]
def setTimeList(self, newTimeList: list) -> None:
"""Sets the current walk time to a user defined one.
Parameters
----------
newTimeList : list
New time list.
"""
timeList = cp.linspace(
newTimeList[0], newTimeList[1], int(
newTimeList[1]))
self._timeList = timeList.tolist()
[docs]
def getTime(self) -> float:
"""Gets the current walk time.
Returns
-------
float
Current value of time.
"""
return self._operator.getTime()
[docs]
def getTimeList(self) -> float:
"""Gets the current walk time.
Returns
-------
float
Current value of time.
"""
return self._timeList
[docs]
def setAdjacencyMatrix(
self, newAdjMatrix: cp.ndarray, initStateList: list = None
) -> None:
"""Sets the current adjacency matrix to a user defined one.
Parameters
----------
newAdjMatrix : cp.ndarray
New adjacency matrix.
initStateList : list, optional
New initial state list, by default None.
"""
newAdjMatrix = cp.array(newAdjMatrix)
self._n = len(self._operator.getAdjacencyMatrix())
self._operator.setAdjacencyMatrix(newAdjMatrix)
self._initState = State(self._n, initStateList)
self._quantumWalk = QuantumWalk(self._initState, self._operator)
self._probDist = ProbabilityDistribution(
self._quantumWalk.getFinalState())
[docs]
def getAdjacencyMatrix(self) -> cp.ndarray:
"""Gets the current adjacency matrix.
Returns
-------
cp.ndarray
Current adjacency matrix.
"""
return self._operator.getAdjacencyMatrix()
[docs]
def setHamiltonian(self, newHamiltonian: cp.ndarray) -> None:
"""Sets the current Hamiltonian to a user defined one.
Parameters
----------
newHamiltonian : cp.ndarray
New Hamiltonian.
"""
self._operator.setHamiltonian(newHamiltonian)
[docs]
def getHamiltonian(self) -> cp.ndarray:
"""Gets the current Hamiltonian.
Returns
-------
cp.ndarray
Current Hamiltonian.
"""
return self._operator.getHamiltonian()
[docs]
def setOperator(self, newOperator: Operator) -> None:
"""Sets the current walk operator a user defined one.
Parameters
----------
newOperator : Operator
New operator object.
"""
self._operator.setOperator(newOperator)
[docs]
def getOperator(self) -> Operator:
"""Gets the current walk operator.
Returns
-------
Operator
Current operator object.
"""
return self._operator
[docs]
def setWalk(self, newWalk: State) -> None:
"""Sets current walk amplitudes to a user defined state.
This might not be needed and removed in the future.
Parameters
----------
newWalk : State
New walk amplitudes.
"""
self._quantumWalk.setWalk(newWalk)
[docs]
def getWalk(self) -> QuantumWalk:
"""Gets current QuantumWalk object
Returns
-------
QuantumWalk
Current state amplitudes.
"""
return self._quantumWalk
[docs]
def getFinalState(self) -> State:
"""Gets current QuantumWalk State.
Returns
-------
State
State of the QuantumWalk.
"""
return self._quantumWalk.getFinalState()
[docs]
def getAmpVec(self) -> cp.ndarray:
"""Gets the array of the QuantumWalk state.
Returns
-------
cp.ndarray
Array of the QuantumWalk state.
"""
return self._quantumWalk.getAmpVec()
[docs]
def setProbDist(self, newProbDist: ProbabilityDistribution) -> None:
"""Sets current walk probability distribution to a user defined one.
This might not be needed and removed in the future.
Parameters
----------
newProbDist : ProbabilityDistribution
New probability distribution.
"""
self._probDist.setProbDist(newProbDist)
[docs]
def getProbDist(self) -> ProbabilityDistribution:
"""Gets the current probability distribution.
Returns
-------
ProbabilityDistribution
ProbabilityDistribution object.
"""
return self._probDist
[docs]
def getProbDistList(self) -> list:
"""Returns a list of probability distributions in the case of multiple walks.
Returns
-------
list
List of ProbabilityDistribution objects.
