8 research outputs found
QuNetSim: A Software Framework for Quantum Networks
As quantum internet technologies develop, the need for simulation software
and education for quantum internet rises. QuNetSim aims to fill this need.
QuNetSim is a Python software framework that can be used to simulate quantum
networks up to the network layer. The goal of QuNetSim is to make it easier to
investigate and test quantum networking protocols over various quantum network
configurations and parameters. The framework incorporates many known quantum
network protocols so that users can quickly build simulations and beginners can
easily learn to implement their own quantum networking protocols.Comment: 11 pages, 6 figure
Quantum Autoencoders for Learning Quantum Channel Codes
This work investigates the application of quantum machine learning techniques
for classical and quantum communication across different qubit channel models.
By employing parameterized quantum circuits and a flexible channel noise model,
we develop a machine learning framework to generate quantum channel codes and
evaluate their effectiveness. We explore classical, entanglement-assisted, and
quantum communication scenarios within our framework. Applying it to various
quantum channel models as proof of concept, we demonstrate strong performance
in each case. Our results highlight the potential of quantum machine learning
in advancing research on quantum communication systems, enabling a better
understanding of capacity bounds under modulation constraints, various
communication settings, and diverse channel models.Comment: Submitted to IEEE GLOBECOM 2023 and is subject to licence chang
Quantum key distribution in a packet-switched network
Abstract Packet switching revolutionized the Internet by allowing the efficient use of network resources for data transmission. In a previous work, we introduced packet switching in quantum networks as a path to the Quantum Internet and presented a proof-of-concept for its application to quantum key distribution (QKD). In this paper, we outline a three-step approach for key rate optimization in a packet-switched network. Our simulated results show that practical key rates may be achieved in a sixteen-user network with no optical storage capacity. Under certain network conditions, we may improve the key rate by using an ultra-low-loss fiber delay line to store packets during network delays. We also find that implementing cut-off storage times in a strategy analogous to real-time selection in free-space QKD can significantly enhance performance. Our work demonstrates that packet switching is imminently suitable as a platform for QKD, an important step towards developing large-scale and integrated quantum networks