Future quantum networks will rely on control-plane decisions such as routing, scheduling, entanglement provisioning, purification, and memory allocation. Each decision depends on a timely and reliable estimate of the quality of the entanglement that a candidate path can deliver, with end-to-end fidelity as a central metric. Inspired by the optical-networking experience with quality-of-transmission (QoT) estimation, this article asks when a quantum controller should compute fidelity analytically, simulate it, or learn a surrogate model. The optical analogy is useful but incomplete: optical QoT estimation often predicts a scalar transmission margin, whereas quantum QoT estimation must account for probabilistic entanglement generation, random memory waiting times, decoherence, state consumption, and measurement constraints that can make fidelity a stochastic target. We propose a compute/simulate/learn framework for quantum QoT estimation and illustrate it with a simulation-based proof of concept. Our study shows that analytical composition can be sufficient in simple regimes, that simulation becomes necessary when stochastic memory effects shape the delivered fidelity distribution, and that lightweight learned surrogates can approximate simulator labels at much lower runtime cost for online control. We close with open challenges for deployment, including stochastic targets, sim-to-real transfer, data-generation cost, physics-informed learning, and generalization beyond individual paths to full network topologies.
Future quantum networks will rely on control-plane decisions such as routing, scheduling, entanglement provisioning, purification, and memory allocation. Each decision depends on a timely and reliable estimate of the quality of the entanglement that a candidate path can deliver, with end-to-end fidelity as a central metric. Inspired by the optical-networking experience with quality-of-transmission (QoT) estimation, this article asks when a quantum controller should compute fidelity analytically, simulate it, or learn a surrogate model. The optical analogy is useful but incomplete: optical QoT estimation often predicts a scalar transmission margin, whereas quantum QoT estimation must account for probabilistic entanglement generation, random memory waiting times, decoherence, state consumption, and measurement constraints that can make fidelity a stochastic target. We propose a compute/simulate/learn framework for quantum QoT estimation and illustrate it with a simulation-based proof of concept. Our study shows that analytical composition can be sufficient in simple regimes, that simulation becomes necessary when stochastic memory effects shape the delivered fidelity distribution, and that lightweight learned surrogates can approximate simulator labels at much lower runtime cost for online control. We close with open challenges for deployment, including stochastic targets, sim-to-real transfer, data-generation cost, physics-informed learning, and generalization beyond individual paths to full network topologies.
Future quantum networks will enable the interconnection of multiple users distributed across vast geographic distances. Due to these large separations and limited physical resources, communication will often rely on multi-path routing strategies, where physical resources are distributed across channels of varying lengths and delivered to end users. Efficient long-distance quantum communication therefore requires optimizing the allocation of these resources across available paths. In this work, we introduce a performance-oriented approach to quantum network routing by extending the classical concept of Quality of Service (QoS) to the context of multi-path quantum resources distribution. Unlike prior models that consider entanglement generation or quantum memory coherence in isolation, we investigate the interplay between path assignment strategies, coherence time constraints, and quantum error correction (QEC), and how they jointly impact end-to-end communication fidelity. We analyze both unencoded and encoded transmissions over aggregated network paths, quantifying the effects of resource allocation on transmission success. Our findings show that fidelity cannot be optimized independently of memory lifetimes, and that while QEC can enhance performance under specific conditions, it also imposes additional constraints depending on network topology and path-length asymmetries. This work provides a foundation for developing QoS-aware quantum routing protocols that balance fidelity, throughput, and memory utilization -- key considerations for near-term quantum repeater networks.
The unprecedented advancements in quantum technology have opened new prospects for the widespread adoption of quantum applications, placing new demands on the information transmission capabilities of large-scale quantum networks. Long-distance and stable entanglements are deemed as the lifeline in quantum network communication. However, some weaknesses, e.g., quantum decoherence, scarce quantum memory, and uneven-quality entanglement, of the quantum entanglement hinder the development. In this paper, we proposeSophon, an online transmission framework for quantum networks, which utilizes high-dimensional entanglements to concurrently transmit multi-qubit data to satisfy the transmission requirements of the real-time request set. We first model the quantum network with multi-qubit entanglement represented byWquantum state and then formulate the Entanglement Routing and Qubit Provisioning (ERQP) problem as a global-local optimization process. To solve theERQPproblem, we distributedly regard each network node as an RL agent for resource provisioning and extend the step-updating of the Markov Decision Process by introducing a centralized controller for entanglement route selection to optimize local and global objectives, respectively. Extensive simulations demonstrate, on the self-made simulation platform,Sophonachieves a21.89%−66.52%decrease in the communication cost, and is more robust on different scales of the network topology and the request set than the baselines.
