# 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_rand
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class ErdosRenyiTopology(Topology):
"""
ErdosRenyiTopology includes `nodes_number` Qnodes.
The topology is randomly generated following the Erdos-Renyi model(G(n,p) Model).
Each pair of Qnodes has a probability `generate_prob` to be connected by a QuantumChannel.
"""
def __init__(self, nodes_number, generate_prob: float,
nodes_apps: List[Application] = [],
qchannel_args: Dict = {}, cchannel_args: Dict = {},
memory_args: Optional[List[Dict]] = {}):
"""
Args:
nodes_number: the number of Qnodes
generate_prob: the probability of QuantumChannel generation between two Qnodes
"""
super().__init__(nodes_number, nodes_apps, qchannel_args, cchannel_args, memory_args)
self.generate_prob = generate_prob
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def build(self) -> Tuple[List[QNode], List[QuantumChannel]]:
nl: List[QNode] = []
ll: List[QuantumChannel] = []
# generate Qnodes
for i in range(self.nodes_number):
n = QNode(f"n{i+1}")
nl.append(n)
# generate QuantumChannels
edges = list(itertools.combinations(nl, 2))
for n1, n2 in edges:
if get_rand() < self.generate_prob:
qc = QuantumChannel(name=f"l{n1}-{n2}", **self.qchannel_args)
ll.append(qc)
n1.add_qchannel(qc)
n2.add_qchannel(qc)
# QNode configuration
self._add_apps(nl)
self._add_memories(nl)
return nl, ll