"""
return self._probDistList
[docs]
def setProbDistList(self, newProbDistList: list) -> None:
"""Sets the current probability distribution list to a user defined one.
Parameters
----------
newProbDistList : list
New probability distribution list.
"""
self._probDistList = newProbDistList
[docs]
def getProbVec(self) -> cp.ndarray:
"""Gets the current probability distribution vector.
Returns
-------
cp.ndarray
Probability Distribution vector.
"""
return self._probDist.getProbVec()
[docs]
def getProbVecList(self) -> list:
"""Returns a list of probability distribution vectors in the case of multiple walks.
Returns
-------
list
List of probability distribution vectors.
"""
return [probDist.getProbVec()
for probDist in self._probDistList]
def searchNodeAmplitude(self, searchNode: int) -> complex:
"""User inputted node for search
Parameters
----------
searchNode : int
User inputted node for the search.
Returns
-------
complex
Amplitude associated with the search node.
"""
return self._quantumWalk.searchNodeAmplitude(searchNode)
def searchNodeProbability(self, searchNode: int) -> float:
"""Searches and gets the probability associated with a given node.
Parameters
----------
searchNode : int
User inputted node for the search.
Returns
-------
float
Probability associated with the search node.
"""
return self._probDist.searchNodeProbability(searchNode)
def getMean(self, resultRounding: int = None) -> float:
"""Gets the mean of the probability distribution.
Parameters
----------
resultRounding : int, optional
Rounding of the result, by default None.
Returns
-------
float
Mean of the probability distribution.
"""
return self._probDist.moment(1) if (
resultRounding is None) \
else round(self._probDist.moment(1), resultRounding)
def getMeanList(self, resultRounding: int = None) -> list:
"""Gets the mean of the probability distribution list.
Parameters
----------
resultRounding : int, optional
Rounding of the results, by default None.
Returns
-------
list
List of means of the probability distributions.
"""
return [
probDist.moment(1) for probDist in self._probDistList] if (
resultRounding is None) \
else [
round(
probDist.moment(1),
resultRounding) for probDist in self._probDistList]
def getSndMoment(self, resultRounding: int = None) -> float:
"""Gets the second moment of the probability distribution.
Parameters
----------
resultRounding : int, optional
Rounding of the result, by default None.
Returns
-------
float
Second moment of the probability distribution.
"""
return self._probDist.moment(2) if (resultRounding is None) \
else round(
self._probDist.moment(2), resultRounding)
def getStDev(self, resultRounding: int = None) -> float:
"""Gets the standard deviation of the probability distribution.
Parameters
----------
resultRounding : int, optional
Rounding of the result, by default None.
Returns
-------
float
Standard deviation of the probability distribution.
"""
return self._probDist.stDev() if (resultRounding is None) \
else round(
self._probDist.stDev(),
resultRounding)
def getStDevList(self, resultRounding: int = None) -> list:
"""Gets the standard deviation of the probability distribution list.
Parameters
----------
resultRounding : int, optional
Rounding of the results, by default None.
Returns
-------
list
List of standard deviations of the probability distributions.
"""
return [
probDist.stDev() for probDist in self._probDistList] if (
resultRounding is None) \
else [
round(
probDist.stDev(),
resultRounding) for probDist in self._probDistList]
def getInversePartRatio(self, resultRounding: int = None) -> float:
"""Gets the inverse participation ratio of the probability distribution.
Parameters
----------
resultRounding : int, optional
Rounding of the result, by default None.
Returns
-------
float
Inverse participation ratio of the probability distribution.
"""
return self._probDist.invPartRatio() if (
resultRounding is None) \
else round(
self._probDist.invPartRatio(), resultRounding)
def getInversePartRatioList(
self, resultRounding: int = None) -> list:
"""Gets the inverse participation ratio of the probability distribution list.
Parameters
----------
resultRounding : int, optional
Rounding of the results, by default None.
Returns
-------
list
List of inverse participation ratios of the probability distributions.
