发表机构
Iowa State University; Grand Valley State University; Kansas State University（爱荷华州立大学; 大峡谷州立大学; 堪萨斯州立大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对触觉互联网安全，提出自适应射击变分量子电路策略，动态分配量子测量次数，在资源约束下平衡决策可靠性与资源消耗，显著节省射击次数并保持低决策分歧。
AI 中文摘要
触觉互联网（TI）安全分析必须在受限的计算和测量资源下平衡可靠的阈值决策。我们针对有限射击混合量子异常推理研究了这一张力，并引入了自适应射击变分量子电路（AS-VQC）策略。这种经过验证校准的策略从每条记录的128次射击开始，并仅在有限射击异常分数保持在验证选择的安全阈值附近时，通过256、512和1024次射击累积升级。量子评分器作为路径外安全分析组件进行评估，而非触觉关键路径的一部分。使用包含4,875条记录的CESNET-TimeSeries24派生的聚合流基准，泄漏安全的随机、实体组不相交和时间保持，以及每个保持的五个训练量子神经网络（QNN）检查点，主要的AS-VQC-95（beta=0.95）策略平均每条记录使用129.2、276.9和131.2次射击，分别节省了均匀1024次射击基线（Fixed-1024）的87.4%、73.0%和87.2%。与解析（精确期望）推理的决策分歧为0.771%、0.409%和0.635%，低于均匀128次射击基线（Fixed-128）和匹配预算的洗牌分配控制。Fixed-1024保持更稳定的决策，建立了可测量的可靠性-资源权衡，而非无成本的等价。更保守的AS-VQC-99（beta=0.99）进一步减少了分歧，同时在所有保持中使用少于512次的平均射击。这些结果表明，有限的量子测量可以被视为一种推理资源，并集中在边界敏感的TI安全决策上，同时在未见实体条件下暴露了依赖检查点的升级。
英文摘要
Tactile Internet (TI) security analytics must balance reliable thresholded decisions with constrained computational and measurement resources. We study this tension for finite-shot hybrid quantum anomaly inference and introduce the Adaptive-Shot Variational Quantum Circuit (AS-VQC) policy. This validation-calibrated policy begins each record at 128 shots and cumulatively escalates through 256, 512, and 1024 shots only when the finite-shot anomaly score remains close to a validation-selected security threshold. The quantum scorer is evaluated as an off-path security analytics component rather than part of the haptic critical path. Using a 4,875-record CESNET-TimeSeries24-derived aggregate-flow benchmark, leakage-safe random, entity-group-disjoint, and temporal holdouts, and five trained quantum neural network (QNN) checkpoints per holdout, the primary AS-VQC-95 (beta = 0.95) policy averages 129.2, 276.9, and 131.2 shots per record, saving 87.4%, 73.0%, and 87.2% of the uniform 1024-shot baseline (Fixed-1024), respectively. The decision disagreement with analytic (exact-expectation) inference is 0.771%, 0.409%, and 0.635%, lower than both the uniform 128-shot baseline (Fixed-128) and a matched-budget shuffled-allocation control. Fixed-1024 remains more decision-stable, establishing a measurable reliability-resource trade-off rather than cost-free equivalence. A more conservative AS-VQC-99 (beta = 0.99) further reduces disagreement while using fewer than 512 average shots across all holdouts. These results show that finite quantum measurements can be treated as an inference resource and concentrated on boundary-sensitive TI-security decisions while exposing checkpoint-dependent escalation under unseen-entity conditions.
CommentsAccepted at the 40th Conference on Neural Information Processing Systems (NeurIPS 2026). Workshop: Secure and Trustworthy Quantum Machine Learning (SaTQuML). Presentation: Short Oral Presentation. Code: https://github.com/msudipto/AdaptiveShot_HybridQAD_Framework
