Quantum computers in the noisy intermediate-scale quantum (NISQ) era are constrained by limited qubit counts, imperfect gate fidelities, and short coherence times. NISQ-era computers often have too few qubits to run many algorithms, and those that do run often have limited quantum advantage. Distributed quantum computing (DQC) is a promising approach to overcome this, where multiple quantum processors are interconnected through shared entanglement. This work serves as a system overview of an entanglement distribution system. We achieve this by examining a zero-added loss multiplexing (ZALM) entanglement generation source, simulating multi-hop, repeater-less networks, and various routing algorithms to distribute entanglement, and real-hardware quantum experiments that reconstruct entanglement links of degraded fidelity and simulate classical-communication delay. Network simulations evaluate routing and spectrum-allocation algorithms under entanglement-generation-imposed rate constraints, by evaluating both fairness and throughput across network sizes and topologies. We identify two polynomial-time approximation algorithms that perform well, or better than others under these metrics. Using both simulations and quantum hardware, we evaluate how degraded entanglement and communication latency affect teleportation-based distributed multipartite-entanglement-state construction. The results reveal the coupled influence of source rate, routing efficiency, fidelity, and classical delay on networked entanglement distribution and DQC performance, providing a reproducible framework for both experimental and simulated studies of near-term DQC.
Quantum computers in the noisy intermediate-scale quantum (NISQ) era are constrained by limited qubit counts, imperfect gate fidelities, and short coherence times. NISQ-era computers often have too few qubits to run many algorithms, and those that do run often have limited quantum advantage. Distributed quantum computing (DQC) is a promising approach to overcome this, where multiple quantum processors are interconnected through shared entanglement. This work serves as a system overview of an entanglement distribution system. We achieve this by examining a zero-added loss multiplexing (ZALM) entanglement generation source, simulating multi-hop, repeater-less networks, and various routing algorithms to distribute entanglement, and real-hardware quantum experiments that reconstruct entanglement links of degraded fidelity and simulate classical-communication delay. Network simulations evaluate routing and spectrum-allocation algorithms under entanglement-generation-imposed rate constraints, by evaluating both fairness and throughput across network sizes and topologies. We identify two polynomial-time approximation algorithms that perform well, or better than others under these metrics. Using both simulations and quantum hardware, we evaluate how degraded entanglement and communication latency affect teleportation-based distributed multipartite-entanglement-state construction. The results reveal the coupled influence of source rate, routing efficiency, fidelity, and classical delay on networked entanglement distribution and DQC performance, providing a reproducible framework for both experimental and simulated studies of near-term DQC.
Quantum computers offer computational advantages for specific problems over classical systems [8], but today's devices are limited by qubit counts and susceptibility to noise. Distributed quantum computing (DQC) addresses these constraints by interconnecting quantum processors via quantum networks to enable cooperative execution of quantum algorithms [4]. DQC can be realized either through quantum teleportation [3], which transfers qubit states across nodes, or through remote gates [7], which enact quantum operations between spatially separated qubits while preserving their physical allocation. Both approaches depend on pre-shared entanglement, which must be distributed across potentially multi-hop quantum networks via entanglement swapping [11]. In realistic settings, DQC performance is influenced by factors such as entanglement fidelity, quantum memory coherence time, and routing path length, yet the impact of these parameters on computational fidelity remains insufficiently understood. This work investigates these effects through simulation of distributed Grover circuits executed over quantum networks with 1-4 routers, incorporating entanglement swapping, network delays, and varying physical constraints to assess how network-level properties affect circuit-level performance.
We investigate how quantum computers can be used to emulate quantum networks and study their performance under practical impairments. In particular, we evaluate how degraded entanglement and communication latency affect teleportation-based distributed multipartite-entanglement-state construction. We model imperfect Bell-pair sources using depolarizing noise channels and classical communication delays using thermal relaxation. We implement the depolarization using Stinespring dilation, randomly applied Pauli errors, and quasi-probability decompositions, evaluating the latter two on IQM quantum hardware and all three in simulation. We then study the performance of the entanglement distribution under noise generated by the aforementioned models. Although these noise models are mathematically equivalent, we find that hardware constraints result in profound differences in the corresponding results, highlighting the importance of careful experiment design.
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.
Quantum computing has the potential to solve complicated problems that are impossible for classical servers. Nevertheless, the applications of current quantum processors are restricted by their limited qubit capacity. Distributed Quantum Computing (DQC) is promising to scale up the computing capability by interconnecting quantum processors and performing computing collectively. The network interconnecting quantum processors can impact the efficiency of DQC. In this paper, we analyze and compare the performance of various interconnection networks for DQC. First, we meticulously derive the success probabilities of entanglement generation and the fidelity of shared Bell states generated within three typical static networks: line, ring, and grid. In addition, we propose a switching network with a minimal number of switch stages and evaluate its performance in terms of probability and fidelity. Moreover, we conduct extensive simulations based on real-world parameters to compare the static and switching networks, and the results reveal that the switching network performs better and is more scalable.
Distributed Quantum Computing (DQC) offers a promising path toward scalable quantum computation by connecting multiple quantum processors through quantum and classical communication channels, helping overcome the limited qubit counts and noise of individual Noisy Intermediate-Scale Quantum (NISQ) devices. Beyond scaling, DQC has been shown to provide a provable computational advantage: Balliu et al. (STOC 2025) proved that certain locally checkable labeling problems require Ω(Δ) rounds classically in the LOCAL model, where Δ is the maximum degree of the graph, yet only a single round in the quantum-LOCAL model. This separation is unconditional, but a practical demonstration on real hardware had not previously been carried out. This thesis addresses that gap through two contributions. First, it covers the background needed to understand distributed quantum computing: quantum gates, entanglement and the GHZ state, classical distributed models such as LOCAL and CONGEST, locally checkable labeling problems, and the operating principles of superconducting quantum computers, including the practical challenges of connecting them across distances. Second, it presents a practical demonstration of the iterated GHZ game, the locally checkable labeling problem underlying the separation above. The quantum-LOCAL winning strategy is proven correct and implemented as a quantum circuit, executed on IQM’s superconducting quantum computers Garnet and Emerald via the IQM Resonance cloud service. Since physically distributed superconducting hardware is not yet publicly available, a chip-partitioning scheme is used, treating regions of a single chip as independent quantum nodes to emulate a distributed network. The results show that Round 1 games consistently exceed the 75% classical winning bound of the GHZ game, confirming that this win-rate quantum advantage is detectable under NISQ-era noise. In a deeper 3-round demonstration, performance degrades in later rounds, suggesting that demonstrating the advantage consistently across deeper circuits will require longer coherence times, higher gate fidelities, or error correction.
Distributed Quantum Computing (DQC) enables the execution of quantum circuits across multiple interconnected quantum processing units (QPUs), but requiring efficient qubit allocation and network topology design to optimize computational performance. Proper qubit allocation minimizes entanglement costs across QPUs, balances computational workload, and ensures efficient execution of quantum computing tasks. Meanwhile, network topology plays a crucial role in reducing entanglement routing complexity and communication overhead for remote quantum gate operations. In this paper, we propose a joint optimization framework for network topology design and qubit allocation in DQC to minimize the communication overhead. We formulate the problem as a tractable integer nonlinear programming model that explicitly incorporates entanglement routing, thereby ensuring a more tractable optimization process. To further improve computational efficiency, we present a partially linearized version of the problem, making it solvable using any classical optimization solver. Extensive simulations on both random and real-world quantum circuits validate the effectiveness of our proposed approach, demonstrating its capability to handle complex quantum circuits while reducing communication costs in DQC.