arXiv subjects

Gayane Vardoyan

Publications and source records attributed to Gayane Vardoyan.

At least 19 recordsLinked to original sources

arXiv2026

Utility-Based Path Selection and Configuration in Quantum Networks via Layered Shortest Paths

A path in a quantum network is a chain of repeaters that distributes entanglement between two users. Selecting a path requires balancing the rate and quality (e.g., fidelity) of the delivered entanglement, but these quantities, unlike standard routing metrics, compose non-additively. The problem is compounded by link-level configuration choices (e.g., distillation rounds or emitter brightness tuning), each trading rate against fidelity, so that a path's performance depends jointly on its route and its per-link settings. We cast this joint path selection and configuration problem as a shortest path computation on a layered graph whose layers track discretized end-to-end fidelity. A single run returns the full rate fidelity Pareto frontier, from which the path maximizing any nondecreasing utility function of rate and fidelity can be selected. We prove that for certain utility functions (including the secret key rate of BB84), the method is a fully polynomial time approximation scheme, returning a near-optimal path within a specified tolerance. We further characterize exactly when cheaper scalarization-based routing suffices: it is optimal for utility functions with convex fidelity profiles, but can be arbitrarily suboptimal otherwise (e.g., for step-like, sigmoidal utilities), whereas the layered method remains reliable in all cases.

quant-ph↗

arXiv2026

Quantum-Classical Coexistence Network Tomography

Quantum-classical coexistence networks (QCNs) share optical fiber between quantum and classical signals via wavelength-division multiplexing, offering a practical path to quantum communication over existing telecom infrastructure. However, co- and counter-propagating classical traffic introduce distinct depolarization noise, complicating channel characterization. We develop a tomography framework that infers per-link channel parameters of a QCN from end-to-end measurements alone. We first model each coexisting fiber by decomposing the signal evolution into photon loss, successful transmission, and three direction-dependent depolarization components. We then derive closed-form link-level estimators, and extend the approach to star-topology networks through a system of multiplicative equations across end-node pairs, together with a simple classical-signal-direction-switching protocol that resolves the remaining unknowns. On single-link experimental testbed data, we recover per-link depolarization probabilities accurately, with estimated process fidelities closely tracking the Bayesian-process-tomography baseline across multiple fiber lengths and wavelengths; residual gaps reflect the depolarization-only approximation. Absent a multi-link coexistence testbed, we validate the star-network estimators on emulated paths built from measured single-link channels. We further extend the framework in two directions: (i) a channel model that factorizes the coexisting fiber into a depolarizing-with-loss signal channel and a Raman-noise-injection channel on separate optical modes -- a completely-positive, trace-preserving tensor product -- whose link observables reduce exactly to our basic model; and (ii) a generalization to arbitrary topologies via a peeling algorithm (trees) and a least-squares estimator (meshes), validated by Monte-Carlo simulations on tree and cyclic-mesh networks.

cs.IT↗

arXiv2026

Efficient routing and spectrum allocation in arbitrary flex-grid entanglement networks

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.

quant-ph↗

arXiv2026

Reinforcement Learning for Quantum Network Control with Application-Driven Objectives

Optimized control of quantum networks is essential for enabling distributed quantum applications with strict performance requirements. In near-term architectures with constrained hardware, effective control may determine the feasibility of deploying such applications. Because quantum network dynamics are suitable for being modeled as a Markov decision process, dynamic programming and reinforcement learning (RL) offer promising tools for optimizing control strategies. However, key quantum network performance measures -- such as secret key rate in quantum key distribution -- often involve a non-linear relationship between interdependent variables that describe quantum state quality and generation rate. Such objectives are not easily captured by standard RL approaches based on additive rewards. We propose a novel gradient-based RL framework that directly optimizes non-linear, differentiable objective functions, while accounting for uncertainties introduced by classical communication delays. We evaluate this framework in the context of entanglement distillation between two quantum network nodes equipped with multiplexing capability, and demonstrate up to 20-23% improvement over heuristic baselines in certain parameter regimes. 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.

