The realization of a large-scale Quantum Internet is a long-term objective of quantum information science, aiming to interconnect quantum devices through the reliable distribution of entanglement across long distances. Unlike classical networks, quantum networks are constrained by decoherence, probabilistic operations, and the no-cloning theorem, which fundamentally alter network design principles. This paper presents a new simulation-driven study of a scalable and resilient Quantum Internet architecture based on adaptive, entanglement-centric networking. A detailed system model is developed, incorporating realistic quantum channel noise, memory decoherence, and repeater-assisted communication. Novel fidelity-aware routing and resource-adaptive entanglement management strategies are evaluated through extensive simulations. Mathematical models, algorithmic pseudocode, and performance analysis demonstrate how adaptive control significantly improves end-to-end fidelity and network throughput under non-ideal conditions. The results provide actionable design insights for future experimental and large-scale quantum networking deployments.
The realization of a large-scale Quantum Internet is a long-term objective of quantum information science, aiming to interconnect quantum devices through the reliable distribution of entanglement across long distances. Unlike classical networks, quantum networks are constrained by decoherence, probabilistic operations, and the no-cloning theorem, which fundamentally alter network design principles. This paper presents a new simulation-driven study of a scalable and resilient Quantum Internet architecture based on adaptive, entanglement-centric networking. A detailed system model is developed, incorporating realistic quantum channel noise, memory decoherence, and repeater-assisted communication. Novel fidelity-aware routing and resource-adaptive entanglement management strategies are evaluated through extensive simulations. Mathematical models, algorithmic pseudocode, and performance analysis demonstrate how adaptive control significantly improves end-to-end fidelity and network throughput under non-ideal conditions. The results provide actionable design insights for future experimental and large-scale quantum networking deployments.
The scalability of quantum communication networks is crucial for enabling secure information exchange and distributed quantum computing across global infrastructures. However, achieving scalability remains a significant challenge due to the effects of decoherence, photon loss, and fidelity degradation over extended distances. This paper performs a critical investigation into the scalability limits of quantum communication networks, focusing on the interplay between entanglement generation rates, end-to-end fidelity, and network parameters such as node separation, noise levels, and total distance. Leveraging the SeQUeNCe simulation framework, we model adaptable trapped-ion repeater networks to uncover fundamental insights into both homogeneous and heterogeneous configurations. Our findings reveal transformative insights that help reshape system designs for near term quantum networks. We identify maximum node separation thresholds critical for practical implementations, illuminate the non-linear degradation of fidelity with increasing distance, and expose how routing and swapping protocols influence entanglement success rates at scale. Most notably, we demonstrate that while additional repeaters can mitigate distance-induced failures, excessive node counts introduce compounding fidelity losses that threaten network viability under certain architectures. These results provide a roadmap for revolutionizing quantum networks, with actionable design strategies such as optimized node placement, advanced routing algorithms, and the adoption of cutting-edge qubit technologies like topological qubits. By addressing key bottlenecks in quantum communication scalability, our work lays the foundation for the next generation of global quantum networks.
The quantum internet enables distribution of quantum states across distant nodes, supporting secure communication, distributed computing, and quantum sensing. Unlike classical networks, it is constrained by the no cloning theorem, probabilistic entanglement generation, decoherence, and hardware drift, making classical abstractions inadequate. Scalable quantum networking therefore requires new architectures, protocols, and optimisation methods that explicitly account for these limitations.
This thesis studies the architecture, routing, and operation of quantum networks under realistic constraints, focusing on bipartite entanglement distribution over quantum repeater networks. Key metrics include end to end fidelity, throughput, scalability, and fairness. At the network layer, routing strategies are developed beyond assumptions of homogeneous nodes and full network knowledge. Routing under heterogeneous repeater efficiencies shows how partial knowledge of node quality improves fidelity and reduces path blocking. A grey box routing approach is then introduced, where path selection relies only on topology and end to end estimates, achieving robustness and fairness without detailed link information.
At the link layer, calibration and hardware drift are addressed through a calibration aware model separating activation and calibration phases. For linear repeater chains, an optimal calibration schedule is derived to balance operation time and calibration overhead. This is extended to general topologies with shared links, where a greedy orchestration heuristic is proposed.
Finally, the thesis connects network protocols with hardware via an instruction set architecture for programmable quantum repeater nodes based on NV centers, enabling coherent programmability and linking physical operations to higher layer protocols.
