{"ID":6497691,"CreatedAt":"2026-07-13T01:19:40.13847098Z","UpdatedAt":"2026-07-14T01:36:59.12045529Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.09378","arxiv_id":"2607.09378","title":"When Routes Run Out: Adversarial Co-Learning and Explainable Robustness in Quantum Repeater Networks","abstract":"We study an adversarial bandit problem for entanglement-based quantum-network routing over a modest graph corpus. Alice selects an end-to-end repeater route for an Ekert-91 protocol (E91) representing her move, while Eve selects an attack surface, either edge intercept--resend or repeater memory degradation. Payoffs are drawn from cached SeQUeNCe-simulated E91 transcripts, and Alice accepts a turn when the finite-sample statistic violates the Clauser-Horne-Shimony-Holt (CHSH) bound. Performing adversarial co-learning across 50 structured topologies, we find that learned retention tracks a full-matrix minimax reference closely (Pearson $r=0.99$): under a one-surface Eve action model, bottleneck families have zero retention, while non-bottleneck families follow a $1-1/N$ coverage principle. We then fit decision-tree explanation models to graph-, attack-, and route-level topology-corpus targets and report their faithfulness. Finally, we construct prompt records for local language models to summarize the tree evidence, resulting in an open-source explanation workflow for quantum-repeater network games.","short_abstract":"We study an adversarial bandit problem for entanglement-based quantum-network routing over a modest graph corpus. Alice selects an end-to-end repeater route for an Ekert-91 protocol (E91) representing her move, while Eve selects an attack surface, either edge intercept--resend or repeater memory degradation. Payoffs ar...","url_abs":"https://arxiv.org/abs/2607.09378","url_pdf":"https://arxiv.org/pdf/2607.09378v1","authors":"[\"Brennan Bell\",\"Inti Gabriel Mendoza Estrada\",\"Andreas Trügler\",\"Paul Erker\"]","published":"2026-07-10T13:01:55Z","proceeding":"quant-ph","tasks":"[\"quant-ph\",\"cs.AI\",\"cs.CR\"]","methods":"[\"Language Model\"]","has_code":false}