"""
return [
probDist.invPartRatio() for probDist in self._probDistList] if (
resultRounding is None) else [
round(
probDist.invPartRatio(),
resultRounding) for probDist in self._probDistList]
def getSurvivalProb(
self,
fromNode,
toNode,
resultRounding: int = None) -> float:
"""Gets the survival probability of the probability distribution.
Parameters
----------
fromNode : int
Starting node.
toNode : int
Ending node.
resultRounding : int, optional
Rounding of the result, by default None.
Returns
-------
float
Survival probability of the probability distribution.
Raises
------
MissingNodeInput
Missing input node error.
"""
try:
return self._probDist.survivalProb(
fromNode,
toNode) if (
resultRounding is None) else round(
self._probDist.survivalProb(
fromNode,
toNode),
resultRounding)
except MissingNodeInput as err:
raise err
def getSurvivalProbList(
self,
fromNode,
toNode,
resultRounding: int = None) -> list:
"""Gets the survival probability of the probability distribution list.
Parameters
----------
fromNode : int
Starting node.
toNode : int
Ending node.
resultRounding : int, optional
Rounding of the results, by default None.
Returns
-------
list
List of survival probabilities of the probability distributions.
Raises
------
MissingNodeInput
Missing input node error.
"""
try:
return [
probDist.survivalProb(
fromNode,
toNode) for probDist in self._probDistList] if (
resultRounding is None) else [
round(
probDist.survivalProb(
fromNode,
toNode),
resultRounding) for probDist in self._probDistList]
except MissingNodeInput as err:
raise err
def setQWAK(self, newQWAK: QWAK) -> None:
"""Sets the QWAK instance's attributes to the ones of the given QWAK instance.
Parameters
----------
newQWAK : QWAK
QWAK instance to copy the attributes from.
"""
self.setGraph(newQWAK.getGraph())
self.setDim(newQWAK.getDim(), graph=self._graph)
self.setInitState(newQWAK.getInitState())
self.setOperator(newQWAK.getOperator())
self.setWalk(newQWAK.getWalk())
self.setProbDist(newQWAK.getProbDist())
def searchNodeAmplitude(self, searchNode: int) -> complex:
"""User inputted node for search
Parameters
----------
searchNode : int
User inputted node for the search.
Returns
-------
complex
Amplitude associated with the search node.
"""
return self._quantumWalk.searchNodeAmplitude(searchNode)
def searchNodeProbability(self, searchNode: int) -> float:
"""Searches and gets the probability associated with a given node.
Parameters
----------
searchNode : int
User inputted node for the search.
Returns
-------
float
Probability associated with the search node.
"""
return self._probDist.searchNodeProbability(searchNode)
def getMean(self, resultRounding: int = None) -> float:
"""Gets the mean of the probability distribution.
Parameters
----------
resultRounding : int, optional
Rounding of the result, by default None.
Returns
-------
float
Mean of the probability distribution.
"""
return self._probDist.moment(1) if (
resultRounding is None) \
else round(self._probDist.moment(1), resultRounding)
def getMeanList(self, resultRounding: int = None) -> list:
"""Gets the mean of the probability distribution list.
Parameters
----------
resultRounding : int, optional
Rounding of the results, by default None.
Returns
-------
list
List of means of the probability distributions.
"""
return [
probDist.moment(1) for probDist in self._probDistList] if (
resultRounding is None) \
else [
round(
probDist.moment(1),
resultRounding) for probDist in self._probDistList]
def getSndMoment(self, resultRounding: int = None) -> float:
"""Gets the second moment of the probability distribution.
Parameters
----------
resultRounding : int, optional
Rounding of the result, by default None.
Returns
-------
float
Second moment of the probability distribution.
"""
return self._probDist.moment(2) if (resultRounding is None) \
else round(
self._probDist.moment(2), resultRounding)
def getStDev(self, resultRounding: int = None) -> float:
"""Gets the standard deviation of the probability distribution.
Parameters
----------
resultRounding : int, optional
Rounding of the result, by default None.
Returns
-------
float
Standard deviation of the probability distribution.
"""
return self._probDist.stDev() if (resultRounding is None) \
else round(
self._probDist.stDev(),
resultRounding)
def getStDevList(self, resultRounding: int = None) -> list:
"""Gets the standard deviation of the probability distribution list.