quant-ph↗

arXiv2026

Rate-Fidelity Tradeoffs in All-Photonic and Memory-Equipped Quantum Switches

Quantum entanglement switches are a key building block for early quantum networks, and a central design question is whether near-term devices should use only flying photons or also incorporate quantum memories. We compare two architectures: an all-photonic entanglement generation switch (EGS) that repeatedly attempts Bell-state measurements (BSM) without storing qubits, and a quantum memory-equipped switch that buffers entanglement and triggers measurements only when heralded connectivity is available (herald-then-swap control). These two designs trade off simple, memoryless operation that avoids decoherence and memory-induced latency against heralding-based control that buffers entanglement to use BSMs more efficiently. We formalize both models under a common hardware abstraction and characterize their achievable rate-fidelity regions, yielding a benchmarking methodology that translates hardware and protocol parameters into network-level performance. Numerical evaluation quantifies the rate-fidelity tradeoffs of both models, identifies operating regions in which each architecture dominates, and shows how hardware and protocol knobs can be tuned to meet application-specific targets.

quant-ph↗

arXiv2025

A Framework for Distributed Resource Allocation in Quantum Networks

We introduce a distributed resource allocation framework for the Quantum Internet that relies on feedback-based, fully decentralized coordination to serve multiple co-existing applications. We develop quantum network control algorithms under the mathematical framework of Quantum Network Utility Maximization (QNUM), where utility functions quantify network performance by mapping entanglement rate and quality into a joint optimization objective. We then introduce QPrimal-Dual, a decentralized, scalable algorithm that solves QNUM by strategically placing network controllers that operate using local state information and limited classical message exchange. We prove global asymptotic stability for concave, separable utility functions, and provide sufficient conditions for local stability for broader non-concave cases. To reduce control overhead and account for quantum memory decoherence, we also propose schemes that locally approximate global quantities and prevent congestion in the network. We evaluate the performance of our approach via simulations in realistic quantum network architectures. Results show that QPrimalDual significantly outperforms baseline allocation strategies, scales with network size, and is robust to latency and decoherence. Our observations suggest that QPrimalDual could be a practical, high-performance foundation for fully distributed resource allocation in quantum networks.

quant-ph↗

arXiv2025

Adaptive Policies for Resource Generation in a Quantum Network

Protocols for distributed quantum systems commonly require the simultaneous availability of $n$ entangled states, each with a fidelity above some fixed minimum $F_{\mathrm{app}}$ relative to the target maximally-entangled state. However, the fidelity of entangled states degrades over time while in memory. Entangled states are therefore rendered useless when their fidelity falls below $F_{\mathrm{app}}$. This is problematic when entanglement generation is probabilistic and attempted in a sequential manner, because the expected completion time until $n$ entangled states are available can be large. Motivated by existing entanglement generation schemes, we consider a system where the entanglement generation parameters (the success probability $p$ and fidelity $F$ of the generated entangled state) may be adjusted at each time step. We model the system as a Markov decision process, where the policy dictates which generation parameters $(p,F)$ to use for each attempt. We use dynamic programming to derive optimal policies that minimise the expected time until $n$ entangled states are available with fidelity greater than $F_{\mathrm{app}}$. We observe that the advantage of our optimal policies over the selected baselines increases significantly with $n$. In the parameter regimes explored, which are based closely on current experiments, we find that the optimal policy can provide a speed-up of as much as a factor of twenty over a constant-action policy. In addition, we propose a computationally inexpensive heuristic method to compute policies that perform either optimally or near-optimally in the parameter regimes explored. Our heuristic method can be used to find high-performing policies in parameter regimes where finding an optimal policy is intractable.

quant-ph↗

arXiv2025

Entanglement Routing over Networks with Time Multiplexed Repeaters

Quantum networks will be able to service consumers with long-distance entanglement by use of quantum repeaters that generate Bell pairs (or links) with their neighbors, iid with probability $p$ and perform Bell State Measurements (BSMs) on the links that succeed iid with probability $q$. While global link state knowledge is required to maximize the rate of entanglement generation between any two consumers, it increases the protocol latency due to the classical communication requirements and requires long quantum memory coherence times. We propose two entanglement routing protocols that require only local link state knowledge to relax the quantum memory coherence time requirements and reduce the protocol latency. These protocols utilize multi-path routing protocol and time multiplexed repeaters. The time multiplexed repeaters first generate links for $k$-time steps before performing BSMs on any pairs of links. Our two protocols differ in the decision rule used for performing BSMs at the repeater: the first being a static path based routing protocol and second a dynamic distance based routing protocol. The performance of these protocols depends on the quantum network topology and the consumers' location. We observe that the average entanglement rate and the latency increase with the time multiplexing block length, $k$, irrespective of the protocol. When a step function memory decoherence model is introduced such that qubits are held in the quantum memory for an exponentially distributed time with mean $μ$, an optimal $k$ ($k_\text{opt}$) value appears, such that for increasing $k$ beyond $k_{\rm opt}$ hurts the entanglement rate. $k_{\rm opt}$ decreases with $p$ and increases with $μ$. $k_{\rm opt}$ appears due to the tradeoff between benefits from time multiplexing and the increased likelihood of previously established Bell pairs decohering due to finite memory coherence times.

