Topological State-Aware Simulation Framework for Inter-Satellite Twin-Field QKD Networks
- URL: http://arxiv.org/abs/2608.12659v1
- Date: Wed, 12 Aug 2026 23:45:37 GMT
- Title: Topological State-Aware Simulation Framework for Inter-Satellite Twin-Field QKD Networks
- Abstract summary: Inter-satellite links (ISLs) are the mandatory backbone for global quantum networks.<n>TF-QKD is highly vulnerable to dynamic, non-IID orbital environments.<n>We introduce a reference-only topological post-selection oracle.
- Score: 1.7259898169307613
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Inter-satellite links (ISLs) are the mandatory backbone for global quantum networks. While Twin-Field Quantum Key Distribution (TF-QKD) successfully surpasses linear rate-loss bounds, its extreme phase sensitivity makes it highly vulnerable to dynamic, non-IID (Independent and Identically Distributed) orbital environments. In composable finite-key analyses governed by the Generalized Entropy Accumulation Theorem (GEAT), traditional adaptive post-selection heuristics either violate strict independence conditions or incur massive second-order penalties that collapse the secret key rate. To overcome this, we introduce a reference-only topological post-selection oracle. By modeling the constellation as a Cellular Sheaf and applying Topological Data Analysis (TDA), our protocol derives a public acceptance event ($Ω$) exclusively from classical beacon telemetry. To rigorously validate this mechanism, we develop a modular simulation framework equipped with stochastic noise injection and an explicit GEAT security ledger. Simulations across 2,000-5,000 km ISL separations compare the same Hodge-Koopman gate with TDA disabled and enabled. At 2,000 km, the median conditional candidate rates are $2.14 \times 10^{-6}$ and $5.87 \times 10^{-7}$ bit per emitted pulse, respectively; both configurations return zero at 3,000-5,000 km. TDA is active in all 4,788 evaluated windows, but does not extend the positive-candidate range in this scenario. These exported rates are conditional numerical candidates: the full protocol-level composable-security proof remains incomplete and the certified composable rate is therefore zero throughout.
Related papers
- Demonstration of an LLO CV-QKD system over 12 km of optical fiber [0.0]
We report on a CV-QKD implementation using fully independent transmitter and receiver lasers over a 12 km fiber spool.<n>The full-fledged classical post-processing is capable of recovering the channel parameters and extracting secret key rates of 5.11 Mbit/s in the finite-size regime.<n>This work establishes the foundation for metropolitan fiber deployment of CV-QKD under strict security constraints.
arXiv Detail & Related papers (2026-08-07T14:37:33Z) - Comparing SGLD and a fixed-noise Predictor-Corrector adaptation in canonical Joint Energy-Based Models on CIFAR-10 [0.0]
We reproduce canonical Joint Energy-Based Models (JEM) on WideResNet-28-10 without normalisation layers on two independent runs.<n>We test a fixed-noise PC adaptation - with the degenerate-noise predictor replaced by a deterministic step - across three protocols.<n>The reconstruction reaches 92.88% test accuracy and buffer-FID 44.46 (canonical: 92.9% and 38.40)
arXiv Detail & Related papers (2026-08-05T16:28:57Z) - Modeling Normal Is All You Need: Joint Latent Clustering for Anomaly Detection in Multimodal Cyber-Physical Systems [47.027290803102666]
Normal behaviour is the union of many imbalanced, curved, thin-fringed operating regimes.<n>We model the normal law with a jointly learned latent representation plus explicit Gaussian-mixture mode clustering.<n>We evaluate under a deliberately fair protocol: raw point-wise metrics with no point adjustment, a trivial-detector difficulty split, prevalence-matched F1, and train-normal-only calibration.