Parameters
----------
resultRounding : int, optional
Rounding of the results, by default None.
Returns
-------
list
List of standard deviations of the probability distributions.
"""
return [
probDist.stDev() for probDist in self._probDistList] if (
resultRounding is None) \
else [
round(
probDist.stDev(),
resultRounding) for probDist in self._probDistList]
def getInversePartRatio(self, resultRounding: int = None) -> float:
"""Gets the inverse participation ratio of the probability distribution.
Parameters
----------
resultRounding : int, optional
Rounding of the result, by default None.
Returns
-------
float
Inverse participation ratio of the probability distribution.
"""
return self._probDist.invPartRatio() if (
resultRounding is None) \
else round(
self._probDist.invPartRatio(), resultRounding)
def getInversePartRatioList(
self, resultRounding: int = None) -> list:
"""Gets the inverse participation ratio of the probability distribution list.
Parameters
----------
resultRounding : int, optional
Rounding of the results, by default None.
Returns
-------
list
List of inverse participation ratios of the probability distributions.
"""
return [
probDist.invPartRatio() for probDist in self._probDistList] if (
resultRounding is None) else [
round(
probDist.invPartRatio(),
resultRounding) for probDist in self._probDistList]
def getSurvivalProb(
self,
fromNode,
toNode,
resultRounding: int = None) -> float:
"""Gets the survival probability of the probability distribution.
Parameters
----------
fromNode : int
Starting node.
toNode : int
Ending node.
resultRounding : int, optional
Rounding of the result, by default None.
Returns
-------
float
Survival probability of the probability distribution.
Raises
------
MissingNodeInput
Missing input node error.
"""
try:
return self._probDist.survivalProb(
fromNode,
toNode) if (
resultRounding is None) else round(
self._probDist.survivalProb(
fromNode,
toNode),
resultRounding)
except MissingNodeInput as err:
raise err
def getSurvivalProbList(
self,
fromNode,
toNode,
resultRounding: int = None) -> list:
"""Gets the survival probability of the probability distribution list.
Parameters
----------
fromNode : int
Starting node.
toNode : int
Ending node.
resultRounding : int, optional
Rounding of the results, by default None.
Returns
-------
list
List of survival probabilities of the probability distributions.
Raises
------
MissingNodeInput
Missing input node error.
"""
try:
return [
probDist.survivalProb(
fromNode,
toNode) for probDist in self._probDistList] if (
resultRounding is None) else [
round(
probDist.survivalProb(
fromNode,
toNode),
resultRounding) for probDist in self._probDistList]
except MissingNodeInput as err:
raise err
[docs]
def setQWAK(self, newQWAK: QWAK) -> None:
"""Sets the QWAK instance's attributes to the ones of the given QWAK instance.
Parameters
----------
newQWAK : QWAK
QWAK instance to copy the attributes from.
"""
self.setGraph(newQWAK.getGraph())
self.setDim(newQWAK.getDim(), graph=self._graph)
self.setInitState(newQWAK.getInitState())
self.setOperator(newQWAK.getOperator())
self.setWalk(newQWAK.getWalk())
self.setProbDist(newQWAK.getProbDist())
[docs]
def searchNodeAmplitude(self, searchNode: int) -> complex:
"""User inputted node for search
Parameters
----------
searchNode : int
User inputted node for the search.
Returns
-------
complex
Amplitude associated with the search node.
"""
return self._quantumWalk.searchNodeAmplitude(searchNode)
[docs]
def searchNodeProbability(self, searchNode: int) -> float:
"""Searches and gets the probability associated with a given node.
Parameters
----------
searchNode : int
User inputted node for the search.
Returns
-------
float
Probability associated with the search node.
"""
return self._probDist.searchNodeProbability(searchNode)
[docs]
def getMean(self, resultRounding: int = None) -> float:
"""Gets the mean of the probability distribution.
Parameters
----------
resultRounding : int, optional
Rounding of the result, by default None.
Returns
-------
float
Mean of the probability distribution.
