An Extensible Quantum Network Simulator Built on ns-3: Q2NS Design and Evaluation

• URL: http://arxiv.org/abs/2603.02857v1
• Date: Tue, 03 Mar 2026 11:05:38 GMT
• Title: An Extensible Quantum Network Simulator Built on ns-3: Q2NS Design and Evaluation
• Authors: Adam Pearson, Francesco Mazza, Marcello Caleffi, Angela Sara Cacciapuoti,
• Abstract summary: We present Q2NS, a modular and scalable quantum network simulator built on top of ns-3.<n>Q2NS supports multiple quantum state representations through a unified interface.<n>We provide a dedicated visualization tool that jointly captures physical and entanglement-enabled connectivity.
• Score: 4.00618497702806
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: As quantum networking hardware remains costly and not yet widely accessible, simulation tools are essential for the design and evaluation of quantum network architectures and protocols. However, designing a scalable and computationally efficient quantum network simulator is intrinsically challenging: i) quantum dynamics must be emulated on classical computing platforms while capturing the stateful and non-local nature of entanglement, a quantum resource without any classical networking analog; ii) quantum networking is inherently hybrid, as protocol execution also fundamentally depends on classical signaling. This makes a tight and faithful co-simulation of quantum operations and classical message exchanges a core requirement. In this light, we present Q2NS, a modular and extensible quantum network simulator, built on top of ns-3, designed to seamlessly integrate quantum-network primitives with ns-3's established classical protocol stack. Q2NS adopts a modular architecture that decouples protocol control logic from node- and channel-level operations, enabling rapid prototyping and adaptation across heterogeneous and evolving Quantum Internet scenarios. Q2NS natively supports multiple quantum state representations through a unified interface, allowing interchangeable state-vector, density-matrix, and stabilizer backends. We validate Q2NS through realistic use-case studies and comprehensive benchmarks, demonstrating superior computational efficiency over representative state-of-the-art alternatives, while preserving modeling flexibility. Finally, we provide a dedicated visualization tool that jointly captures physical and entanglement-enabled connectivity and supports entangled-state manipulations, facilitating an intuitive interpretation of entanglement dynamics and protocol behavior. Q2NS offers a flexible, open, and scalable simulation platform for advancing Quantum Internet research.

Related papers

• InterQnet: A Heterogeneous Full-Stack Approach to Co-designing Scalable Quantum Networks [6.4884341485131545]
  InterQnet-Achieve focuses on practical realizations of heterogeneous quantum networks.<n>InterQnet-Scale focuses on a systems study of architectural choices for scalable quantum networks.
  arXiv  Detail & Related papers  (2025-09-23T19:22:45Z)
• Quantum Visual Fields with Neural Amplitude Encoding [70.86293548779774]
  We introduce a new type of Quantum Implicit Neural Representation (QINR) for 2D image and 3D geometric field learning.<n>QVF encodes classical data into quantum statevectors using neural amplitude encoding grounded in a learnable energy manifold.<n>Our ansatz follows a fully entangled design of learnable parametrised quantum circuits, with quantum (unitary) operations performed in the real Hilbert space.
  arXiv  Detail & Related papers  (2025-08-14T17:59:52Z)
• Towards System-Level Quantum-Accelerator Integration [3.4486179803947254]
  We propose a vertically integrated quantum systems architecture that treats quantum accelerators and processing units as peripheral system components.<n>A central element is the Quantum Abstraction Layer (QAL) at operating system kernel level.<n>We present first results towards such an integrated architecture, including a virtual QPU model based on QEMU.
  arXiv  Detail & Related papers  (2025-07-25T12:30:42Z)
• SeQUeNCe GUI: An Extensible User Interface for Discrete Event Quantum Network Simulations [55.2480439325792]
  SeQUeNCe is an open source simulator of quantum network communication.<n>We implement a graphical user interface which maintains the core principles of SeQUeNCe.
  arXiv  Detail & Related papers  (2025-01-15T19:36:09Z)
• Control Flow Adaption: An Efficient Simulation Method For Noisy Quantum Networks [3.097454217793115]
  Quantum network research at both the software stack and hardware implementation level has become an exciting area of quantum information science.<n>We introduce a novel quantum network simulation method called control flow adaptation.<n>We have developed a prototype quantum network simulator, qns-3, as a module for ns-3.
  arXiv  Detail & Related papers  (2024-12-12T05:37:55Z)
• A Quantum-Classical Collaborative Training Architecture Based on Quantum State Fidelity [50.387179833629254]
  We introduce a collaborative classical-quantum architecture called co-TenQu. Co-TenQu enhances a classical deep neural network by up to 41.72% in a fair setting. It outperforms other quantum-based methods by up to 1.9 times and achieves similar accuracy while utilizing 70.59% fewer qubits.
  arXiv  Detail & Related papers  (2024-02-23T14:09:41Z)
• Feasible Architecture for Quantum Fully Convolutional Networks [4.849886707973093]
  We propose a feasible pure quantum architecture that can be operated on noisy intermediate-scale quantum devices. Our study represents the successful training of a pure quantum fully convolutional network and discusses advantages by comparing it with the hybrid solution.
  arXiv  Detail & Related papers  (2021-10-05T01:06:54Z)
• Tensor Network Quantum Virtual Machine for Simulating Quantum Circuits at Exascale [57.84751206630535]
  We present a modernized version of the Quantum Virtual Machine (TNQVM) which serves as a quantum circuit simulation backend in the e-scale ACCelerator (XACC) framework. The new version is based on the general purpose, scalable network processing library, ExaTN, and provides multiple quantum circuit simulators. By combining the portable XACC quantum processors and the scalable ExaTN backend we introduce an end-to-end virtual development environment which can scale from laptops to future exascale platforms.
  arXiv  Detail & Related papers  (2021-04-21T13:26:42Z)
• Hybrid quantum-classical classifier based on tensor network and variational quantum circuit [0.0]
  We introduce a hybrid model combining the quantum-inspired tensor networks (TN) and the variational quantum circuits (VQC) to perform supervised learning tasks. We show that a matrix product state based TN with low bond dimensions performs better than PCA as a feature extractor to compress data for the input of VQCs in the binary classification of MNIST dataset.
  arXiv  Detail & Related papers  (2020-11-30T09:43:59Z)
• Quantum Deformed Neural Networks [83.71196337378022]
  We develop a new quantum neural network layer designed to run efficiently on a quantum computer. It can be simulated on a classical computer when restricted in the way it entangles input states.
  arXiv  Detail & Related papers  (2020-10-21T09:46:12Z)
• SeQUeNCe: A Customizable Discrete-Event Simulator of Quantum Networks [53.56179714852967]
  This work develops SeQUeNCe, a comprehensive, customizable quantum network simulator. We implement a comprehensive suite of network protocols and demonstrate the use of SeQUeNCe by simulating a photonic quantum network with nine routers equipped with quantum memories. We are releasing SeQUeNCe as an open source tool and aim to generate community interest in extending it.
  arXiv  Detail & Related papers  (2020-09-25T01:52:15Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.