Coordination in distributed systems is often hampered by communication latency, which degrades performance. Quantum entanglement enables correlations stronger than classically possible without communication. Such correlations manifest instantaneously upon measurement, irrespective of the physical distance separating the systems. We investigate the application of shared entanglement to a dual-objective optimization problem in a distributed system comprising two servers. The servers process both a continuously available, preemptible baseline task and incoming paired customer requests, to maximize the baseline task throughput subject to a Quality of Service (QoS) constraint on average customer waiting time. We present a rigorous analytical model demonstrating that an entanglement-assisted routing strategy allows the system to achieve higher baseline throughput compared to communication-free classical strategies, provided the baseline task's output exhibits sufficiently increasing returns with processing time. This advantage stems from entanglement enabling better coordination, which allows the system to satisfy the customer QoS constraint with a lower overall probability of splitting customer requests, leading to more favorable conditions for baseline task processing and thus higher throughput. We further show that the magnitude of this throughput gain is particularly pronounced for tasks exhibiting increasing returns, where output grows super-linearly with processing time. Our results identify optimization of scheduling in distributed systems as a novel application domain for near-term quantum networks.
Coordination in distributed systems is often hampered by communication latency, which degrades performance. Quantum entanglement enables correlations stronger than classically possible without communication. Such correlations manifest instantaneously upon measurement, irrespective of the physical distance separating the systems. We investigate the application of shared entanglement to a dual-objective optimization problem in a distributed system comprising two servers. The servers process both a continuously available, preemptible baseline task and incoming paired customer requests, to maximize the baseline task throughput subject to a Quality of Service (QoS) constraint on average customer waiting time. We present a rigorous analytical model demonstrating that an entanglement-assisted routing strategy allows the system to achieve higher baseline throughput compared to communication-free classical strategies, provided the baseline task's output exhibits sufficiently increasing returns with processing time. This advantage stems from entanglement enabling better coordination, which allows the system to satisfy the customer QoS constraint with a lower overall probability of splitting customer requests, leading to more favorable conditions for baseline task processing and thus higher throughput. We further show that the magnitude of this throughput gain is particularly pronounced for tasks exhibiting increasing returns, where output grows super-linearly with processing time. Our results identify optimization of scheduling in distributed systems as a novel application domain for near-term quantum networks.
Coordination in distributed systems is often hampered by communication latency, which degrades performance. Quantum entanglement offers fundamentally stronger correlations than classically achievable without communication. Crucially, these correlations manifest instantaneously upon measurement, irrespective of the physical distance separating the systems. We investigate the application of shared entanglement to a dual-work optimization problem in a distributed system comprising two servers. The system must process both a continuously available, preemptible baseline task and incoming customer requests arriving in pairs. System performance is characterized by the trade-off between baseline task throughput and customer waiting time. We present a rigorous analytical model demonstrating that when the baseline task throughput function is strictly convex, rewarding longer uninterrupted processing periods, entanglement-assisted routing strategies achieve Pareto-superior performance compared to optimal communication-free classical strategies. We prove this advantage through queueing-theoretic analysis, non-local game formulation, and computational certification of classical bounds. Our results identify distributed scheduling and coordination as a novel application domain for near-term entanglement-based quantum networks.
This paper proposes a novel approach to resource allocation in distributed computing systems utilizing the unique properties of quantum entanglement. Traditional resource allocation methods are often hampered by latency issues stemming from signal propagation delays within networks. This research introduces a system where quantum entanglement is directly harnessed to establish instantaneous correlations between computing nodes. These correlations enable real-time assessment of resource availability and demand, facilitating dynamic and optimized resource assignments. The core mechanism involves creating entangled pairs, followed by employing entanglement witness measurements to determine optimal allocations. The system fundamentally shifts from relying on communication-based routing to leveraging quantum correlations for optimization. This approach promises significantly reduced latency and improved overall system efficiency compared to conventional methods. Mathematical models are presented to illustrate the core concepts and theoretical performance bounds. The key performance indicators (KPIs) investigated include allocation latency, resource utilization, and overall system throughput. The results demonstrate the potential for entanglement-based resource allocation to outperform traditional approaches under various network conditions.
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.
In recent years, advances in quantum computing have been driven by substantial improvements in both the number and quality of qubits. As the field progresses, there is growing interest in interconnecting quantum systems to enable scalable computation through Distributed Quantum Computing (DQC) architectures. Consider a distributed quantum application executed across multiple Quantum Processing Units (QPUs) within a quantum data center, where remote gates require establishing entanglement between different QPUs. The creation of such end-to-end entanglement can lead to network congestion and resource contention. To address these challenges, we propose a resource management framework that maximizes fidelity-guaranteed throughput while satisfying dependency constraints. We first formulate the problem as a Mixed-Integer Linear Programming (MILP) model to provide a performance benchmark. Building on this, we develop efficient approximate scheduling algorithms that achieve performance comparable to the optimization solver. Although a trade-off exists between execution time and network throughput, simulation results demonstrate that one of the proposed strategies, Weighted Group Least Resource First (WGLRF), closely approximates the solver’s performance across most scenarios. These findings suggest that the lightweight strategy is sufficient for current DQC settings, offering a practical solution for managing remote-gate resource contention in distributed quantum circuits and improving overall system performance.
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 communication networks require transmission of high-fidelity, uncoded qubits for applications such as entanglement distribution and quantum key distribution. However, current implementations are constrained by limited buffer capacity and qubit decoherence, which degrades qubit quality while waiting in the buffer. A key challenge arises from the stochastic nature of qubit generation, there exists a random delay (D) between the initiation of a generation request and the availability of the qubit. This induces a fundamental trade off early initiation increases buffer waiting time and hence decoherence, whereas delayed initiation leads to server idling and reduced throughput.
We model this system as an admission control problem in a finite buffer queue, where the reward associated with each job is a decreasing function of its sojourn time. We derive analytical conditions under which a simple "no lag" policy where a new qubit is generated immediately upon the availability of buffer space is optimal. To address scenarios with unknown system parameters, we further develop a Bayesian learning framework that adaptively optimizes the admission policy. In addition to quantum communication systems, the proposed model is applicable to delay sensitive IoT sensing and service systems.