"""
return self._probDist.moment(1) if (
resultRounding is None) \
else round(self._probDist.moment(1), resultRounding)
[docs]
def getMeanList(self, resultRounding: int = None) -> list:
"""Gets the mean of the probability distribution list.
Parameters
----------
resultRounding : int, optional
Rounding of the results, by default None.
Returns
-------
list
List of means of the probability distributions.
"""
return [
probDist.moment(1) for probDist in self._probDistList] if (
resultRounding is None) \
else [
round(
probDist.moment(1),
resultRounding) for probDist in self._probDistList]
[docs]
def getSndMoment(self, resultRounding: int = None) -> float:
"""Gets the second moment of the probability distribution.
Parameters
----------
resultRounding : int, optional
Rounding of the result, by default None.
Returns
-------
float
Second moment of the probability distribution.
"""
return self._probDist.moment(2) if (resultRounding is None) \
else round(
self._probDist.moment(2), resultRounding)
[docs]
def getStDev(self, resultRounding: int = None) -> float:
"""Gets the standard deviation of the probability distribution.
Parameters
----------
resultRounding : int, optional
Rounding of the result, by default None.
Returns
-------
float
Standard deviation of the probability distribution.
"""
return self._probDist.stDev() if (resultRounding is None) \
else round(
self._probDist.stDev(),
resultRounding)
[docs]
def getStDevList(self, resultRounding: int = None) -> list:
"""Gets the standard deviation of the probability distribution list.
Parameters
----------
resultRounding : int, optional
Rounding of the results, by default None.
Returns
-------
list
List of standard deviations of the probability distributions.
"""
return [
probDist.stDev() for probDist in self._probDistList] if (
resultRounding is None) \
else [
round(
probDist.stDev(),
resultRounding) for probDist in self._probDistList]
[docs]
def getInversePartRatio(self, resultRounding: int = None) -> float:
"""Gets the inverse participation ratio of the probability distribution.
Parameters
----------
resultRounding : int, optional
Rounding of the result, by default None.
Returns
-------
float
Inverse participation ratio of the probability distribution.
"""
return self._probDist.invPartRatio() if (
resultRounding is None) \
else round(
self._probDist.invPartRatio(), resultRounding)
[docs]
def getInversePartRatioList(
self, resultRounding: int = None) -> list:
"""Gets the inverse participation ratio of the probability distribution list.
Parameters
----------
resultRounding : int, optional
Rounding of the results, by default None.
Returns
-------
list
List of inverse participation ratios of the probability distributions.
"""
return [
probDist.invPartRatio() for probDist in self._probDistList] if (
resultRounding is None) else [
round(
probDist.invPartRatio(),
resultRounding) for probDist in self._probDistList]
[docs]
def getSurvivalProb(
self,
fromNode,
toNode,
resultRounding: int = None) -> float:
"""Gets the survival probability of the probability distribution.
Parameters
----------
fromNode : int
Starting node.
toNode : int
Ending node.
resultRounding : int, optional
Rounding of the result, by default None.
Returns
-------
float
Survival probability of the probability distribution.
Raises
------
MissingNodeInput
Missing input node error.
"""
try:
return self._probDist.survivalProb(
fromNode,
toNode) if (
resultRounding is None) else round(
self._probDist.survivalProb(
fromNode,
toNode),
resultRounding)
except MissingNodeInput as err:
raise err
[docs]
def getSurvivalProbList(
self,
fromNode,
toNode,
resultRounding: int = None) -> list:
"""Gets the survival probability of the probability distribution list.
Parameters
----------
fromNode : int
Starting node.
toNode : int
Ending node.
resultRounding : int, optional
Rounding of the results, by default None.
Returns
-------
list
List of survival probabilities of the probability distributions.
Raises
------
MissingNodeInput
Missing input node error.
"""
try:
return [
probDist.survivalProb(
fromNode,
toNode) for probDist in self._probDistList] if (
resultRounding is None) else [
round(
probDist.survivalProb(
fromNode,
toNode),
resultRounding) for probDist in self._probDistList]
except MissingNodeInput as err:
raise err