quant-ph↗

arXiv2025

Piecemaker: a resource-efficient entanglement distribution protocol

We introduce multipartite entanglement distribution protocols that use a quantum switch to deliver stabilizer states to a number of remote end users. As in existing schemes, the first step in our protocols involves Bell pair generation between the switch and each end user. However, unlike existing schemes that wait for all Bell pairs to be established before distributing the desired state -- for example, via a projective measurement -- our approach stores only a minimal subset of Bell pairs while processing every subsequent Bell pair immediately. In doing so, our protocols reduce the average Bell pair storage time compared to existing schemes, resulting in less cumulative noise as a direct consequence. On the theoretical side, our protocol design is grounded in the structure of vertex covers in graph states up to local complementation. Through a comprehensive numerical evaluation, we compare the fidelities of delivered states with those of a baseline scheme, for state sizes up to n = 50 qubits. Simulations also show that our protocols can achieve the critical fidelity threshold of 1/2 for multipartite entanglement in a wider range of depolarization rates and success probabilities of Bell-pair generation. Overall, our protocols always achieve an equal or higher fidelity of the distributed state, and can reduce infidelity by up to 45%.

quant-ph↗

arXiv2025

Leveraging Internet Principles to Build a Quantum Network

Designing an operational architecture for the Quantum Internet is challenging in light of both fundamental limits imposed by physics laws and technological constraints. Here, we propose a method to abstract away most of the quantum-specific elements and formulate a best-effort quantum network architecture based on packet switching, akin to that of the classical Internet. This reframing provides an opportunity to exploit the many available and well-understood protocols within the Internet context. As an illustration, we tailor and adapt classical congestion control and active queue management protocols to quantum networks, employing an architecture wherein quantum end and intermediate nodes effectively regulate demand and resource utilization, respectively. Results show that these classical networking tools can be effective in managing quantum memory decoherence and maintaining end-to-end fidelity around a target value.

quant-ph↗

arXiv2025

Service-the-Longest-Queue Among d Choices Policy for Quantum Entanglement Switching

An Entanglement Generation Switch (EGS) is a quantum network hub that provides entangled states to a set of connected nodes by enabling them to share a limited number of hub resources. As entanglement requests arrive, they join dedicated queues corresponding to the nodes from which they originate. We propose a load-balancing policy wherein the EGS queries nodes for entanglement requests by randomly sampling d of all available request queues and choosing the longest of these to service. This policy is an instance of the well-known power-of-d-choices paradigm previously introduced for classical systems such as data-centers. In contrast to previous models, however, we place queues at nodes instead of directly at the EGS, which offers some practical advantages. Additionally, we incorporate a tunable back-off mechanism into our load-balancing scheme to reduce the classical communication load in the network. To study the policy, we consider a homogeneous star network topology that has the EGS at its center, and model it as a queueing system with requests that arrive according to a Poisson process and whose service times are exponentially distributed. We provide an asymptotic analysis of the system by deriving a set of differential equations that describe the dynamics of the mean-field limit and provide expressions for the corresponding unique equilibrium state. Consistent with analogous results from randomized load-balancing for classical systems, we observe a significant decrease in the average request processing time when the number of choices d increases from one to two during the sampling process, with diminishing returns for a higher number of choices. We also observe that our mean-field model provides a good approximation to study even moderately-sized systems.

