Resilience Analysis of Quantum Network Against Targeted Attacks: Recovery via Rerouting and Purification
Bibliographic record
Abstract
Quantum networks are essential for secure quantum communication and distributed quantum computing. However, their performance is highly vulnerable to targeted attacks that disrupt entanglement distribution, leading to significant network degradation. To address this challenge, we propose two heuristic routing algorithms Quantum Entanglement Distribution Algorithm 1 (QEDA1) and Quantm Entanglement Distribtion Algorithm 2 (QEDA2) that minimize loss of fidelity by reducing the number of intermediate nodes while optimizing entanglement swapping and purification strategies. In QEDA1 we only applied purification as a recovery mechanism, while in QEDA2 we utilized a purificationrerouting approach. Furthermore, unlike previous studies, which relied primarily on idealized or small-scale topologies, we evaluated our approach on a real-world network topology (Surfnet), analyzing throughput variations under both normal conditions and targeted attacks. Furthermore, we compare the performance of the algorithm in memory-assisted and memoryless quantum networks, demonstrating the impact of quantum memory on network resilience. In addition, we introduce an attack model based on centrality-driven node failures and propose a recovery mechanism that integrates rerouting and entanglement purification to mitigate the effects of targeted attacks. Our results indicate that QEDA2 is more effective in mitigating the effect of attacks on throughput. Moreover, our findings highlight the trade-offs between network robustness, resource allocation, and fidelity constraints, providing valuable insights for the design of resilient large-scale quantum networks.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".