A Controlled Evaluation of Quantum Correlation Refinement for Few-Shot Semantic Segmentation: Resource Cost and IBM Quantum Hardware Validation

Abstract

PQCs are increasingly proposed as trainable components in classical ML pipelines, but rarely characterized alongside a controlled measurement of task-level benefit. We report such an evaluation, using few shot segmentation as testbed. We integrate a Quantum Correlation Refiner (QCR), a six-qubit variational circuit with amplitude embedding and a gated residual connection into a classical correlation-based architecture, under the four fold PASCAL 5i protocol, three seeds per fold, against an unrefined baseline and a matched MLP refiner. Across 12 fold-seed comparisons, QCR changes mIoU by only +0.0001 (t(11)=0.10, p=0.92, dz=0.03); the MLP control and an expanded Pauli-measurement variant show similarly no improvement. QCR adds just 473 parameters but roughly doubles per epoch training time. A trained circuit on IBM hardware (18 patches) agrees closely with the noiseless simulator (r=0.954, MAE=0.101), a fidelity check, not an accuracy gain. The module is functional, trainable, and hardware deployable, yet shows no task-level advantage over a matched classical alternative under this regime, a template for evaluating quantum components jointly via matched controls, repeated seeds, resource measurement, and hardware validation, rather than any one alone.