Utility-Based Path Selection and Configuration in Quantum Networks via Layered Shortest Paths

• URL: http://arxiv.org/abs/2609.16198v1
• Date: Mon, 14 Sep 2026 18:30:46 GMT
• Title: Utility-Based Path Selection and Configuration in Quantum Networks via Layered Shortest Paths
• Abstract summary: A path in a quantum network is a chain of repeaters that distributes entanglement between two users.<n>We show how a path's performance depends jointly on its route and its per-link settings.<n>A single run returns the full rate fidelity frontier, from which the path maximizing any nondecreasing utility function can be selected.
• Score: 20.986889484035093
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: A path in a quantum network is a chain of repeaters that distributes entanglement between two users. Selecting a path requires balancing the rate and quality (e.g., fidelity) of the delivered entanglement, but these quantities, unlike standard routing metrics, compose non-additively. The problem is compounded by link-level configuration choices (e.g., distillation rounds or emitter brightness tuning), each trading rate against fidelity, so that a path's performance depends jointly on its route and its per-link settings. We cast this joint path selection and configuration problem as a shortest path computation on a layered graph whose layers track discretized end-to-end fidelity. A single run returns the full rate fidelity Pareto frontier, from which the path maximizing any nondecreasing utility function of rate and fidelity can be selected. We prove that for certain utility functions (including the secret key rate of BB84), the method is a fully polynomial time approximation scheme, returning a near-optimal path within a specified tolerance. We further characterize exactly when cheaper scalarization-based routing suffices: it is optimal for utility functions with convex fidelity profiles, but can be arbitrarily suboptimal otherwise (e.g., for step-like, sigmoidal utilities), whereas the layered method remains reliable in all cases.

Related papers

• Closed-Form Spectral Regularization for Multi-Task Model Merging [96.82449201305234]
  Model merging combines several independently fine-tuned experts into a single multi-task model without any training data.<n>State-of-the-art merging methods formulate merging as a layer-wise interference problem.<n>We propose SWUDI, a closed-form method that combines a soft exponential filter, which matches the gradient-flow trajectory of iterative descent.
  arXiv  Detail & Related papers  (2026-06-05T14:00:47Z)
• Multi-Fidelity Flow Matching: Cascaded Refinement of PDE Solutions [6.144605787421899]
  Multi-Fidelity Flow Matching is a cascade refinement framework for PDE solutions.<n>We exploit this in Multi-Fidelity Flow Matching (MFFM), a cascade refinement framework for PDE solutions.<n>MFFM is a learned analog of multigrid refinement that reaches the finest grid in $L$ deterministic network evaluations per query.
  arXiv  Detail & Related papers  (2026-05-15T16:02:18Z)
• Stochastic Multipath Routing for High-Throughput Entanglement Distribution in Quantum Repeater Networks [6.036769600366487]
  Quantum repeater networks distribute entanglement over lossy links while many users share a limited pool of entangled pairs.<n>Most existing routing schemes either always use a single best path or rely on global optimizations that are hard to run in real time.<n>Here we propose and analyze a simple alternative: a multipath rule in which each entanglement request is sent at random along one of several edge-disjoint paths.
  arXiv  Detail & Related papers  (2026-03-26T15:36:08Z)
• Efficient Path Generation with Curvature Guarantees by Mollification [2.6381163133447836]
  We present a method to regularize non-differentiable functions and generate feasible paths through mollification.<n>Specifically, we approximate an arbitrary path with a differentiable function that can converge to it with arbitrary precision.<n>The proposed approach is analytically more efficient than standard methods, enabling real-time implementation on microcontrollers.
  arXiv  Detail & Related papers  (2025-12-15T10:48:42Z)
• Provably Optimal Quantum Circuits with Mixed-Integer Programming [0.0]
  We present a depth-aware optimization framework for quantum circuit compilation.<n>For exact synthesis of a target unitary, we formulate a mixed-integer linear program (MILP) that linearly global-phase equivalence.<n>To scale beyond exact MILP, we propose a novel rolling-Circuit optimization (RHO) that rolls primarily in time, caps the active-qubits, and enforces per-qubit closure.
  arXiv  Detail & Related papers  (2025-10-01T08:25:43Z)
• Fidelity-Aware Multipath Routing for Multipartite State Distribution in Quantum Networks [49.1574468325115]
  We consider the problem of distributing entangled multipartite states across a quantum network with improved distribution rate and fidelity.<n>We propose fidelity-aware multi-path routing protocols, assess their performance in terms of the rate and fidelity of the distributed Greenberger-Horne-Zeilinger (GHZ) states and compare such performance against that of single-path routing.
  arXiv  Detail & Related papers  (2025-03-05T19:00:11Z)
• Finding Transformer Circuits with Edge Pruning [71.12127707678961]
  We propose Edge Pruning as an effective and scalable solution to automated circuit discovery.<n>Our method finds circuits in GPT-2 that use less than half the number of edges compared to circuits found by previous methods.<n>Thanks to its efficiency, we scale Edge Pruning to CodeLlama-13B, a model over 100x the scale that prior methods operate on.
  arXiv  Detail & Related papers  (2024-06-24T16:40:54Z)
• Smart Routing with Precise Link Estimation: DSEE-Based Anypath Routing for Reliable Wireless Networking [6.123324869194196]
  Shortest Anypath routing offers a solution by adapting routing decisions based on real-time link conditions. This paper introduces a novel approach that leverages the Deterministic Sequencing of Exploration and Exploitation (DSEE), a multi-armed bandit algorithm. By coupling DSEE with Anypath routing, this algorithm continuously learns and ensures accurate delivery probability estimation.
  arXiv  Detail & Related papers  (2024-05-16T18:16:56Z)
• Joint inference and input optimization in equilibrium networks [68.63726855991052]
  deep equilibrium model is a class of models that foregoes traditional network depth and instead computes the output of a network by finding the fixed point of a single nonlinear layer. We show that there is a natural synergy between these two settings. We demonstrate this strategy on various tasks such as training generative models while optimizing over latent codes, training models for inverse problems like denoising and inpainting, adversarial training and gradient based meta-learning.
  arXiv  Detail & Related papers  (2021-11-25T19:59:33Z)
• Fidelity-Guarantee Entanglement Routing in Quantum Networks [64.49733801962198]
  Entanglement routing establishes remote entanglement connection between two arbitrary nodes. We propose purification-enabled entanglement routing designs to provide fidelity guarantee for multiple Source-Destination (SD) pairs in quantum networks.
  arXiv  Detail & Related papers  (2021-11-15T14:07:22Z)
• Purification and Entanglement Routing on Quantum Networks [55.41644538483948]
  A quantum network equipped with imperfect channel fidelities and limited memory storage time can distribute entanglement between users. We introduce effectives enabling fast path-finding algorithms for maximizing entanglement shared between two nodes on a quantum network.
  arXiv  Detail & Related papers  (2020-11-23T19:00:01Z)

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.