For the last decade, layered stacks have dominated the way of reasoning about architectures for quantum networks. However, layered architectures impose stringent design and timing constraints on quantum networks, adding additional latency to the time required to serve an entanglement generation request. Moreover, increasing delays from the layered approach to network control causes degradation of state, effectively minimizing achievable fidelities. In this work we simulate a resource-centric, task-based approach to quantum network control by utilizing a centralized controller. Using the SeQUeNCe quantum network simulator, we implement the centralized controller which tracks quantum memory availability across all nodes, and schedules objectives in an offline fashion using a priority-based scheduler. We evaluate the performance of this controller on multiple topologies (bottleneck, grid, star, caveman) of significant scale, with varying reservation patterns; thereby we demonstrate the viability of the resource-centric task-based quantum network control framework for scaling. Our simulation results show that the caveman and grid topologies have a higher fraction of delivered requests with low delay compared to the star topology, but with a higher fraction of highly delayed requests as well. Furthermore, we find a linear shift of the CDFs in terms of queue size for all topologies depending on the reservation delay. More interestingly, we conclude that the CDFs of priority queues for the star topology converge fast into saturation for increasing request arrival rates, demonstrating together with the other results that the framework is robust for high load scenarios in quantum networks.
For the last decade, layered stacks have dominated the way of reasoning about architectures for quantum networks. However, layered architectures impose stringent design and timing constraints on quantum networks, adding additional latency to the time required to serve an entanglement generation request. Moreover, increasing delays from the layered approach to network control causes degradation of state, effectively minimizing achievable fidelities. In this work we simulate a resource-centric, task-based approach to quantum network control by utilizing a centralized controller. Using the SeQUeNCe quantum network simulator, we implement the centralized controller which tracks quantum memory availability across all nodes, and schedules objectives in an offline fashion using a priority-based scheduler. We evaluate the performance of this controller on multiple topologies (bottleneck, grid, star, caveman) of significant scale, with varying reservation patterns; thereby we demonstrate the viability of the resource-centric task-based quantum network control framework for scaling. Our simulation results show that the caveman and grid topologies have a higher fraction of delivered requests with low delay compared to the star topology, but with a higher fraction of highly delayed requests as well. Furthermore, we find a linear shift of the CDFs in terms of queue size for all topologies depending on the reservation delay. More interestingly, we conclude that the CDFs of priority queues for the star topology converge fast into saturation for increasing request arrival rates, demonstrating together with the other results that the framework is robust for high load scenarios in quantum networks.
Quantum entanglement is the cornerstone of quantum network communication, yet the qubit resources required to establish it are severely limited. Moreover, users within the network impose strict delay constraints on entanglement requests due to various factors, such as the real-time requirements of upper-layer applications or the finite coherence time of userside quantum memories. Consequently, effectively scheduling scarce qubit resources to serve these time-sensitive requests presents a significant challenge. This difficulty is exacerbated by the partial observability of quantum systems and the time-varying nature of qubit generation rates, rendering traditional scheduling algorithms ineffective. Furthermore, while Deep Reinforcement Learning (DRL) offers a promising avenue for quantum network optimization, existing research primarily focuses on network-layer routing, overlooking the finer-grained entanglement scheduling problem subject to strict delay and resource constraints. To bridge this gap, we propose the Deep Quantum Request Scheduler (DQRS), a novel DRL-based algorithm designed to maximize service quality under these dual constraints. Specifically, DQRS integrates a spatio-temporal branching architecture and virtual action slots within an ActorCritic framework. This design enables robust scheduling with continuous actions while adaptively managing fluctuating qubit budgets. Extensive simulation results demonstrate that DQRS achieves up to 44 % performance improvement over traditional methods and a 4 % gain compared to state-of-the-art DRL benchmarks across various resource constraints and observability settings, validating the robustness of the proposed approach.