quant-ph↗

arXiv2024

Surrogate-guided optimization in quantum networks

We propose an optimization algorithm to improve the design and performance of quantum communication networks. When physical architectures become too complex for analytical methods, numerical simulation becomes essential to study quantum network behavior. Although highly informative, these simulations involve complex numerical functions without known analytical forms, making traditional optimization techniques that assume continuity, differentiability, or convexity inapplicable. Additionally, quantum network simulations are computationally demanding, rendering global approaches like Simulated Annealing or genetic algorithms, which require extensive function evaluations, impractical. We introduce a more efficient optimization workflow using machine learning models, which serve as surrogates for a given objective function. We demonstrate the effectiveness of our approach by applying it to three well-known optimization problems in quantum networking: quantum memory allocation for multiple network nodes, tuning an experimental parameter in all physical links of a quantum entanglement switch, and finding efficient protocol settings within a large asymmetric quantum network. The solutions found by our algorithm consistently outperform those obtained with our baseline approaches -- Simulated Annealing and Bayesian optimization -- in the allotted time limit by up to 18\% and 20\%, respectively. Our framework thus allows for more comprehensive quantum network studies, integrating surrogate-assisted optimization with existing quantum network simulators.

quant-ph↗

arXiv2024

On the Analysis of Quantum Repeater Chains with Sequential Swaps

We evaluate the performance of two-way quantum repeater chains with sequential entanglement swapping. Within the analysis we consider memory decoherence, gate imperfections, and imperfect link-level entanglement generation. Our main results include closed-form expressions for the average entanglement fidelity of the generated end-to-end entangled states. We generalize previous findings for the one-shot fidelity analysis and study the case where repeater chains serve end-to-end requests continuously. We provide solutions to the continuous request scenario by combining results from quantum information theory and queuing theory. Finally, we apply the formulas obtained to analyze the impacts of hardware parameters, i.e., coherence times and gate fidelity, and distance on the entanglement fidelity and secret key rate of homogeneous quantum repeater chains.

quant-ph↗

arXiv2024

An on-demand resource allocation algorithm for a quantum network hub and its performance analysis

To effectively support the execution of quantum network applications for multiple sets of user-controlled quantum nodes, a quantum network must efficiently allocate shared resources. We study traffic models for a type of quantum network hub called an Entanglement Generation Switch (EGS), a device that allocates resources to enable entanglement generation between nodes in response to user-generated demand. We propose an on-demand resource allocation algorithm, where a demand is either blocked if no resources are available or else results in immediate resource allocation. We model the EGS as an Erlang loss system, with demands corresponding to sessions whose arrival is modelled as a Poisson process. To reflect the operation of a practical quantum switch, our model captures scenarios where a resource is allocated for batches of entanglement generation attempts, possibly interleaved with calibration periods for the quantum network nodes. Calibration periods are necessary to correct against drifts or jumps in the physical parameters of a quantum node that occur on a timescale that is long compared to the duration of an attempt. We then derive a formula for the demand blocking probability under three different traffic scenarios using analytical methods from applied probability and queueing theory. We prove an insensitivity theorem which guarantees that the probability a demand is blocked only depends upon the mean duration of each entanglement generation attempt and calibration period, and is not sensitive to the underlying distributions of attempt and calibration period duration. We provide numerical results to support our analysis. Our work is the first analysis of traffic characteristics at an EGS system and provides a valuable analytic tool for devising performance driven resource allocation algorithms.

quant-ph↗

arXiv2024

On the Bipartite Entanglement Capacity of Quantum Networks

We consider the problem of multi-path entanglement distribution to a pair of nodes in a quantum network consisting of devices with non-deterministic entanglement swapping capabilities. Multi-path entanglement distribution enables a network to establish end-to-end entangled links across any number of available paths with pre-established link-level entanglement. Probabilistic entanglement swapping, on the other hand, limits the amount of entanglement that is shared between the nodes; this is especially the case when, due to architectural and other practical constraints, swaps must be performed in temporal proximity to each other. Limiting our focus to the case where only bipartite entangled states are generated across the network, we cast the problem as an instance of generalized flow maximization between two quantum end nodes wishing to communicate. We propose a mixed-integer quadratically constrained program (MIQCP) to solve this flow problem for networks with arbitrary topology. We then compute the overall network capacity, defined as the maximum number of EPR states distributed to users per time unit, by solving the flow problem for all possible network states generated by probabilistic entangled link presence and absence, and subsequently by averaging over all network state capacities. The MIQCP can also be applied to networks with multiplexed links. While our approach for computing the overall network capacity has the undesirable property that the total number of states grows exponentially with link multiplexing capability, it nevertheless yields an exact solution that serves as an upper bound comparison basis for the throughput performance of easily-implementable yet non-optimal entanglement routing algorithms. We apply our capacity computation method to several networks, including a topology based on SURFnet -- a backbone network used for research purposes in the Netherlands.

