Skip to main content

A discrete-event scheduler designed for quantum networks

Project description


Pytest Flake8

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 propose. It means that SimQN can be used for both QKD network, entanglement distribution network 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 networks investigations, SimQN stays flexible in this aspect.

SimQN provides high performance for large-scale network simulation. SimQN use Cython to compile critical codes in C/C++ libraries to boost the evaluation. Also, along with the common 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.

Get Help


Install and update using pip:

pip3 install -U qns

First sight of SimQN

Here is an example of using SimQN.

    from qns.simulator.simulator import Simulator
    from import RandomTopology
    from import EntanglementDistributionApp
    from import QuantumNetwork
    from import DijkstraRouteAlgorithm
    from import ClassicTopology
    import qns.utils.log as log

    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

    # 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,
        qchannel_args={"delay": qchannel_delay},
        cchannel_args={"delay": cchannel_delay},
        memory_args=[{"capacity": memory_capacity}],

    # build the network, with Dijkstra's routing algorithm
    net = QuantumNetwork( topo=topo, classic_topo=ClassicTopology.All, route=DijkstraRouteAlgorithm())

    # build the routing table

    # 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.

    # run simulation


Why choose SimQN?

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.

How to contribute?

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.

Project details

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distributions

No source distribution files available for this release. See tutorial on generating distribution archives.

Built Distribution

qns-0.1.5-py3-none-any.whl (108.6 kB view hashes)

Uploaded py3

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page