Model-Driven Digital Twin Framework for Quantum Networks
- URL: http://arxiv.org/abs/2607.10367v1
- Date: Sat, 11 Jul 2026 15:43:29 GMT
- Title: Model-Driven Digital Twin Framework for Quantum Networks
- Abstract summary: This paper argues that Model-Driven Engineering can provide a systematic basis for integrating and evolving heterogeneous artefacts.<n>A conceptual implementation case study illustrates this using SysML v2, QKD kit, an EMF-based controller, and SeQUeNCe.
- Score: 4.84052813172154
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Quantum networks are advancing towards larger and more operational infrastructures, yet their evaluation remains fragmented across heterogeneous physical platforms, simulators, protocols, and architectural abstractions. Current digital-twin studies for quantum networks mainly realise isolated capabilities or application-specific solutions rather than reusable system-level twins. This paper argues that Model-Driven Engineering (MDE) can provide a systematic basis for integrating and evolving these heterogeneous artefacts. It derives requirements for design-time evaluation and runtime synchronisation, and proposes a progression of architectures from code-driven and domain-model-driven solutions to point-to-point and hub-and-spoke integration. A conceptual implementation case study illustrates this using SysML v2, QKD kit, an EMF-based controller, and SeQUeNCe. The work provides a foundation for adaptable and interoperable digital twins for quantum networks.
Related papers
- Architectural scaling tradeoffs in modular 3D bosonic quantum processors [60.61253784194378]
We propose a three-dimensional bosonic quantum processor built from coupled-cavity modules linked by interconnect networks.<n>We compare nearest-neighbor, hub-based, and hybrid architectures in terms of interconnect count, communication distance, resource concentration, and implementation complexity.
arXiv Detail & Related papers (2026-08-22T02:31:24Z) - Resource Management in Heterogeneous Quantum Repeater Networks [0.0]
I explore whether it is possible to build a unified Quantum Internet architecture that supports different types of quantum repeaters.<n>I propose an architecture based on an existing recursive network design and programmable RuleSet-based protocols.<n>I introduce a new emitter-photon building block to bridge memory-based and all-photonic segments.
arXiv Detail & Related papers (2026-05-24T15:22:34Z) - Telecom World Models: Unifying Digital Twins, Foundation Models, and Predictive Planning for 6G [60.29349053952935]
We introduce the Telecom World Model(TWM) concept, an architecture for learned, action-conditioned, uncertainty-aware modeling of telecom system dynamics.<n>We propose a three-layer architecture, comprising a field world model for spatial environment prediction, a control/dynamics world model for action-conditioned Key Performance Indicator (KPI) trajectory prediction, and a telecom foundation model layer for intent translation and orchestration.
arXiv Detail & Related papers (2026-04-08T09:41:58Z) - Quantum Networking Fundamentals: From Physical Protocols to Network Engineering [1.431027896679779]
This tutorial provides a network-centric view of quantum networking.<n>We establish Software-Defined Quantum Networking (SDQN) as a prerequisite for scale.<n>We analyze Distributed Quantum AI (DQAI) over imperfect networks as a case study.
arXiv Detail & Related papers (2026-04-02T11:28:34Z) - An Extensible Quantum Network Simulator Built on ns-3: Q2NS Design and Evaluation [4.00618497702806]
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.
arXiv Detail & Related papers (2026-03-03T11:05:38Z) - Reinforcement Learning for Quantum Network Control with Application-Driven Objectives [53.03367590211247]
Dynamic programming and reinforcement learning offer promising tools for optimizing control strategies.<n>We propose a novel RL framework that directly optimize non-linear, differentiable objective functions.<n>Our work comprises the first step towards non-linear objective function optimization in quantum networks with RL, opening a path towards more advanced use cases.
arXiv Detail & Related papers (2025-09-12T18:41:10Z) - 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) - Foundation Neural-Networks Quantum States as a Unified Ansatz for Multiple Hamiltonians [39.47297975798672]
Foundation Neural-Network Quantum States (FNQS) is an integrated paradigm for studying quantum many-body systems.<n>FNQS leverage key principles of foundation models to define variational wave functions based on a single, versatile architecture.<n>FNQS can generalize to physical Hamiltonians beyond those encountered during training.
arXiv Detail & Related papers (2025-02-13T16:52:07Z) - A Design Framework for the Simulation of Distributed Quantum Computing [2.969582361376132]
Growing demand for large-scale quantum computers is pushing research on Distributed Quantum Computing (DQC)
Recent experimental efforts have demonstrated some of the building blocks for such a design.
DQC systems are clusters of quantum processing units connected by means of quantum network infrastructures.
arXiv Detail & Related papers (2023-06-20T13:52:05Z) - A performance characterization of quantum generative models [35.974070202997176]
We compare quantum circuits used for quantum generative modeling.
We learn the underlying probability distribution of the data sets via two popular training methods.
We empirically find that a variant of the discrete architecture, which learns the copula of the probability distribution, outperforms all other methods.
arXiv Detail & Related papers (2023-01-23T11:00:29Z) - Solving Coupled Differential Equation Groups Using PINO-CDE [42.363646159367946]
PINO-CDE is a deep learning framework for solving coupled differential equation groups (CDEs)
Based on the theory of physics-informed neural operator (PINO), PINO-CDE uses a single network for all quantities in a CDEs.
This framework integrates engineering dynamics and deep learning technologies and may reveal a new concept for CDEs solving and uncertainty propagation.
arXiv Detail & Related papers (2022-10-01T08:39:24Z) - QuaSiMo: A Composable Library to Program Hybrid Workflows for Quantum
Simulation [48.341084094844746]
We present a composable design scheme for the development of hybrid quantum/classical algorithms and for applications of quantum simulation.
We implement our design scheme using the hardware-agnostic programming language QCOR into the QuaSiMo library.
arXiv Detail & Related papers (2021-05-17T16:17:57Z) - Composable Programming of Hybrid Workflows for Quantum Simulation [48.341084094844746]
We present a composable design scheme for the development of hybrid quantum/classical algorithms and for applications of quantum simulation.
We implement our design scheme using the hardware-agnostic programming language QCOR into the QuaSiMo library.
arXiv Detail & Related papers (2021-01-20T14:20:14Z) - Deep Multi-Task Learning for Cooperative NOMA: System Design and
Principles [52.79089414630366]
We develop a novel deep cooperative NOMA scheme, drawing upon the recent advances in deep learning (DL)
We develop a novel hybrid-cascaded deep neural network (DNN) architecture such that the entire system can be optimized in a holistic manner.
arXiv Detail & Related papers (2020-07-27T12:38:37Z)
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.