quant-ph↗

arXiv2024

Reconfigurable Intelligent Surface (RIS)-Assisted Entanglement Distribution in FSO Quantum Networks

Quantum networks (QNs) relying on free-space optical (FSO) quantum channels can support quantum applications in environments wherein establishing an optical fiber infrastructure is challenging and costly. However, FSO-based QNs require a clear line-of-sight (LoS) between users, which is challenging due to blockages and natural obstacles. In this paper, a reconfigurable intelligent surface (RIS)-assisted FSO-based QN is proposed as a cost-efficient framework providing a virtual LoS between users for entanglement distribution. A novel modeling of the quantum noise and losses experienced by quantum states over FSO channels defined by atmospheric losses, turbulence, and pointing errors is derived. Then, the joint optimization of entanglement distribution and RIS placement problem is formulated, under heterogeneous entanglement rate and fidelity constraints. This problem is solved using a simulated annealing metaheuristic algorithm. Simulation results show that the proposed framework effectively meets the minimum fidelity requirements of all users' quantum applications. This is in stark contrast to baseline algorithms that lead to a drop of at least 83% in users' end-to-end fidelities. The proposed framework also achieves a 64% enhancement in the fairness level between users compared to baseline rate maximizing frameworks. Finally, the weather conditions, e.g., rain, are observed to have a more significant effect than pointing errors and turbulence.

cs.NI↗

arXiv2024

Optimistic Entanglement Purification in Quantum Networks

Noise and photon loss encountered on quantum channels pose a major challenge for reliable entanglement generation in quantum networks. In near-term networks, heralding is required to inform endpoints of successfully generated entanglement. If after heralding, entanglement fidelity is too low, entanglement purification can be utilized to probabilistically increase fidelity. Traditionally, purification protocols proceed as follows: generate heralded EPR pairs, execute a series of quantum operations on two or more pairs between two nodes, and classically communicate results to check for success. Purification may require several rounds while qubits are stored in memories, vulnerable to decoherence. In this work, we explore the notion of optimistic purification in a single link setup, wherein classical communication required for heralding and purification is delayed, possibly to the end of the process. Optimism reduces the overall time EPR pairs are stored in memory. While this is beneficial for fidelity, it can result in lower rates due to the continued execution of protocols with sparser heralding and purification outcome updates. We apply optimism to the entanglement pumping scheme, ground- and satellite-based EPR generation sources, and current state-of-the-art purification circuits. We evaluate sensitivity performance to a number of parameters including link length, EPR source rate and fidelity, and memory coherence time. We observe that our optimistic protocols are able to increase fidelity, while the traditional approach becomes detrimental to it for long distances. We study the trade-off between rate and fidelity under entanglement-based QKD, and find that optimistic schemes can yield higher rates compared to non-optimistic counterparts, with most advantages seen in scenarios with low initial fidelity and short coherence times.

quant-ph↗

arXiv2023

A Control Architecture for Entanglement Generation Switches in Quantum Networks

Entanglement between quantum network nodes is often produced using intermediary devices - such as heralding stations - as a resource. When scaling quantum networks to many nodes, requiring a dedicated intermediary device for every pair of nodes introduces high costs. Here, we propose a cost-effective architecture to connect many quantum network nodes via a central quantum network hub called an Entanglement Generation Switch (EGS). The EGS allows multiple quantum nodes to be connected at a fixed resource cost, by sharing the resources needed to make entanglement. We propose an algorithm called the Rate Control Protocol (RCP) which moderates the level of competition for access to the hub's resources between sets of users. We proceed to prove a convergence theorem for rates yielded by the algorithm. To derive the algorithm we work in the framework of Network Utility Maximization (NUM) and make use of the theory of Lagrange multipliers and Lagrangian duality. Our EGS architecture lays the groundwork for developing control architectures compatible with other types of quantum network hubs as well as system models of greater complexity.

quant-ph↗