Quantum method improves medical image retrieval with fewer parameters
QuPID: Quantum Parameter-Efficient Input-Dependent Retrieval Adaptation for Medical RAG
Artificial IntelligenceMachine Learning
Summary
Finding relevant medical images quickly can help doctors diagnose patients better. The authors introduce a quantum-inspired method called QuPID that adjusts how images are matched using fewer adjustable parts, making it work well even with little training data. QuPID outperforms other methods on medical chest X-rays and bone images, showing that small, smart quantum approaches can improve accuracy. It also works well on some non-medical tasks and handles noisy conditions better than alternatives.
What this means in practice
- •For medical image analysts: Improve precision in retrieving related medical images from large archives with fewer model parameters and less training data.
- •For clinical report generation teams: Enhance report generation accuracy by better matching images to textual data using small quantum-inspired adaptors.
Authors
Hyojun Ahn, Emily Jimin Roh, Soohyun Park, Walid Saad, Hyung-Chul Lee, Joongheon Kim
Abstract
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.