Quantum networks face a fundamental challenge absent in classical networks; finite memory coherence times mean that queuing delay directly degrades information quality, causing decoherence that has no classical analog. Existing routing protocols optimize for hop count or channel fidelity while treating scheduling delays as a secondary concern, leaving fidelity vulnerable to congestion-induced decoherence. We present a coherence-aware routing protocol that integrates decoherence constraints into path selection via a priority-modulated aging model derived analytically from Weighted Fair Queueing theory, where high-priority traffic receives compressed effective queue delay. We build a custom discrete-event simulator verified against NetSquid (across 104 states), and evaluate across two structurally diverse topology classes under identical physical parameters, isolating topology structure as the sole independent variable. In all experiments, our protocol is benchmarked against loss-only Dijkstra routing and FIFO scheduling as baselines. On topologies, P2 traffic achieves 96.08% fidelity under a 9x load increase with only 0.04 percentage point degradation, versus a 13.51 percentage point collapse for the loss-only baseline; P2 tail latency holds stable at 0.055 ms while Dijkstra degrades 5.5x. On Barabasi-Albert scale-free topologies, our protocol delivers a +4.12 percentage point fidelity advantage with 97.8% higher aggregate throughput at operational loads over the baseline, with a clearly characterized hub-saturation boundary at 160k QPS. FIFO scheduling provides negligible priority differentiation on both topology classes, providing strong evidence that coherence-aware routing-not scheduling alone is responsible for the observed QoS gains.
This paper investigates resource allocation for entanglement distribution in multi-node, multi-channel quantum networks at the metropolitan scale. A multi-slot quantum network simulation framework is developed across physical and network layers, incorporating heterogeneous link characteristics, limited quantum memories, request queuing, retry mechanisms, and concurrent entanglement distribution. Based on this framework, a centralized scheduling architecture is proposed to coordinate quantum memories, communication channels, and routing paths for multiple simultaneous entanglement requests. Three classes of resource allocation strategies are evaluated: heuristic approaches, a mixed-integer linear programming (MILP) optimization method, and a Proximal Policy Optimization (PPO)-based reinforcement learning approach. The heuristic schemes reveal fundamental trade-offs between request delay and entanglement success rate: Dynamic Efficient minimizes delay, Success Enhancement improves success probability through adaptive multi-path allocation, and Static Efficient provides a balance between these objectives. The MILP approach achieves optimized resource allocation by jointly considering request handling and multi-path assignment, while the PPO-based method learns adaptive scheduling policies to improve overall performance. Simulation results demonstrate that these approaches provide different performance advantages in terms of request delay, entanglement success rate, capacity utilization, and request handling rate, highlighting the trade-offs between efficiency and reliability in metropolitan-scale quantum network scheduling.
Sharing entanglement among multiple users remains a central challenge for scalable quantum networks. Recent work proposed an on-demand entanglement packet architecture in which a controller uses a Time Division Multiple Access (TDMA) approach to allocate network resources. Quantum nodes are assigned a periodic schedule that probabilistically fulfills application requests for end-to-end entanglements. The schedule is recomputed periodically using well-known algorithms, such as Earliest Deadline First (EDF). However, a static schedule offers limited flexibility when outcomes are stochastic and arrivals are asynchronous. To overcome this limitation, we propose an online scheduler that dynamically schedules, defers, retries, or drops entanglement distribution reservations. In our simulations, the dynamic scheduler achieves lower completion time, higher completion ratio, and higher throughput than the static baseline. Furthermore, when the network is overloaded, the dynamic scheduler continues to construct deadline-feasible schedules and degrades gracefully.
