Welcome to SimQN's documentation. SimQN is a discrete-event-based network simulation platform for quantum networks. SimQN enables large-scale investigations, including QKD protocols, entanglement distributions protocols, and routing algorithms, resource allocation schemas in quantum networks. For example, users can use SimQN to design routing algorithms for better QKD performance. For more information, please refer to the Documents.
SimQN is a Python3 library for quantum networking simulation. It is designed to be general purpose. It means that SimQN can be used for both QKD network, entanglement distribution networks, and other kinds of quantum networks' evaluation. The core idea is that SimQN makes no architecture assumption. Since there is currently no recognized network architecture in quantum network investigations, SimQN stays flexible in this aspect.
SimQN provides high performance for large-scale network simulation. SimQN uses Cython to compile critical codes in C/C++ libraries to boost the evaluation. Also, along with the commonly used quantum state-based physical models, SimQN provides a higher-layer fidelity-based entanglement physical model to reduce the computation overhead and brings convenience for users in evaluation. Last but not least, SimQN provides several network auxiliary models for easily building network topologies, producing routing tables and managing multiple session requests.
- This documentation may answer most questions.
- The tutorial here presents how to use SimQN.
- The API manual shows more detailed information.
- Welcome to report bugs at Github.
Install and update using pip:
pip3 install -U qns
Here is an example of using SimQN.
from qns.simulator.simulator import Simulator
from qns.network.topology import RandomTopology
from qns.network.protocol.entanglement_distribution import EntanglementDistributionApp
from qns.network import QuantumNetwork
from qns.network.route.dijkstra import DijkstraRouteAlgorithm
from qns.network.topology.topo import ClassicTopology
import qns.utils.log as log
import logging
init_fidelity = 0.99 # the initial entanglement's fidelity
nodes_number = 150 # the number of nodes
lines_number = 450 # the number of quantum channels
qchannel_delay = 0.05 # the delay of quantum channels
cchannel_delay = 0.05 # the delay of classic channels
memory_capacity = 50 # the size of quantum memories
send_rate = 10 # the send rate
requests_number = 10 # the number of sessions (SD-pairs)
# generate the simulator
s = Simulator(0, 10, accuracy=1000000)
# set the log's level
log.logger.setLevel(logging.INFO)
log.install(s)
# generate a random topology using the parameters above
# each node will install EntanglementDistributionApp for hop-by-hop entanglement distribution
topo = RandomTopology(nodes_number=nodes_number,
lines_number=lines_number,
qchannel_args={"delay": qchannel_delay},
cchannel_args={"delay": cchannel_delay},
memory_args=[{"capacity": memory_capacity}],
nodes_apps=[EntanglementDistributionApp(init_fidelity=init_fidelity)])
# build the network, with Dijkstra's routing algorithm
net = QuantumNetwork(topo=topo, classic_topo=ClassicTopology.All, route=DijkstraRouteAlgorithm())
# build the routing table
net.build_route()
# randomly select multiple sessions (SD-pars)
net.random_requests(requests_number, attr={"send_rate": send_rate})
# all entities in the network will install the simulator and do initiate works.
net.install(s)
# run simulation
s.run()SimQN is designed as a functional and easy-to-use simulator, like NS3 in classic networks, it provides numerous functions for anyone who wants to simulate a QKD network or entanglement-based network.
Compared with the existing quantum network simulators, the developers pay more attention to simulation in the network area. Currently, a network simulation can be complicated, as users may have to implement routing algorithms and multiply protocols in different layers to complete a simulation. SimQN aims to break down this problem by providing a modulized quantum node and reusable algorithms and protocols. As a result, users can focus on what they study and reuse other built-in modules. The developers believe this will significantly reduce the burden on our users. As for the physics area, SimQN can also simulate quantum noise, fidelity, and more. Thus, if you focus on the research of the quantum network area, SimQN can be a competitive choice.
Welcome to contribute through Github Issue or Pull Requests. Please refer to the develop guide. If you have any questions, you are welcome to contact the developers via e-mail.
SimQN is an open-source project under GPLv3 license. The authors of the paper includes:
- Lutong Chen (ertuil), School of Cyber Science and Technology, University of Science and Technology of China, China. elliot.98@outlook.com
- Jian Li(infonetlijian), School of Cyber Science and Technology, University of Science and Technology of China, China.
- Kaiping Xue (kaipingxue), School of Cyber Science and Technology, University of Science and Technology of China, China. xue.kaiping@gmail.com
- Nenghai Yu, School of Cyber Science and Technology, University of Science and Technology of China, China.
- Ruidong Li, Institute of Science and Engineering, Kanazawa University, Japan.
- Qibin Sun, School of Cyber Science and Technology, University of Science and Technology of China, China.
- Jun Lu, School of Cyber Science and Technology, University of Science and Technology of China, China.
Other contributors includes:
- Zirui Xiao, School of Cyber Science and Technology, University of Science and Technology of China, China.
- Yuqi Yang, School of Cyber Science and Technology, University of Science and Technology of China, China.
- Bing Yang, School of Cyber Science and Technology, University of Science and Technology of China, China.
- Xumin Gao, School of Cyber Science and Technology, University of Science and Technology of China, China.