Quantum communication networks—the substrate of a future quantum Internet—demand analytical tools that account for entanglement, fidelity, and quantum-specific constraints absent from classical models. In this paper, we introduce the Quantum Network Science (QNS) framework that adapts core network metrics to the quantum setting through fidelity-, success-probability-, and capacity-aware weighting. We formalize centrality (including Quantum PageRank and continuous-time quantum-walk variants), community structure on entanglement graphs, and robustness/percolation with fidelity thresholds. The framework is validated via analytic motifs and controlled simulations on Erdős–Rényi, scale-free, and small-world topologies, as well as satellite-assisted versus fiber-only designs. Our results show that (i) fidelity weighting reorders structural importance and can reconnect networks that appear fragmented classically; (ii) heavy-tailed degree patterns improve tolerance to random failures but heighten vulnerability to targeted hub attacks; (iii) small-world shortcuts induced by long-range quantum links shrink path lengths; and (iv) overlapping “connected components” emerge from entanglement swapping, motivating revised connectivity baselines. We also discuss design implications—degree caps and hub hardening, link-type diversity, multipath routing, and buffering policies—and outline extensions to temporal and multilayer modeling that couple the quantum plane with its classical control layer. QNS thus offers a principled, measurement-oriented foundation for analyzing, comparing, and engineering resilient, high-capacity quantum networks.
The integration of quantum networking with future Internet architectures introduces new challenges in resource optimization, routing, and network reliability. Traditional optimization techniques are often insufficient to handle the dynamic and probabilistic nature of quantum networks. This paper proposes a machine learning-assisted framework for quantum network optimization, where data-driven models are used to enhance decision-making in routing, resource allocation, and entanglement management. The approach combines classical learning algorithms with quantum network control mechanisms to improve adaptability and performance under varying network conditions. Experimental evaluation demonstrates improved efficiency in network utilization, reduced latency, and enhanced robustness compared to conventional methods. The results highlight the potential of machine learning-assisted optimization as a key enabler for scalable and intelligent quantum Internet infrastructures.
The quantum internet enables secure communication, distributed quantum computing, and remote sensing by distributing entanglement among distant nodes [1], [2]. However, establishing end-to-end entanglement across regional networks remains challenging because quantum links experience photon loss, entanglement generation is probabilistic, quantum memories have limited coherence times, and repeater resources are constrained [2], [3]. Conventional shortest-path routing does not jointly account for these quantum-specific limitations. This paper proposes a Fidelity- and Coherence-Aware Dynamic Entanglement Routing (FCADR) framework for regional quantum internet networks. The network is modelled as a graph containing end nodes, quantum repeaters, quantum memories, and lossy entanglement links. FCADR selects routes by jointly considering estimated end-to-end fidelity, entanglement-generation and swapping success probabilities, available quantum-memory capacity, and expected distribution delay. A discrete-event simulation framework is designed to compare FCADR with shortest-path routing, link-success-based routing, and fidelity-aware routing under varying channel loss, coherence time, traffic load, and node or link failures. Performance is evaluated using end-to-end fidelity, request-success rate, distribution delay, memory utilization, and network resilience. The framework aims to improve routing decisions by incorporating quantum-state quality and resource availability into dynamic path selection.
The efficacy of a communication network hinges upon both its physical architecture and the protocols that are employed within it. In the context of quantum communications, there exists a fundamental rate-loss trade-off for point-to-point quantum channels such that the rate for distributing entanglement, secret keys, or quantum states decays exponentially with respect to transmission distance. Quantum networks are the solution to overcome point-to-point limitations, but they simultaneously invite a challenging open question: How should quantum networks be designed to effectively and efficiently guarantee high rates? Now that performance and physical topology are inexorably linked, this question is not easy, but the answer is essential for a future quantum internet to be successful. In this work, we offer crucial insight into this open question for complex optical-fiber quantum networks. Using realistic descriptions of quantum networks via random network models and practical end-to-end routing protocols, we reveal critical phenomena associated with large-scale quantum networks. Our work reveals the weaknesses of applying single-path routing protocols within quantum networks, observing an inability to achieve reliable rates over long distances. Adapting algorithms for multipath routing, we employ an efficient and practical multipath routing algorithm capable of boosting performance while minimizing costly quantum resources.