# SimQN: a discrete-event simulator for the quantum networks
# Copyright (C) 2021-2022 Lutong Chen, Jian Li, Kaiping Xue
# University of Science and Technology of China, USTC.
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <https://www.gnu.org/licenses/>.
import itertools
from qns.entity.node.app import Application
from qns.entity.qchannel.qchannel import QuantumChannel
from qns.entity.node.node import QNode
from typing import Dict, List, Optional, Tuple
from qns.network.topology import Topology
from qns.utils.rnd import get_weighted_choice
[docs]
class BarabasiAlbertTopology(Topology):
"""
BarabasiAlbertTopology is a random topology generator based on the Barabasi-Albert model.
Each new QNode added has `new_nodes_egdes` to existing QNodes.
The probability of a new QNode connecting to an existing QNode is proportional to the degree of the existing QNode.
"""
def __init__(self, nodes_number, new_nodes_egdes: int,
nodes_apps: List[Application] = [],
qchannel_args: Dict = {}, cchannel_args: Dict = {},
memory_args: Optional[List[Dict]] = {}):
"""
Args:
nodes_number: the number of Qnodes
new_nodes_egdes: the number of edges of a new node, must be greater than 0 and less than nodes_number
"""
super().__init__(nodes_number, nodes_apps, qchannel_args, cchannel_args, memory_args)
self.new_nodes_egdes = new_nodes_egdes
[docs]
def build(self) -> Tuple[List[QNode], List[QuantumChannel]]:
# check config
if self.new_nodes_egdes < 1 or self.new_nodes_egdes >= self.nodes_number:
raise ValueError("new_nodes_egdes must be greater than 0 and less than nodes_number")
nl: List[QNode] = []
ll: List[QuantumChannel] = []
# generate initial QNodes and QuantumChannels
for i in range(self.new_nodes_egdes):
n = QNode(f"n{i+1}")
nl.append(n)
initial_edges = list(itertools.combinations(nl, 2))
for n1, n2 in initial_edges:
qc = QuantumChannel(name=f"l{n1}-{n2}", **self.qchannel_args)
ll.append(qc)
n1.add_qchannel(qc)
n2.add_qchannel(qc)
# generate new QNodes following Barabasi-Albert model
for i in range(self.new_nodes_egdes, self.nodes_number):
n = QNode(f"n{i+1}")
# deal with boundary conditions
if self.new_nodes_egdes == 1 and i == 1:
n1 = nl[0]
nl.append(n1)
qc = QuantumChannel(name=f"l{n1}-{n}", **self.qchannel_args)
ll.append(qc)
n1.add_qchannel(qc)
n.add_qchannel(qc)
continue
# add new_node_egdes edges for the new node
weighted_choice = [len(n_i.qchannels) for n_i in nl]
choice_list = get_weighted_choice(nl, weighted_choice, self.new_nodes_egdes)
for n_i in choice_list:
qc = QuantumChannel(name=f"l{n_i}-{n}", **self.qchannel_args)
ll.append(qc)
n.add_qchannel(qc)
n_i.add_qchannel(qc)
nl.append(n)
# QNode configuration
self._add_apps(nl)
self._add_memories(nl)
return nl, ll