QuPID：面向医学RAG的量子参数高效输入相关检索适配
QuPID: Quantum Parameter-Efficient Input-Dependent Retrieval Adaptation for Medical RAG
- Korea University（高丽大学）
- Sookmyung Women’s University（淑明女子大学）
- Virginia Tech（弗吉尼亚理工学院暨州立大学）
- Seoul National University（首尔大学）
- Seoul National University Hospital（首尔大学医院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
QuPID通过数据重上传和测量读数比较实现输入相关量子检索适配，仅用60个参数在医学RAG上超越冻结编码器及多达525万参数的适配器，显著提升P@5。
AI中文摘要：
基于保真度的量子检索通过查询态与档案态之间的保真度对候选进行排序。在固定状态编码后应用共享的输入无关酉变换不会改变该保真度，因此训练电路无法改变排序。量子参数高效输入相关检索适配（QuPID）通过数据重上传使电路输入相关，并比较测量读数（局部泡利期望值向量）而非状态，从而修复了这一问题。其结果是：一个用于将冻结的图像特征适配到数据有限的本地档案的小型读数器：训练在经典计算机上模拟电路，推理在GPU上运行，使用固定的学习参数。我们将该类别刻画为输入调制二次特征映射的结构化分解，界定了其重上传通道的频率支持，并给出了一个参数计数泛化界，以证明其小预算的合理性。在共享冻结骨干网络和无标签协议下，QuPID的60个参数在ChestX-ray14和MURA上比冻结的医学编码器以及最多525万个可训练参数的适配器和低秩适配（LoRA）获得更高的精度@5（P@5）。在ChestX-ray14上，相对于冻结编码器的P@5增益为+0.116，在512个适配示例时对重新调优的适配器的领先幅度最大（+0.040），相对于同等紧凑的经典旋转平面头的全预算优势为+0.023，且95%置信区间不包括零。医学影像是主要测试平台；该模式在两个非医学基准、报告生成以及模拟门噪声和有限样本读数下重复出现。
英文摘要：
Fidelity-based quantum retrieval ranks candidates by the fidelity between query and archive states. Applying a shared input-independent unitary after fixed state encoding leaves that fidelity unchanged, so training the circuit cannot alter the ranking. Quantum parameter-efficient input-dependent retrieval adaptation (QuPID) repairs this by making the circuit input-dependent through data re-uploading and by comparing measurement readouts, vectors of local Pauli expectations, rather than states. The result is a small readout for adapting frozen image features to a local archive with limited data: training simulates the circuit classically, and inference runs on a GPU with fixed learned parameters. We characterize the class as a structured factorization of input-modulated quadratic feature maps, bound the frequency support of its re-uploading channel, and give a parameter-count generalization bound that motivates its small budget. Under a shared frozen backbone and a label-free protocol, QuPID's 60 parameters give higher precision-at-5 (P@5) on ChestX-ray14 and MURA than frozen medical encoders, and than adapters and low-rank adaptation (LoRA) with up to 5.25 million trainable parameters. On ChestX-ray14, the P@5 gain over the frozen encoder is +0.116, the lead over retuned adapters is widest at 512 adaptation examples (+0.040), and the full-budget margin over an equally compact classical rotation-plane head is +0.023 with a 95% interval excluding zero. Medical imaging is the primary testbed; the pattern recurs on two non-medical benchmarks, in report generation, and under simulated gate noise and finite-shot readout.
