Quantum networks rely on high-fidelity entanglement links, but achieving target fidelity often increases latency and Bell pair consumption due to purification. This paper proposes a cost-based scheduler that jointly optimizes path selection and purification round, along with two hop-level estimators (a Deep Neural Network classifier and a Bayesian optimizer) to predict the minimal purification rounds needed for target hop fidelity. The scheme flexibly adjusts final entanglement fidelity while minimizing latency, improving request success rates and efficient Bell pair usage. Simulations integrating purification, entanglement generation, and network-level scheduling show that our approach reduces mean latency by up to 8% and increases success rates by 14% compared to fixed-round purification with FIFO scheduling.
Quantum networks rely on high-fidelity entanglement links, but achieving target fidelity often increases latency and Bell pair consumption due to purification. This paper proposes a cost-based scheduler that jointly optimizes path selection and purification round, along with two hop-level estimators (a Deep Neural Network classifier and a Bayesian optimizer) to predict the minimal purification rounds needed for target hop fidelity. The scheme flexibly adjusts final entanglement fidelity while minimizing latency, improving request success rates and efficient Bell pair usage. Simulations integrating purification, entanglement generation, and network-level scheduling show that our approach reduces mean latency by up to 8% and increases success rates by 14% compared to fixed-round purification with FIFO scheduling.
Quantum networks must deliver end-to-end entanglement whose fidelity meets application requirements, which typically requires entanglement purification along multi-hop paths. Existing scheduling approaches either purify at the link level or optimize end-to-end fidelity for a static set of requests, and do not capture concurrent requests competing for network resources over time. This paper studies concurrent entanglement purification scheduling in a time-slotted quantum network with heterogeneous links, in which requests compete for link capacity and quantum memories, and each request may be served by multiple parallel paths for increased reliability. We formulate a mixed-integer linear programming (MILP) scheduler that maximizes the number of scheduled requests, with max-min path allocation as a fairness criterion, and develop reinforcement learning (RL) schedulers based on DQN, PPO, and SAC, whose rewards balance request success against Bell-pair consumption. Simulations under varying network load, topologies and target fidelity thresholds show that the RL schedulers achieve the highest success rates, improving on MILP by up to 6% and on Progressive Filling and Greedy heuristics by 16.5% and 19.5% respectively, at the cost of higher Bell pair consumption and the deferral of a small fraction of requests. MILP, in contrast, achieves the highest fairness and the lowest Bell pair consumption in most scenarios.
Entanglement purification is a fundamental operation to combat against quantum decoherence and improve the fidelity of the entanglement. When multiple rounds of entanglement purification are performed, the fidelity of the final entanglement depends on the way how we schedule entanglement purification operations at each round. In this paper, we formulate and analyze the optimal entanglement purification scheduling problem arising from this context. Our main results include an optimal link-level purification scheduling algorithm and a joint entanglement purification scheduling and routing algorithm to establish fidelity-guaranteed end-to-end entanglements with quasi-optimal throughput in quantum networks. Our algorithmic framework developed in this paper can serve as functional building blocks for entanglement provisioning to support high-fidelity quantum information transfer by exploiting the limited quantum resources in a cost-effective way in order to fully realize the unrivalled capabilities offered by quantum networks.
Entanglement-based quantum networks provide unconditionally secure quantum communication by distributing end-to-end entangled pairs. To reach such goal, entanglement swapping and purification are two necessary operations, which can expand the entanglement distance and improve the fidelity of entangled pairs, respectively. During the end-to-end entanglement distribution process, the execution sequence of these two operations has a significant impact on the final performance, such as throughput and fidelity. Thus, how to schedule the sequence of swapping and purification becomes a critical problem in quantum networks. Our work focuses on such scheduling problem and we devise an efficient solution with fidelity maintenance. In our article, we first propose a concurrent swapping method for any-length path to reduce delay. Secondly, by modeling the scheduling sequence as a permutation problem, we obtain an optimal scheduling principle called “prioritized purification (PP)”. Finally, to meet the fidelity requirement of the end-to-end entangled pairs, we propose a fidelity maintenance scheme based on PP (FMSPP). The simulation results show that the proposed FMSPP can achieve higher distribution rates and less resource overhead than other scheduling solutions, and the advantages are more significant when there are more hops on the path.
