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IEEE Journal on Selected Areas in Communications

HWDSQP: A Historical Weighted and Dynamic Scheduling Quantum Protocol to Enhance Communication Reliability

Liwei Lin, Rongbo Ma, Zejian Wang, Zinuo Cai, Haochen Xu, Bo Zhang, Ruhui Ma, Rajkumar Buyya

Published May 8, 2025• 8 citations
Publisher

Abstract

Quantum computing holds the promise of solving problems difficult for classical computers. However, we are still in the era of Noisy Intermediate-Scale Quantum (NISQ) computers, necessary to establish effective distributed quantum communication protocols to distribute complex quantum computing tasks across different quantum computers for execution. Significant progress has been made in quantum communication technology, particularly in quantum path creation and resource scheduling. The establishment of quantum paths relies on quantum entanglement and quantum relay technologies, achieving long-distance, high-fidelity quantum state transmission through entanglement swapping between multiple relays. However, resources in quantum communication networks are limited and expensive, making efficient resource scheduling strategies crucial for improving overall network efficiency. To address these issues, we design a network protocol that includes the Historical Weighted Fidelity Routing (HWFR) algorithm and the Dynamic Multi-Priority Quantum Scheduling (DMPQS) algorithm to enhance communication reliability across quantum computers. Both algorithms aim to enhance the reliability of quantum links, optimize resource utilization, and adapt to dynamic changes in the links. The former algorithm dynamically selects the optimal path by considering factors such as link length, noise level, entanglement success rate, and quantum relay resource constraints, ensuring high-fidelity and reliable quantum communication. The latter dynamically adjusts request priorities based on the urgency of quantum service requests and fidelity requirements, optimizing resource utilization. Experimental results show that the proposed protocol performs excellently in terms of an average response time of requests and link utilization, effectively improving the utilization efficiency of network resources and the overall performance of the system.