arXiv Detail & Related papers (2026-07-07T10:10:00Z) - Privacy-Preserving Federated Temporal Graph Learning with Digital Twin--Guided Adaptive Deception for Cyber-Resilient IoMT [0.6095710009266794]
This paper presents the Federated Temporal Graph Convolutional Network with Advantage Actor-Critic (Federated TGCN-A2C)<n>The framework achieves 99.48% and 99.61% test accuracy with weighted-F1 scores of 0.9948 and 0.9961, converging within 25 and 10 federated rounds, outperforming Fed-Inforce-Fusion by 0.21 percentage points while covering three additional attack categories.<n>Post-hoc explainability via SHAP, LIME, Grad-CAM, and counterfactual analysis confirms decisions are grounded in semantically meaningful flow features, supporting regulatory accountability in clinical deployments.
arXiv Detail & Related papers (2026-06-19T15:09:38Z) - UTOPYA: A Multimodal Deep Learning Framework for Physics-Informed Anomaly Detection and Time-Series Prediction [42.532335022354545]
Anomaly detection in batch processes is hindered by transient dynamics, scarce fault labels, and reliance on singlemodality sensor data.<n>This work introduces UTOPYA, a multimodal framework that jointly addresses anomaly detection, time-series prediction, and phase classification in batch distillation.<n>A physics-informed regularisation scheme introduced in this work enforces temporal smoothness and thermodynamic monotonicity.
arXiv Detail & Related papers (2026-05-18T10:28:15Z) - Where the Quantum Lives in D-Wave Hybrid Portfolio Optimization [0.0]
We audit how much of D-Wave's hybrid quantum-classical portfolio-optimization service is actually quantum.<n>The constraint-native LeapHybridCQM service matches Gurobi's proven optimum on all 54 instances where Gurobi proves optimality.
arXiv Detail & Related papers (2026-05-17T19:41:17Z) - Distributional Energy-Based Models for Uncertainty-Aware Structured LLM Reasoning [40.342912574072024]
Large Language Models produce structured outputs such as travel plans or code solutions.<n>Individual reasoning steps may appear correct while the output as a whole violates budgets, fails test cases, or contradicts earlier deductions.<n>We propose a learned quality scorer with deterministic analytical constraint penalties for verifying structured LLM outputs.
arXiv Detail & Related papers (2026-05-15T17:08:27Z) - Eradicating Negative Transfer in Multi-Physics Foundation Models via Sparse Mixture-of-Experts Routing [0.0]
Shodh-MoE is a sparse-activated latent transformer architecture for multi-physics transport.<n>A Top-1 router dynamically assigns localized latent patches to expertworks.<n>Results support sparse expert routing as a practical architectural mechanism for mitigating multi-physics interference in universal neural operators.
arXiv Detail & Related papers (2026-05-14T17:58:15Z) - Hierarchical Transformer Preconditioning for Interactive Physics Simulation [28.137076331332413]
Hierarchical Transformer Preconditioner, a neural preconditioner anchored to a weak-admissibility H-matrix partition.<n>Network models the inverse through low-rank far-field factors.<n>Uses highway connections to propagate context across depth sparse.
arXiv Detail & Related papers (2026-05-13T11:02:27Z) - CAWN: Continuous Acoustic Wave Networks for Autoregressive Language Modeling [46.16066322190728]
We introduce the Continuous Acoustic Wave Network (CAWN), a fully continuous sequence-mixing architecture.<n>Instead of discrete matrix-based attention, CAWN projects hidden states into multi-headed complex-domain phasors.<n>To prevent signal degradation over ultra-long contexts, we introduce a dual-gated Selective Phase Resonance mechanism.
arXiv Detail & Related papers (2026-04-05T20:13:22Z) - LLM-Based Emulation of the Radio Resource Control Layer: Towards AI-Native RAN Protocols [28.04609776570199]
Large AI Models (LAMs) are key enablers of the AI-Native Air Interface (AI-AI)<n>This paper presents the first standards-compliant emulation of the Radio Resource Control layer using a decoder-only LAM.<n>Results demonstrate that LAMs, when augmented with protocol-aware reasoning, can directly orchestrate control-plane procedures.
arXiv Detail & Related papers (2025-05-22T15:55:56Z)
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.