The core function of quantum networks is to establish high-fidelity quantum entanglement for long-distance communication. However, the main challenge is to efficiently allocate resources under limited conditions, maximize throughput, satisfy end-to-end (E2E) fidelity requirements, and prevent quantum decoherence caused by inefficient routing algorithms. Current research focuses on optimizing either throughput or fidelity, with a lack of approaches that optimize both simultaneously; furthermore, existing algorithms suffer from high computational complexity. To tackle these challenges, this study proposes a Satisfying Fidelity Threshold Routing and Adaptive Purification Strategy (SFTRAP). SFTRAP maximizes throughput for each request by selecting multiple paths and dynamically choosing links for entanglement purification based on the current state of link resources, thus minimizing throughput loss while satisfying fidelity threshold. The strategy also adaptively adjusts the number of purification rounds according to the fidelity threshold, thereby optimizing the time required for deep purification and enhancing algorithmic efficiency. For multi-request scenarios, SFTRAP employs a priority sorting mechanism that takes into account both path cost and path freedom, which refines request scheduling and path selection to create more efficient request combinations, thus further boosting the overall network throughput. Simulation results indicate that SFTRAP surpasses state-of-the-art methods in terms of both throughput and algorithmic efficiency, highlighting its potential for optimizing resources in quantum networks.
Many quantum-network applications require end-to-end Bell pairs whose fidelity exceeds a request-specific threshold, but existing entanglement routing algorithms either optimize only throughput without regard for fidelity or enforce fidelity guarantees using centralized controllers with global link-state knowledge. We present Q-GUARD, an online entanglement routing algorithm that enforces per-request fidelity thresholds within a distributed protocol model in which nodes exchange link-state information only with their k-hop neighbors. After link outcomes are realized in each slot, Q-GUARD builds per-link purification cost tables from realized Bell pairs, allocates per-hop fidelity targets using a Werner-state equal-split rule, and selects between candidate path segments using a segment-local expected-goodput (EXG) metric that jointly accounts for swap success, purification overhead, and resource availability. We also introduce Q-GUARD-WS, an extension that exploits per-link hardware quality estimates to allocate purification effort non-uniformly across hops. On synthetic 100-node topologies with heterogeneous link fidelity and stochastic BBPSSW purification, Q-GUARD raises the qualified success rate from under 20\% to over 85\% on 4-hop paths and nearly doubles the qualified service radius in Euclidean distance relative to throughput-only and naive-purification baselines, while Q-GUARD-WS provides additional throughput gains under high hardware heterogeneity.
Abstract Quantum networks can establish End-to-End (E2E) entanglement connections between two arbitrary nodes for various quantum applications through entanglement swapping. To support quantum applications reliably, quantum networks should establish E2E entanglement connections with desired entanglement fidelity by performing entanglement purification. The existing works provide mechanisms for link-level purification, which, however, leads to low throughput because of lacking consideration on purifications at network-level. (That is, efficiently allocating scarce resources to jointly purify entanglements under the situation with multiple concurrent requests in a network remains a critical but unsolved problem.) To address this problem, we analyze the cost of purification, design the E2E fidelity calculation method in detail, and propose a novel approach called Purification Scheduling Control (PSC). The basic idea of PSC is to determine the appropriate purification through jointly optimizing purification and resource allocation processes based on conflict avoidance. We conduct extensive experiments showing that PSC can maximize throughput under the fidelity requirement. This solution should spur the development of more applied reliable routing designs for quantum networks, combining quantum error correction, quantum applications, and computer network theory.