In this work we consider a quantum network consisting of multiple interconnected quantum repeaters. In this network, data plane of communication is achieved by photon transmission over fibre cables, while the control plane which transmits results of measurements and control information is implemented over classical Internet. Important part of the control plane is the server which distributes connection requests and entanglement resources to nodes involved in the path. Together, data and control plane establish reliable multi hop links over the network deploying purification and swapping operations. We analyze the performance of such a network using probabilistic model of communication time between repeaters, rather than slotted time. We consider random arrivals of connection requests to each repeater and variable path lengths. We model a single repeater using theory ofM[x]/G/m+rqueues where each batch request encompasses heralding, purification, and swapping times pertaining to a single repeater node. To model the network, we have used the concept of Jackson's network over repeater nodes in the path. This model allows derivation of end-to-end entanglement delivery time as random distribution and calculation of probability of decoherence of qubits stored in quantum memories over the path. We considered different numbers of entanglements per request and different types of quantum memories. Results of this analysis are useful for dimensioning capacities of quantum repeaters, memories and links in order to avoid decoherence of quantum memories.
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.
Abstract When physical architectures become too complex for analytical study, numerical simulation proves essential to investigate quantum network behavior. Although highly informative, these simulations involve intricate numerical functions without known analytical forms, making traditional optimization techniques that assume continuity, differentiability, or convexity inapplicable. We introduce a more efficient computational framework that employs machine learning models as surrogates for the objective function. We demonstrate the effectiveness of our approach by applying it to three well-known optimization problems in quantum networking: allocating quantum memory across multiple nodes, tuning an experimental parameter in every physical link of a quantum entanglement switch, and finding effective protocol configurations in a large asymmetric quantum network. Our algorithm consistently outperforms Simulated Annealing and Bayesian optimization within the allotted time, improving results by up to 29% and 28%, respectively. Our framework will thus allow for more comprehensive quantum network studies, integrating surrogate-assisted optimization with existing quantum network simulators.
Quantum networks intersect a paradigmatic shift in communication technologies, offering unprecedented capabilities for secure communication and distributed computation through the exploitation of quantum mechanical phenomena.These networks facilitate revolutionary and unlimited of possibility applications such as quantum key distribution (QKD), distributed quantum computing (DQC), quantum teleportation (QT), quantum clock synchronization (QCS), etc. -all fundamentally predicated on quantum entanglement as their operational cornerstone.Moreover, on the contrary with classical computer communication protocol, quantum networks have the fundamental no-cloning theorem in the quantum mechanic prohibits conventional signal amplification techniques, necessitating the development of quantum-native routing strategies that operate within these unique constraints.However, the practical realization of quantum networks until now still faces a crucial challenge: The reliability and efficient distribution of highfidelity entangled quantum states across long distances.Due to this distribution, the process faces intrinsic limitations arising from quantum decoherence, photonic loss in transmission channels, and the inherently probabilistic nature of entanglement generation protocols.In quantum communication, fidelity serves as a fundamental metric, representing the degree of similarity or correlation between the actual distributed entangled state and an ideal maximally entangled Bell pair.Fidelity is a crucial measure of the quality of quantum states in a network since it directly affects the reliability and effectiveness of quantum communication protocols.High fidelity ensures that the transmitted entangled states maintain their quantum properties, closely resembling the ideal Bell pair, and are thus suitable for performing secure and efficient quantum operations.Latency, on the other hand, refers to the total time delay involved in the entire process of entanglement establishment, from the initial creation of the entangled pairs to the final verification and confirmation of the entanglement quality.This includes several distinct components: the physical propagation delay, which accounts for the time it takes for quantum information to travel through the communication channel (such as optical fibers); the entanglement generation time, which involves the probabilistic creation of entangled states between quantum nodes; and the quantum memory storage time, which reflects the duration for which quantum states I