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International Telecommunication Networks and Applications Conference (ITNAC)

SPEP: Adaptive Resource Orchestration in Quantum Networks through Strategic Entanglement Pre-Positioning

Vineet Kumar Dwivedi, Vivek Shukla, Chandrashekar Jatoth, Rajesh Doriya, Rajkumar Buyya

Published Nov 26, 2025• 1 citation
Publisher

Abstract

Quantum networks promise unprecedented capabilities for secure communication and distributed quantum computing through entanglement distribution. However, the fragile nature of quantum entanglement and its exponential decay present significant challenges for efficient resource allocation. This paper introduces SPEP (Strategic Predictive Entanglement Pre-positioning), a novel framework that leverages machine learning techniques to anticipate communication demands and proactively establish entangled links. We implement and evaluate multiple prediction models including LSTM neural networks and ARIMA time series analysis, comparing them against traditional reactive and greedy routing approaches. Through comprehensive simulations across four distinct network topologies with over multiple simulation runs, our SPEP framework demonstrates significant improvement in success rates, $15 \%$ reduction in average latency, and $25 \%$ better resource utilization compared to baseline methods. The results provide strong evidence that machine learning-driven prediction can significantly enhance quantum network performance, particularly in networks with temporal demand patterns.

DOI
10.1109/itnac66378.2025.11302511

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Quantum networks promise unprecedented capabilities for secure communication and distributed quantum computing through entanglement distribution. However, the fragile nature of quantum entanglement and its exponential decay present significant challenges for efficient resource allocation. This paper introduces SPEP (Strategic Predictive Entanglement Pre-positioning), a novel framework that leverages machine learning techniques to anticipate communication demands and proactively establish entangled links. We implement and evaluate multiple prediction models including LSTM neural networks and ARIMA time series analysis, comparing them against traditional reactive and greedy routing approaches. Through comprehensive simulations across four distinct network topologies with over multiple simulation runs, our SPEP framework demonstrates significant improvement in success rates, 15%15 \%15% reduction in average latency, and 25%25 \%25% better resource utilization compared to baseline methods. The results provide strong evidence that machine learning-driven prediction can significantly enhance quantum network performance, particularly in networks with temporal demand patterns.
The integration of quantum networking with future Internet architectures introduces new challenges in resource optimization, routing, and network reliability. Traditional optimization techniques are often insufficient to handle the dynamic and probabilistic nature of quantum networks. This paper proposes a machine learning-assisted framework for quantum network optimization, where data-driven models are used to enhance decision-making in routing, resource allocation, and entanglement management. The approach combines classical learning algorithms with quantum network control mechanisms to improve adaptability and performance under varying network conditions. Experimental evaluation demonstrates improved efficiency in network utilization, reduced latency, and enhanced robustness compared to conventional methods. The results highlight the potential of machine learning-assisted optimization as a key enabler for scalable and intelligent quantum Internet infrastructures.
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This paper presents a novel algorithm for optimizing the allocation of entangled resources within quantum networks. The core of the approach lies in employing a reinforcement learning (RL) framework to dynamically manage and distribute entanglement, addressing the inherent challenges of maximizing utilization and enhancing quantum communication efficiency. Unlike traditional static allocation methods, the proposed RL agent learns optimal allocation strategies through iterative interaction with a simulated quantum network environment. The algorithm considers factors such as node connectivity, qubit availability, and communication delays to minimize resource wastage and improve overall network performance. The key performance indicator (KPI) is the entanglement fidelity between communicating nodes. The developed algorithm offers a promising pathway towards realizing the full potential of quantum networks by intelligently managing their most valuable asset: entanglement. The primary goal is to achieve higher fidelity entanglement distribution and improved communication rates. The effectiveness of the algorithm is evaluated through simulations, demonstrating its ability to outperform existing allocation schemes in various network topologies and operational scenarios.
With the continuous expansion of quantum communication networks and the increasing diversity of their business types, how to achieve efficient configuration under limited quantum resources has become the key to improving system performance. The quantum resources described in this article mainly include entanglement pairs, quantum bits, and channel capacity. Traditional optimization methods typically rely on precise mathematical models, which are difficult to adapt to dynamically evolving network environments and susceptible to noise interference. Therefore, this study proposes a machine learning based framework for optimizing the allocation of quantum communication resources. This framework integrates deep reinforcement learning and graph neural network technology, enabling collaborative optimization of entanglement allocation, routing selection, and time slice scheduling between nodes. Three typical quantum communication scenarios were constructed and tested in a simulation environment, including static topology, dynamic links, and high noise environments. The proposed method was compared and analyzed with integer linear programming, heuristic greedy algorithms, and rule-based strategies. Experimental data shows that this research method achieved an improvement of approximately 12% to 18% in average fidelity retention, a 15% to 22% increase in resource utilization, and a 20% reduction in latency. This study provides useful reference for the intelligent resource management of large-scale quantum networks in the future.
The quantum internet holds transformative potential for global communication by harnessing the principles of quantum information processing. Despite significant advancements in quantum communication technologies, the efficient distribution of critical resources, such as qubits, remains a persistent and unresolved challenge. Conventional approaches often fall short of achieving optimal resource allocation, underscoring the necessity for more effective solutions. This study proposes a novel reinforcement learning-based adaptive entanglement routing framework designed to enable resource allocation tailored to the specific demands of quantum applications. The introduced QuDQN model utilizes reinforcement learning to optimize the management of quantum networks, allocate resources efficiently, and enhance entanglement routing. The model integrates key considerations, including fidelity requirements, network topology, qubit capacity, and request demands.
This paper presents a novel approach to optimizing quantum entanglement topological networks for dynamic computation and data transmission. The core claim centers around the ability to dynamically adjust the physical connections within an entanglement network in real-time, surpassing the limitations of traditional distributed computing. This is achieved by leveraging the inherent fragility of entangled states and employing controlled physical perturbations to modify node connections, thereby altering the transmission pathways and resource allocation of quantum information. A key component of this system is the integration of machine learning algorithms to predict and proactively adjust the entanglement topology, adapting to fluctuating computational demands. The proposed method offers a fundamentally new paradigm for quantum computing, moving beyond static architectures towards adaptable, resource-optimized networks. The underlying mathematical framework is detailed, focusing on the manipulation of entangled states and the optimization algorithms employed. This work demonstrates a pathway towards scalable and efficient quantum computation by capitalizing on the unique properties of quantum entanglement.