As practical quantum networks approach large-scale deployment, the need for efficient user-to-user frequency allocation is increasing, yet current approaches only provide partial solutions to the routing and spectrum allocation problem for an arbitrary quantum network. We address this challenge for repeater-less flex-grid quantum networks based on hyperentangled photons using an efficient three-stage pipeline combining leading tools in classical networking with recent advances in numerical optimization. First, double instantiations of Yen's algorithm obtain low-loss route candidates between each pair of users and the entanglement sources. Second, the advanced process optimizer (APOPT) obtains frequency channel allocations that maximize distribution rates under fidelity constraints. Finally, the constraint programming solver using satisfiability methods (CP-SAT) assigns specific frequency bins to each link, ensuring that there is no contention between frequencies from different sources. We numerically demonstrate this approach on a representative ring network and a Manhattan incumbent local exchange carrier topology, realizing significant improvements over prior genetic algorithm approaches in speed, accuracy, and scalability. Overall, this pipeline provides an efficient heuristic workflow for optimizing broadband entanglement distribution, applicable to arbitrarily connected quantum networks integrated within the existing lightwave infrastructure.
Free-space quantum communication provides a flexible complement to fiber-based quantum networks, but its point-to-point capacity is fundamentally limited by diffraction, atmospheric extinction and beam wandering induced by turbulence. In this work, we study the end-to-end performance of large-scale free-space quantum networks connecting randomly distributed fixed or mobile users, modelled as Waxman random graphs. We derive the mean network capacity, edge consumption and connectivity phase transitions for both single-path and multi-path (flooding) routing. We also study router-centered star networks, deriving the full distribution of end-to-end capacities as a function of the router's coverage radius. We then consider how performance may be improved by reinforcing free-space networks with a small number of optimally placed fiber-based backbone nodes. We prove that any optimal backbone configuration must correspond to a capacity-maximizing Voronoi tessellation of the network region, and show that this can be efficiently approximated by a centroidal Voronoi tessellation via Lloyd's algorithm, with backbone nodes connected according to a Delaunay triangulation. Numerical results show that even a modest number of backbone nodes substantially improves end-to-end capacity and reduces edge consumption for both mobile and fixed users.
A. Fletcher, Ignazio Pedone, S. Pirandola· 0 citations
This paper proposes an advanced resource allocation technique based on multi-objective optimization (MOO) to jointly optimize spectrum and power, mitigating nonlinear impairments and enhancing network performance, thereby improving the optical signal-to-noise ratio.
S. A. Silva, Carmelo J. A. Bastos-Filho, Danilo R. B. Araújo et al.· Journal of Microwaves, Optoe...· 0 citations
In quantum networks, routing mechanisms designed to deliver successfully multiple simultaneous requests to the destination under limited channel capacity should simultaneously consider resource usage optimization, entanglement-generation performance, and the quality of the end-to-end delivered entangled states. The routing approaches developed for quantum networks in the literature mostly consider the change in fidelity during routing. However, it should be noted that quantum states with the same fidelity value may have different levels of coherence. In this study, for the first time, a novel routing and purification approach for quantum networks is presented, using the end-to-end (E2E) relative entropy of coherence (REC) together with E2E fidelity to determine the purification level and the feasibility of candidate paths. In our study, the coherence- and fidelity-aware routing algorithm (CAFARA) is proposed. In CAFARA, the BBPSSW purification levels are determined for each candidate path and ordered from the lowest to the highest. This information is stored in a lookup table. Then, the lowest BBPSSW purification level that simultaneously satisfies the requested fidelity, REC, latency, and capacity constraints is selected. The E2E fidelity, REC, and required number of raw Bell pairs mentioned here are all stored together in the previously mentioned lookup table. This lookup table was constructed using imperfect initial Werner states, one-sided amplitude damping, Werner-state twirling, BBPSSW purification, and density-matrix-based entanglement swapping, and all calculations were completed before the routing process. As the final step, CAFARA selects the best path with the highest entanglement generation rate (EGR) from the selected feasible paths. In our study, the FARA-PostREC and FARA-NoREC algorithms were developed for comparison with CAFARA. While the FARA-PostREC algorithm uses the REC constraint during the final validation stage of the request, FARA-NoREC does not use any REC constraint; it uses only fidelity as the quality parameter of the paths and as the parameter for determining the purification level. In our simulations under varying link distance, channel capacity, network size, and request load, CAFARA achieved a better average request success rate than FARA-PostREC by reducing late-stage request drops related to coherence because it also uses REC during the purification-level determination stage while maintaining the required E2E fidelity and REC values. Although FARA-NoREC often accepts more requests and assumes that they are successfully delivered, the average final fidelity (AFF) and average final coherence (AFC) values of the requests considered successful are lower than those of the other algorithms; the AFC does not even satisfy the REC threshold. Overall, CAFARA provides a balanced trade-off between the quality-guaranteed request success rate, latency, purification overhead, and resource consumption.
H. S. D. Tunç, Joy Halder, Azita Hajizade et al.· Scientific Reports· 0 citations
This work proposes Double-Channel Graph Attention (DCGA), an end-to-end reinforcement learning framework that isolates network reachability and demand-service logic into separate graph channels and constructs valid routes using a simulator-coupled, constraint-informed decoder.
Dynamic entanglement distribution is a key requirement for scalable, multi-user and multi-protocol quantum networks. We demonstrate a metropolitan-scale entanglement-based quantum communication network enabled by a quantum reconfigurable optical add-drop multiplexer (q-ROADM), which dynamically distributes polarisation-entangled photon pairs from a broadband source to six users over deployed campus and metropolitan fibre. The network supports programmable full-mesh, partial-mesh and sliced sub-network configurations, enabling flexible allocation of entanglement resources according to link condition and service requirement. We demonstrate stable six-user full-mesh operation over more than 150 hours, compare full-mesh and time-shared partial-mesh strategies under different source and detector conditions, and realise quantum network slicing with optional/additional interconnection links. We also show that the same infrastructure can support different quantum protocols by showcasing Secure Inaugural Authentication-Transfer (SIAT) combined with Network flooding over multiple paths to improve the security of onboarding a new user. These results demonstrate a q-ROADM-enabled entanglement distribution architecture as a novel route towards reconfigurable, service-oriented quantum networking over optical fibre infrastructure.
Rui Wang, Marcus J. Clark, O. Alia et al.· 0 citations
Quantum memories are a critical component of entanglement-based quantum networks, enabling the storage and synchronisation of quantum states across dynamic links. However, current quantum memories have significantly lower capacity than the rate at which entanglement can be generated, making memory saturation a key bottleneck that reduces network efficiency and hinders the scaling of quantum networks. This problem is especially pronounced in dynamic satellite-based quantum networks, where short visibility windows constrain link availability. In this paper, we present a support entanglement-swapping algorithm that utilises leftover entanglement in quantum memories, thereby alleviating memory saturation and increasing network connectivity. Our algorithm combines two mathematical concepts, line graphs and maximum-cardinality matching, to select independent entanglement swap pairs without sharing any entanglement between concurrent swaps. This property ensures that the resulting changes to the network remain local and mutually independent, making the algorithm easy to integrate alongside any existing routing schemes without requiring network-wide coordination. We evaluate the algorithm through simulations on both static fibre-based networks and dynamic satellite networks. Across most configurations, our algorithm increases both the mean and the total number of entanglements shared between end nodes, while also increasing the network’s long-range connectivity. The ‘SwapWithToUse’ algorithm variant consistently provides the greatest improvements, with gains increasing as entanglement-generation rate increases.