Optimizing Entanglement Distribution Protocols: Maximizing Classical Information in Quantum Networks

• URL: http://arxiv.org/abs/2603.25360v2
• Date: Tue, 31 Mar 2026 09:03:06 GMT
• Title: Optimizing Entanglement Distribution Protocols: Maximizing Classical Information in Quantum Networks
• Abstract summary: Efficient entanglement distribution is the foundational challenge in realizing large-scale Quantum Networks.<n>This paper addresses the entanglement distribution problem by introducing four pivotal advances.<n>First, recognizing that the primary application of quantum communication is the transmission of private information, we derive the Ensemble Capacity (EC), a novel metric that explicitly quantifies the secure classical information enabled by the entanglement distribution.
• Score: 5.621690515150937
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Efficient entanglement distribution is the foundational challenge in realizing large-scale Quantum Networks. However, state-of-the-art solutions are frequently limited by restrictive operational assumptions, prohibitive computational complexities, and performance metrics that misalign with practical application needs. To overcome these barriers, this paper addresses the entanglement distribution problem by introducing four pivotal advances. First, recognizing that the primary application of quantum communication is the transmission of private information, we derive the Ensemble Capacity (EC), a novel metric that explicitly quantifies the secure classical information enabled by the entanglement distribution. Second, we propose a generalized mathematical formulation that removes legacy structural restrictions in the solution space. Our formulation supports an unconstrained, arbitrary sequencing of entanglement swapping and purification. Third, to efficiently navigate the resulting combinatorial optimization space, we introduce a novel Dynamic Programming (DP)-based hypergraph generation algorithm. Unlike prior methods, our approach avoids artificial fidelity quantization, preserving exact, continuous fidelities while proactively pruning sub-optimal trajectories. Finally, we encapsulate these algorithmic solutions into CODE, a system-level, two-tiered orchestration framework designed to enable near-real-time network responsiveness. Extensive evaluations confirm that our DP-driven architecture yields superior private classical information capacity and significant reductions in computational complexity, successfully meeting the strict sub-second latency thresholds required for dynamic QN operation.

Related papers

• Constraint-Aware Quantum Optimization via Hamming Weight Operators [1.1463843149836523]
  Constrained optimization with strict linear constraints underpins applications in drug discovery, power grids, logistics, and finance.<n>We introduce Hamming Weight Operators, a new class of constraint-aware operators that confine quantum evolution strictly within the feasible subspace.<n>We validate our approach on benchmark tasks from both finance and high-energy physics, specifically portfolio optimization and two-jet clustering with energy balance.
  arXiv  Detail & Related papers  (2026-01-04T12:58:04Z)
• Unlocking Symbol-Level Precoding Efficiency Through Tensor Equivariant Neural Network [84.22115118596741]
  We propose an end-to-end deep learning (DL) framework with low inference complexity for symbol-level precoding.<n>We show that the proposed framework captures substantial performance gains of optimal SLP, while achieving an approximately 80-times speedup over conventional methods.
  arXiv  Detail & Related papers  (2025-10-02T15:15:50Z)
• Reinforcement Learning for Quantum Network Control with Application-Driven Objectives [53.03367590211247]
  Dynamic programming and reinforcement learning offer promising tools for optimizing control strategies.<n>We propose a novel RL framework that directly optimize non-linear, differentiable objective functions.<n>Our work comprises the first step towards non-linear objective function optimization in quantum networks with RL, opening a path towards more advanced use cases.
  arXiv  Detail & Related papers  (2025-09-12T18:41:10Z)
• Quantum-Classical Hybrid Quantized Neural Network [8.382617481718643]
  We present a novel Quadratic Binary Optimization (QBO) model for quantized neural network training, enabling the use of arbitrary activation and loss functions.<n>We employ the Quantum Gradient Conditional Descent (QCGD) algorithm, which leverages quantum computing to directly solve the QCBO problem.
  arXiv  Detail & Related papers  (2025-06-23T02:12:36Z)
• Variational Inference for Quantum HyperNetworks [16.447132371824942]
  Variational Quantum Algorithm is employed to generate binary weights through quantum circuit measurements.<n>Key quantum phenomena such as superposition and entanglement facilitate the exploration of a broader solution space.
  arXiv  Detail & Related papers  (2025-06-06T08:57:43Z)
• Single-loop Algorithms for Stochastic Non-convex Optimization with Weakly-Convex Constraints [49.76332265680669]
  This paper examines a crucial subset of problems where both the objective and constraint functions are weakly convex.<n>Existing methods often face limitations, including slow convergence rates or reliance on double-loop designs.<n>We introduce a novel single-loop penalty-based algorithm to overcome these challenges.
  arXiv  Detail & Related papers  (2025-04-21T17:15:48Z)
• Cons-training Tensor Networks: Embedding and Optimization Over Discrete Linear Constraints [2.8834278113855896]
  We introduce a novel family of tensor networks, termed constrained matrix product states (MPS)<n>MPS incorporate exactly arbitrary discrete linear constraints, including inequalities, into sparse block structures.<n>These networks are particularly tailored for modeling distributions with support strictly over the feasible space.
  arXiv  Detail & Related papers  (2024-05-15T00:13:18Z)
• Measurement-Based Quantum Approximate Optimization [0.24861619769660645]
  We focus on measurement-based quantum computing protocols for approximate optimization. We derive measurement patterns for applying QAOA to the broad and important class of QUBO problems. We discuss the resource requirements and tradeoffs of our approach to that of more traditional quantum circuits.
  arXiv  Detail & Related papers  (2024-03-18T06:59:23Z)
• Task-Oriented Sensing, Computation, and Communication Integration for Multi-Device Edge AI [108.08079323459822]
  This paper studies a new multi-intelligent edge artificial-latency (AI) system, which jointly exploits the AI model split inference and integrated sensing and communication (ISAC) We measure the inference accuracy by adopting an approximate but tractable metric, namely discriminant gain.
  arXiv  Detail & Related papers  (2022-07-03T06:57:07Z)
• Space-efficient binary optimization for variational computing [68.8204255655161]
  We show that it is possible to greatly reduce the number of qubits needed for the Traveling Salesman Problem. We also propose encoding schemes which smoothly interpolate between the qubit-efficient and the circuit depth-efficient models.
  arXiv  Detail & Related papers  (2020-09-15T18:17:27Z)
• Combining Deep Learning and Optimization for Security-Constrained Optimal Power Flow [94.24763814458686]
  Security-constrained optimal power flow (SCOPF) is fundamental in power systems. Modeling of APR within the SCOPF problem results in complex large-scale mixed-integer programs. This paper proposes a novel approach that combines deep learning and robust optimization techniques.
  arXiv  Detail & Related papers  (2020-07-14T12:38:21Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.