Enabling quantum capabilities in future generations of networks requires integrating quantum hardware into existing network infrastructure and developing new protocols for quantum communications. However, the cost and inherent noise associated with quantum hardware necessitate an efficient deployment strategy with a minimal number of quantum repeaters and memories. In this paper, we introduce a comprehensive network planning framework for distributing quantum hardware across existing infrastructure, with the objective of maximizing quantum network utility within an entanglement distribution network. We showcase the results of our framework using a dumbbell network topology as a preliminary example and the SURFnet network as a real-world case study. We explore the impact of quantum memory multiplexing within quantum repeaters, as well as the influence of memory coherence time on quantum network utility. We further examine the effects of different fairness assumptions on network planning, unveiling their ramifications on real-time network performance.
Enabling quantum capabilities in future generations of networks requires integrating quantum hardware into existing network infrastructure and developing new protocols for quantum communications. However, the cost and inherent noise associated with quantum hardware necessitate an efficient deployment strategy with a minimal number of quantum repeaters and memories. In this paper, we introduce a comprehensive network planning framework for distributing quantum hardware across existing infrastructure, with the objective of maximizing quantum network utility within an entanglement distribution network. We showcase the results of our framework using a dumbbell network topology as a preliminary example and the SURFnet network as a real-world case study. We explore the impact of quantum memory multiplexing within quantum repeaters, as well as the influence of memory coherence time on quantum network utility. We further examine the effects of different fairness assumptions on network planning, unveiling their ramifications on real-time network performance.
Existing classical optical network infrastructure cannot be immediately used for quantum network applications due to photon loss. The first step towards enabling quantum networks is the integration of quantum repeaters into optical networks. However, the expenses and intrinsic noise inherent in quantum hardware underscore the need for an efficient deployment strategy that optimizes the placement of quantum repeaters and memories. In this paper, we present a comprehensive framework for network planning, aiming to efficiently distribute quantum repeaters across existing infrastructure, with the objective of maximizing quantum network utility within an entanglement distribution network. We apply our framework to several cases including a preliminary illustration of a dumbbell network topology and real-world cases of the SURFnet and ESnet. We explore the effect of quantum memory multiplexing within quantum repeaters, as well as the influence of memory coherence time on quantum network utility. We further examine the effects of different fairness assumptions on network planning, uncovering their impacts on real-time network performance.
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.
Future quantum networks will enable the interconnection of multiple users distributed across vast geographic distances. Due to these large separations and limited physical resources, communication will often rely on multi-path routing strategies, where physical resources are distributed across channels of varying lengths and delivered to end users. Efficient long-distance quantum communication therefore requires optimizing the allocation of these resources across available paths. In this work, we introduce a performance-oriented approach to quantum network routing by extending the classical concept of Quality of Service (QoS) to the context of multi-path quantum resources distribution. Unlike prior models that consider entanglement generation or quantum memory coherence in isolation, we investigate the interplay between path assignment strategies, coherence time constraints, and quantum error correction (QEC), and how they jointly impact end-to-end communication fidelity. We analyze both unencoded and encoded transmissions over aggregated network paths, quantifying the effects of resource allocation on transmission success. Our findings show that fidelity cannot be optimized independently of memory lifetimes, and that while QEC can enhance performance under specific conditions, it also imposes additional constraints depending on network topology and path-length asymmetries. This work provides a foundation for developing QoS-aware quantum routing protocols that balance fidelity, throughput, and memory utilization -- key considerations for near-term quantum repeater networks.
The distribution of entanglement in quantum networks is typically approached under idealized assumptions such as perfect synchronization and centralized control, while classical communication is often neglected. However, these assumptions prove impractical in large-scale networks. In this paper, we present a pragmatic perspective by exploring two minimal asynchronous protocols: a parallel scheme generating entanglement independently at the link level, and a sequential scheme extending entanglement iteratively from one party to the other. Our analysis incorporates non-uniform repeater spacings and classical communications and accounts for quantum memory decoherence. We evaluate network performance using metrics such as entanglement bit rate, end-to-end fidelity, and secret key rate for entanglement-based quantum key distribution. Our findings suggest the sequential scheme's superiority due to comparable performance with the parallel scheme, coupled with simpler implementation. Additionally, we impose a cutoff strategy to improve performance by discarding attempts with prolonged memory idle time, effectively eliminating low-quality entanglement links. Finally, we apply our methods to the real-world topology of SURFnet and report the performance as a function of memory coherence time.
Quantum communication networks are emerging as a promising technology that could constitute a key building block in future communication networks in the 6G era and beyond. These networks have an inherent feature of parallelism that allows them to boost the capacity and enhance the security of communication systems. Recent advances led to the deployment of small- and large-scale quantum communication networks with real quantum hardware. In quantum networks, entanglement is a key resource that allows for data transmission between different nodes. However, to reap the benefits of entanglement and enable efficient quantum communication, the number of generated entangled pairs must be optimized. Indeed, if the entanglement generation rates are not optimized, then some of these valuable resources will be discarded and lost. In this paper, the problem of optimizing the entanglement generation rates and their distribution over a quantum memory is studied. In particular, a quantum network in which users have heterogeneous distances and applications is considered. This problem is posed as a mixed integer nonlinear programming optimization problem whose goal is to efficiently utilize the available quantum memory by distributing the quantum entangled pairs in a way that maximizes the user satisfaction. An interior point optimization method is used to solve the optimization problem and extensive simulations are conducted to evaluate the effectiveness of the proposed system. Simulation results show the key design considerations for efficient quantum networks, and the effect of different network parameters on the network performance.
Quantum networks are envisioned to enable reliable distribution and manipulation of quantum information across distances, forming the foundation of a future quantum internet. The fair and efficient allocation of communication resources in such networks has been addressed through the quantum network utility maximization (QNUM) framework, which optimizes network utility under the assumption of predetermined routes for competing user demands. In this work, we relax this assumption and aim to identify optimal routes that correspond to the maximum achievable network utility. Specifically, we formulate the single-path utility-based entanglement routing problem as a Mixed-Integer Convex Program (MICP). The formulation is exact when negativity is chosen as the entanglement measure for utility quantification or the network supports sufficiently high entanglement generation rates across demands. For other entanglement measures considered, the formulation approximates the problem with over 99.99% accuracy on evaluated real-world examples. To improve computational tractability, we propose a randomized rounding-based heuristic and an upper bound via the relaxation of the MICP. Furthermore, based on min-congestion routing, we introduce an alternative randomized heuristic and upper bound. This heuristic is computationally faster, while both the heuristic and the upper bound often outperform their counterparts on considered real-world networks. Our work provides the framework for extending classical flow-based and quality of service-aware routing concepts to quantum networks.