发表机构
Indian Institute of Technology Jodhpur（印度理工学院焦特布尔分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究针对供应链风险预测的量子联邦学习，提出EWP剪枝方法，可高效移除客户端贡献，性能接近全量重训练且耗时仅为其约1/16。
AI 中文摘要
变分量子分类器的联邦部署适用于跨组织供应链风险预测，因为原始数据不会离开客户端，但GDPR等数据保护法规赋予客户端请求将其贡献从已训练模型中移除的权利。从头开始重新训练联邦模型以满足该请求是正确的，但效率低下，且尚不明确哪些量子电路参数实际承载了特定客户端的影响。我们提出纠缠加权剪枝（Entanglement-Weighted Pruning, EWP），这是一种量子联邦学习的忘恶流程，它通过两个信号的乘积对每个可训练电路参数进行评分：一是通过参数偏移规则在目标客户端数据上估计的量子费舍尔信息矩阵的对角元素，二是与参数所属门关联的结构纠缠权重。评分最低的参数会被剪枝，可选地对保留的客户端进行短期微调。我们在Qiskit中实现了完整流程，针对四量子比特数据重加载ansatz，通过FedAvg在五个模拟供应链风险客户端上进行训练，并在三个随机种子下将EWP与全量重训练、仅微调、随机剪枝、仅费舍尔剪枝、仅纠缠剪枝进行基准测试。EWP的平均忘恶后准确率与全量重训练的基准无统计学差异，同时产生更低的遗忘分数，且所需的墙钟时间约减少16倍。对剪枝阈值、客户端数量和非独立同分布（non-IID）强度的消融实验表明，结合两种信号是必要的，因为仅纠缠剪枝和仅费舍尔剪枝相对于EWP均会显著降低准确率。
英文摘要
Federated deployments of variational quantum classifiers are attractive for cross-organisation risk prediction in supply chains, because raw data never leaves the client, yet data-protection regulations such as the GDPR grant clients a right to request that their contribution be removed from a trained model after the fact. Retraining a federated model from scratch to honour such a request is correct but wasteful, and it is not obvious which quantum circuit parameters actually carry a given client's influence. We introduce Entanglement-Weighted Pruning (EWP), an unlearning procedure for quantum federated learning that scores every trainable circuit parameter with the product of two signals: the diagonal entry of the quantum Fisher information matrix estimated on the target client's data via the parameter-shift rule, and a structural entanglement weight associated with the parameter's gate. Parameters with the lowest scores are pruned, optionally followed by a short fine-tuning pass on the retained clients. We implement the full pipeline in Qiskit for a four-qubit data-re-uploading ansatz trained with FedAvg across five simulated supply-chain-risk clients, and benchmark EWP against full retraining, fine-tuning alone, random pruning, Fisher-only pruning, and entanglement-only pruning, over three random seeds. EWP attains a mean post-unlearning accuracy statistically indistinguishable from the full-retraining oracle, while producing a lower forgetting score and requiring roughly 16 times less wall-clock time. Ablations over pruning threshold, client count, and non-IID strength show that combining the two signals is necessary, as entanglement-only and Fisher-only pruning each substantially degrade accuracy relative to EWP.
Comments25 pages, 10 figures, 11 tables. Code: https://github.com/Sumitchongder/dew-p-qfl-unlearning. Data (Zenodo): https://doi.org/10.5281/zenodo.21372906. Research carried out as part of the QIntern 2026 programme (QWorld Association)