Quantum computing promises computational capabilities beyond those of classical systems by exploiting superposition, entanglement, and quantum interference. However, the scalability of current noisy intermediate-scale quantum (NISQ) devices is limited by physical and engineering constraints, restricting the number of available qubits. Distributed quantum computing (DQC) mitigates this limitation by interconnecting multiple quantum processing units (QPUs) via quantum networks capable of distributing high-fidelity entanglement. Realizing such networks requires overcoming probabilistic entanglement generation, finite memory coherence, photon loss, and architectural constraints. This dissertation conducts an in-depth and comprehensive study of resource allocation in quantum networks, aiming to support high-throughput and high-fidelity entanglement distribution over lossy infrastructures. Our work spans the quantum networking stack, from physical-layer entanglement distribution protocols to network planning, quantum storage architectures, and distributed quantum computing. We begin by introducing two asynchronous entanglement distribution protocols-Sequential and Parallel-designed for early-stage quantum networks without global synchronization. Unlike many prior models, our protocols account for classical communication delays and quantum memory decoherence, reflecting practical constraints. Through simulations on real-world topologies such as SURFnet, we show that simple mechanisms such as cutoff-based memory management and sequential asynchronous entanglement distribution protocols can deliver competitive performance while significantly simplifying coordination, narrowing the gap between theoretical proposals and deployable protocols. Next, we formulate quantum network planning as a utility maximization problem that jointly considers entanglement distribution rate and fidelity, treating repeater and memory placement as optimization variables. We propose scalable heuristics and mixed-integer formulations, evaluating them under realistic conditions such as finite coherence time of quantum memories, limited number of memories per repeaters and end nodes, and real network topologies such as ESNet. Our findings offer practical design guidance and highlight how optimal placements depend on underlying physical constraints. To support dynamic workloads and respond to traffic bursts, we propose the Quantum Storage Network (QSN) architecture, where storage nodes buffer EPR pairs in advance of demand as an overlay network. This temporal decoupling improves responsiveness and throughput. Simulations across multiple topologies show that QSNs reduce entanglement distribution service latency by up to 40% and provide improved robustness compared to non-overlay designs, underscoring the value of storage-aware planning in future quantum networks. Finally, we focus on DQC, where large-scale quantum circuits are executed across networked QPUs. We propose a window-based circuit partitioning algorithm that minimizes nonlocal gate execution while balancing load across QPUs. Building on this partitioning, we design an adaptive, dependency-aware entanglement generation scheduler that opportunistically initiates remote entanglement as soon as dependent gates are ready, while accounting for realistic constraints such as coherence limits, probabilistic link generation, and Bell-state measurement (BSM) contention. Applied to benchmark circuits such as QAOA and QFT, this joint approach reduces execution delay by up to 35%. We further perform a two-qubit gate fidelity-sensitivity analysis under to show when the distributed quantum computing can be beneficial. Results show that the number of QPUs significantly impacts sensitivity to non-local gate fidelity: with fewer QPUs, distributed execution can outperform monolithic execution under looser fidelity constraints, whereas with more QPUs, achieving the same advantage requires substantially higher non-local gate fidelities relative to local gate fidelities. These findings identify the parameter regimes in which distributed quantum computing is beneficial, mapping break-even regions (as a function of number of QPUs) and quantifying how much cross-QPU (non-local) gate fidelity must improve—or, equivalently, how much per-QPU local gate fidelity must improve under modularization—for distributed execution to outperform a monolithic baseline. Together, these contributions form a cohesive framework for resource allocation in quantum networks in order to build scalable, delay-tolerant quantum networks and distributed computing systems. By grounding architectural design in realistic physical assumptions, this work advances the state of the art from theoretical modeling to practical, deployable quantum infrastructure.
In this paper, a novel Deep Q-Network (DQN) based scheduling method to optimize delay time and fairness among entanglement requests in quantum repeater networks is proposed. The scheduling of requests determines which pairs of end nodes should be entangled during the current time slot, while other pairs are placed in a queue for future slots. However, existing research on quantum networking often relies on simple statistical models to capture the behavior of quantum hardware, such as the failure rate of establishing entanglement. Moreover, current quantum simulators do not support network behaviors, including handling, pending, and dropping requests. To bridge the gap between quantum deployments and network behaviors, in this paper a dynamic network model is presented, encompassing quantum simulations, random topologies, and user modeling. The DQN based scheduling scheme allows us to balance the conflicting objectives of minimizing delay time and maximizing fairness among these entanglement requests. The proposed technique was evaluated using simulations, with results showing that the proposed DQN achieves higher performance compared to Greedy, Proportional fair and FIFO scheduling schemes.