Zunzheng Zhang | Xuanli Lin

Computing an Optimal Entanglement Path with Throughput and Fidelity Considerations

Thu, 27 Aug 2026 00:00:00 +0000

Abstract

Entanglement distribution is a core function of quantum networks essential for operations including teleportation, distributed quantum sensing, and multisite computation. Entanglement throughput and fidelity are two critical performance measures that depend on the quantum transmission along the links and swapping operations at the repeaters along the path. We study the problem of computing a end-to-end entanglement path that satisfies both fidelity and throughput requirements, leveraging qubit buffers at the nodes and considering the sequential swapping order. We show that the general problem of simultaneously satisfying both metrics to be NP-hard, and develop an algorithm to maximize throughput subject to a given fidelity threshold. We introduce the concepts of entanglement probability distribution and path domination and exploit them in the design of our algorithm. Extensive numerical results show that our algorithm can find optimal solutions in networks with thousands of nodes in less than a second. We also describe practical and possible implementation aspects of this algorithm in terms of devices and architecture support.

Published online as an Early Access article on August 27, 2026. Final volume and page numbers have not yet been assigned.

Multi-Pair Fidelity-Aware Rate Allocation in a Quantum Network: Approximation Schemes

Tue, 11 Aug 2026 00:00:00 +0000

Abstract

Entanglement distribution in quantum networks must jointly account for limited link capacities, probabilistic entanglement swapping, and heterogeneous link fidelities. In this paper, we study multi-pair fidelity-aware rate allocation in quantum networks. We formulate three rate-allocation problems: rate sum, rate sum subject to minimum-rate constraints, and max-min fairness. Prior work has studied a special case of the rate sum problem, where all links have identical fidelity. This special case admits a polynomial-time algorithm. We prove that all three problems are NP-hard. We then study optimization versions of these problems which maximize the minimum end-to-end fidelity subject to throughput or fairness requirements. We present fully polynomial-time approximation schemes (FPTAS) for solving these optimization problems. Experiments on randomly generated networks demonstrate the computational effectiveness of the proposed schemes.

Preprint, first submitted 2026-08-11.

Resource-Aware Intrusion Detection in Infrastructure Networks: A Game-Theoretic Approach

Fri, 07 Aug 2026 00:00:00 +0000

Abstract

Infrastructure networks increasingly rely on distributed sensing to detect intrusions before attackers reach valuable assets. Yet sensing devices, communication resources, and edge server capacity are limited, while intelligent attackers can adapt their routes to the deployed defense. Motivated by integrated sensing and communication (ISAC), we study how sensing and processing resources should be allocated under strategic interaction between a defender and an attacker. We formulate their interaction as a graph security game in which the defender deploys sensing actions under resource and false alarm constraints, while the attacker selects routes to valuable targets. We consider simultaneous play and settings in which the attacker observes either a pure defender configuration or a mixed defender strategy. Our analysis characterizes the existence, structure, and computational complexity of the Nash and Stackelberg equilibria, showing how the attacker’s observation of the defense affects equilibrium behavior and when optimal strategies become difficult to compute. We develop algorithms that construct effective pure configurations and refine restricted games for mixed Nash and mixed Stackelberg play. On enumerable instances, their solutions have small mean normalized differences from fully enumerated references; the methods also apply when exhaustive strategy enumeration is impractical. We also identify conditions under which Nash and mixed Stackelberg payoffs are ordered or coincide.

Preprint, first submitted 2026-08-07.

Task Offloading across Unreliable Edge Networks via Distributional Dynamic Programming

Mon, 18 May 2026 00:00:00 +0000

Abstract

As Internet of Things (IoT) networks evolve toward integrated sensing and communications (ISAC), ensuring reliable resource management at the mobile edge has become increasingly complex. The combination of limited communication and computing resources with unreliable wireless links necessitates highly robust offloading strategies. Traditional approaches that merely maximize expected network utility often fail to account for the stochastic link failures in dynamic sensing systems, resulting in severe utility degradation. To address this, we propose a risk-averse task offloading framework that systematically manages uncertainties in sensing-enabled mobile edge computing. We formulate a joint optimization problem that maximizes expected total task reward, subject to a conditional value-at-risk (CVaR) constraint, effectively mitigating the tail risk of communication task failures. We solve the resulting optimization problem via a novel distributional dynamic programming (DDP) approach. To improve computational efficiency, we integrate Pareto Frontier pruning based on first-order stochastic dominance (FSD) alongside scaling and rounding approximations. Evaluation results confirm that our framework successfully navigates the risk-reward trade-offs in resource-constrained deployments.

Dynamic Flow Routing and Scheduling for Time-Critical Network Services

Mon, 06 Oct 2025 00:00:00 +0000

Abstract

A key challenge in next-generation networks is providing intelligent network services to support time-sensitive applications such as AR/VR and the Internet of Military Things (IoMT). This work aims to answer the question: “How can we design a network service scheduling and flow routing scheme to satisfy the stringent demand and deadline requirements of tactical network applications?” We take a time-expanded graph-based approach to this study. Specifically, given a time-expanded graph constructed from an original network graph, we propose an optimization problem to find a subset of network services to maximize their total completion rewards such that the data of all services in this subset can be transmitted to their destination nodes by their deadlines. Unfortunately, solving this problem directly is challenging since it is a mixed-integer program. We propose a heuristic algorithm based on a sequential rounding approach where a linear programming (LP) feasibility problem is iteratively solved to update the network service subset, guided by a relaxed reward maximization problem that prioritizes high-reward requests. To further improve efficiency, we fine-tune the above LP feasibility problem from a static flow perspective, enabling fast feasibility checks on the original graph, and design the corresponding efficient heuristic algorithm. The simulation results demonstrate the trade-off among different approaches and validate the effectiveness of the proposed algorithms.