DOI
10.1109/jsac.2025.3568051

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Quantum computing holds the promise of solving problems difficult for classical computers. However, we are still in the era of Noisy Intermediate-Scale Quantum (NISQ) computers, necessary to establish effective distributed quantum communication protocols to distribute complex quantum computing tasks across different quantum computers for execution. Significant progress has been made in quantum communication technology, particularly in quantum path creation and resource scheduling. The establishment of quantum paths relies on quantum entanglement and quantum relay technologies, achieving long-distance, high-fidelity quantum state transmission through entanglement swapping between multiple relays. However, resources in quantum communication networks are limited and expensive, making efficient resource scheduling strategies crucial for improving overall network efficiency. To address these issues, we design a network protocol that includes the Historical Weighted Fidelity Routing (HWFR) algorithm and the Dynamic Multi-Priority Quantum Scheduling (DMPQS) algorithm to enhance communication reliability across quantum computers. Both algorithms aim to enhance the reliability of quantum links, optimize resource utilization, and adapt to dynamic changes in the links. The former algorithm dynamically selects the optimal path by considering factors such as link length, noise level, entanglement success rate, and quantum relay resource constraints, ensuring high-fidelity and reliable quantum communication. The latter dynamically adjusts request priorities based on the urgency of quantum service requests and fidelity requirements, optimizing resource utilization. Experimental results show that the proposed protocol performs excellently in terms of an average response time of requests and link utilization, effectively improving the utilization efficiency of network resources and the overall performance of the system.
Abstract Quantum communication plays a key role in enabling distributed Noisy Intermediate-Scale Quantum (NISQ) systems; however, practical quantum networks are often affected by link fluctuations, limited quantum resources, and entanglement degradation. These factors make reliable routing and scheduling difficult, particularly in large-scale and dynamically changing environments. Classical methods such as Historical Weighted Fidelity Routing (HWFR) and Dynamic Multi-Priority Quantum Scheduling (DMPQS) generally perform well only when network conditions remain stable, and their performance degrades when entanglement success rates vary or relay nodes become congested. A major challenge therefore lies in operating under uncertain and time-varying fidelity conditions. Most existing approaches focus on either routing or scheduling in isolation and do not explicitly consider future fidelity variations, which can lead to inefficient resource usage and increased end-to-end latency. In this work, we propose a Deep Reinforcement Learning-based Quantum Communication Optimization Framework (DRL-QCOF) that jointly addresses routing and scheduling. The framework employs a Bi-LSTM-based Quantum Fidelity Prediction Network (QFPN) to estimate future link fidelity F_"pred" (t)from time-series features including link length, noise coefficient, entanglement success probability, and relay utilization. These predictions are then used by a Deep Reinforcement Learning-based Adaptive Quantum Scheduling Agent (DRAQS-Agent), implemented using an Actor–Critic architecture with a continuous reward formulation, to guide routing and scheduling decisions across distributed nodes. Simulation results indicate that the proposed approach improves the average link fidelity to a maximum of 0.88, compared to 0.78–0.86 achieved by existing methods. The end-to-end latency is reduced from 104 ms to 83 ms, while baseline schemes remain above 110 ms under the same conditions. In addition, the average response time is reduced to approximately 147 ms, resource utilization increases to 71–83%, and the success ratio improves from 93% to 97%, resulting in improved end-to-end reliability. The cumulative reward stabilizes at around 16.0, with convergence observed within 300 training episodes, and the achieved throughput reaches 118–121 entangled pairs per second, confirming the effectiveness of the proposed framework in dynamic NISQ network scenarios.
Scheduling in quantum networks involves strategically allocating quantum resources to maximize entanglement distribution efficiency and overall performance. Unlike classical networks, quantum systems lack conventional buffering mechanisms, making traditional scheduling paradigms less effective. Existing quantum network algorithms predominantly focus on routing algorithms to identify optimal entanglement paths, often neglecting timeslot scheduling for mitigating resource contention and transmission latency, particularly under multi-user environments. To address these challenges, we propose a routing algorithm tightly integrated with priority-based dynamic scheduling. The algorithm not only discovers efficient entanglement paths but also optimizes resource utilization for concurrent requests. Simulation results confirm that this approach conserves quantum resources, reduces latency, and substantially boosts network performance, providing a robust solution for multi-user quantum environments.
The development of quantum computing relies heavily on the effective execution of quantum algorithms. However, current Noisy Intermediate-Scale Quantum (NISQ) computers are plagued by limitations such as short coherence times and significant error rates, posing substantial challenges to scheduling and execution. This paper introduces a novel dynamic resource-aware scheduling algorithm designed to mitigate these challenges. The core of the algorithm lies in its ability to adapt to the fluctuating noise levels and coherence times of the quantum hardware, intelligently prioritizing tasks based on their estimated computational cost and error probability. We demonstrate that this adaptive approach significantly improves the potential for successful quantum algorithm execution on NISQ devices, offering a pathway towards more efficient and reliable quantum computation. The algorithm's effectiveness is evaluated through theoretical analysis and conceptual modeling, focusing on the optimization of resource allocation and task prioritization within the constraints of NISQ technology. This research contributes to the growing field of quantum resource management and scheduling, providing a framework for tackling the complexities inherent in operating quantum computers.
This paper explores the optimization of multi-path communication within quantum networks, aiming to enhance communication efficiency and reliability. The core concept involves distributing quantum information across multiple paths and employing an optimized algorithm to manage this distribution. The algorithm focuses on minimizing latency, maximizing throughput, and mitigating the effects of channel loss inherent in quantum communication. We present a novel approach utilizing a layered optimization strategy, combining shortest path routing with adaptive power allocation. Mathematical formulations are detailed, including metrics for path quality, latency, and overall network throughput. The proposed method offers a significant improvement over traditional single-path communication strategies, particularly in complex quantum network topologies. Simulation results (though not presented through experimental data) demonstrate a substantial increase in communication performance under various network conditions. Further research will explore integration with quantum error correction codes and dynamic network topology adaptation.
This paper proposes a dynamic routing protocol for quantum networks based on the fluctuating entanglement states between quantum nodes. The core of the protocol lies in real-time monitoring of entanglement strength and dynamically adjusting routing paths based on these changes. This approach aims to mitigate the risk of information loss due to entanglement decay, ultimately achieving high reliability in quantum routing. Unlike traditional protocols relying on fixed topologies, this protocol adapts to the dynamic nature of entanglement, enhancing routing efficiency and resilience. The protocol utilizes a graph-based representation of the quantum network and employs algorithms for entanglement assessment and path optimization. Mathematical models are presented to formalize the routing process, including the calculation of entanglement fidelity and path cost. The proposed system demonstrates improved routing performance and fault tolerance compared to static routing schemes. The key contribution is a dynamic, adaptive routing strategy that leverages the inherent properties of quantum networks for reliable quantum information transmission.
Quantum satellite networks offer a promising solution for achieving long-distance quantum communication by enabling entanglement distribution across global scales. This work formulates and solves the quantum satellite network scheduling problem by optimizing satellite-to-ground station pair assignments under realistic system and environmental constraints. Our framework accounts for limited satellite and ground station resources, fairness, entanglement fidelity thresholds, and real world non-idealities including atmospheric losses, weather and background noise. In addition, we incorporate the complexities of multi-satellite relays enabled via inter-satellite links. We propose an integer linear programming (ILP) based optimization framework that supports multiple scheduling objectives, allowing us to analyze tradeoffs between maximizing total entanglement distribution rate and ensuring fairness across ground station pairs. Our framework can also be used as a benchmark tool to measure the performance of other potential transmission scheduling policies.