Quantum networks are usually framed by two hardware limits: how fast entanglement can be heralded, and how long a memory can hold it. We establish a third, geometric limit: entanglement decoheres while control information travels, so the distance to whoever allocates resources enters the fidelity budget directly. We develop a functional-graph abstraction weighting each link by its predicted multiplicative contribution to end-to-end fidelity, separating global but delayed state from local and immediate state, and compare centralized against local allocation in a replicated discrete-event model. Centralized delivery latency grows with network diameter while local latency is nearly scale invariant, differing up to twenty-fold for short-range traffic; the coherence threshold at which a global view outweighs a fresh one does not shift across the tested sizes; and as heralding accelerates, the dominant cost moves into the control plane. Slot synchronization instead obeys the product of slot rate and one-way link delay, so it is bounded by repeater spacing rather than diameter. Controller placement, decision locality, repeater spacing and slot rate are physical design parameters, not implementation details.
Quantum networks are usually framed by two hardware limits: how fast entanglement can be heralded, and how long a memory can hold it. We establish a third, geometric limit: entanglement decoheres while control information travels, so the distance to whoever allocates resources enters the fidelity budget directly. We develop a functional-graph abstraction weighting each link by its predicted multiplicative contribution to end-to-end fidelity, separating global but delayed state from local and immediate state, and compare centralized against local allocation in a replicated discrete-event model. Centralized delivery latency grows with network diameter while local latency is nearly scale invariant, differing up to twenty-fold for short-range traffic; the coherence threshold at which a global view outweighs a fresh one does not shift across the tested sizes; and as heralding accelerates, the dominant cost moves into the control plane. Slot synchronization instead obeys the product of slot rate and one-way link delay, so it is bounded by repeater spacing rather than diameter. Controller placement, decision locality, repeater spacing and slot rate are physical design parameters, not implementation details.
The scalability of quantum communication networks is crucial for enabling secure information exchange and distributed quantum computing across global infrastructures. However, achieving scalability remains a significant challenge due to the effects of decoherence, photon loss, and fidelity degradation over extended distances. This paper performs a critical investigation into the scalability limits of quantum communication networks, focusing on the interplay between entanglement generation rates, end-to-end fidelity, and network parameters such as node separation, noise levels, and total distance. Leveraging the SeQUeNCe simulation framework, we model adaptable trapped-ion repeater networks to uncover fundamental insights into both homogeneous and heterogeneous configurations. Our findings reveal transformative insights that help reshape system designs for near term quantum networks. We identify maximum node separation thresholds critical for practical implementations, illuminate the non-linear degradation of fidelity with increasing distance, and expose how routing and swapping protocols influence entanglement success rates at scale. Most notably, we demonstrate that while additional repeaters can mitigate distance-induced failures, excessive node counts introduce compounding fidelity losses that threaten network viability under certain architectures. These results provide a roadmap for revolutionizing quantum networks, with actionable design strategies such as optimized node placement, advanced routing algorithms, and the adoption of cutting-edge qubit technologies like topological qubits. By addressing key bottlenecks in quantum communication scalability, our work lays the foundation for the next generation of global quantum networks.
As quantum networks move toward multi-user architectures, a central question is how the performance of entanglement distribution scales with the numbers of network nodes N and active users M, yet existing studies use different objectives, resource assumptions, and normalizations. To account for delivery distance under multi-user demand, we define entanglement transport, which weights each delivered Bell pair meeting a fixed fidelity threshold by its endpoint separation. In a two-layer framework, (i) a resource layer measures capacity per activation slot under different aggregation regimes, and (ii) a physical layer measures service per second under implementation constraints. For matched implementations with a bounded number of activation slots per second, asymptotic-aggregation capacity sets a service ceiling, whereas achievable service must be established separately. On expanding honeycomb lattices with one random session, fixed-window entanglement-percolation constructions achieve Θ(N−1) entanglement transport per activation slot below their thresholds and Θ(N) above them. For homogeneous chains, asymptotic aggregation exceeds fixed-window capacity by a factor Θ(N2/M), and in spatial expansion a noisy physical implementation achieves Θ(M/N) service per second. These examples show that entanglement-transport scaling depends not only on connectivity but also on resource aggregation and physical implementation. The framework provides a common basis on which quantum-network scaling laws can be established.
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.
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.
In quantum networks, long-distance quantum communication relies on Einstein–Podolsky–Rosen (EPR) pairs. However, EPR pairs undergo decoherence over time, while their on-demand generation incurs latency. Pre-Storing EPR pairs in quantum memories can mitigate this latency, but such memories are costly and scarce resources, making their efficient placement an important design problem. While prior studies have mainly focused on routing or entanglement distribution, the placement of quantum memories under fidelity and purification constraints has received limited attention. In this paper, we propose an optimization framework for quantum memory placement that minimizes the average entanglement generation latency under a limited budget of deployable memories. Simulations across multiple real-world topologies show that our method consistently achieves lower latency than random and centrality-based placements, suggesting its potential to improve the scalability of large-scale quantum communication networks.
Efforts to scale quantum hardware are commonly framed as a challenge of suppressing local decoherence and stochastic noise through improved physical qubits and quantum error correction. Yet large-scale devices routinely exhibit performance degradation inconsistent with bounded-complexity local noise models, including nonstationary drift, bursty correlated events, and long-range crosstalk. We propose a constraint-based scaling framework in which the dominant limitation to scalability is not local error amplitude but the accumulation of constraint geometry: structured, correlated degrees of freedom that grow with system size and cannot be absorbed by standard endogenous noise models. We formalize this via (i) a bounded-complexity endogenous model class, (ii) a structured residual observable isolating unexplained degradation, and (iii) a coherence budget comparing effective control capacity to constraint burden. We identify a regime transition-the coherence wall-at which constraint burden outpaces control capacity, leading to rapid performance collapse despite stable local fidelities. We provide operational measurement protocols for extracting structured residual scaling, correlated dimensionality, and perturbation sensitivity from standard benchmarking data. The framework yields falsifiable architectural predictions and offers a diagnostic layer bridging quantum theory and scalable quantum engineering.