mlsanityData to 14 Oct 2026

QuPID: Quantum Parameter-Efficient Input-Dependent Retrieval Adaptation for Medical RAG

arXiv 2609.33351 ↗First posted 27 Sep 2026cs.AI cs.LGcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 36 of its 65 references that are in the map. First posted on arXiv 27 Sep 2026.

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References · 36

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24 more
  1. 2020The effect of data encoding on the expressive power of variational quantum machine learning modelscited by 333
  2. 2019Parameterized quantum circuits as machine learning modelscited by 332
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  11. 2024On the relation between trainability and dequantization of variational quantum learning modelscited by 25
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  13. 2017MURA: Large Dataset for Abnormality Detection in Musculoskeletal Radiographscited by 20
  14. 2020VSQL: Variational Shadow Quantum Learning for Classificationcited by 17
  15. 2025Quantum-PEFT: Ultra parameter-efficient fine-tuningcited by 11
  16. 2024eQMARL: Entangled Quantum Multi-Agent Reinforcement Learning for Distributed Cooperation over Quantum Channelscited by 8
  17. 2025A Survey on Retrieval And Structuring Augmented Generation with Large Language Modelscited by 8
  18. 2025TensorRL-QAS: Reinforcement learning with tensor networks for improved quantum architecture searchcited by 7
  19. 2025ScIRGen: Synthesize Realistic and Large-Scale RAG Dataset for Scientific Researchcited by 7
  20. 2025Quantum Doubly Stochastic Transformerscited by 6
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  22. 2025Layerwise Federated Learning for Heterogeneous Quantum Clients using Quoruscited by 5
  23. 2025Predictive Performance of Deep Quantum Data Re-uploading Modelscited by 4
  24. 2025MIRA: A Novel Framework for Fusing Modalities in Medical RAGcited by 4

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