When Routes Run Out: Adversarial Co-Learning and Explainable Robustness in Quantum Repeater Networks
For quantum network security, this work provides an explainable adversarial co-learning framework, though it is incremental as it applies existing bandit and explanation methods to a new domain.
The paper studies adversarial bandit routing in quantum repeater networks, showing that learned retention matches a minimax reference (Pearson r=0.99) and that bottleneck families have zero retention while non-bottleneck families follow a 1-1/N coverage principle.
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.