Central Serous Retinopathy (CSR) is a retinal disorder in which serous fluid accumulates beneath the neurosensory retina. Optical Coherence Tomography (OCT) is the imaging modality most commonly used to detect it. This paper presents HQNNDL, a hybrid pipeline that pairs a frozen, ImageNet-pretrained ResNet-18 feature extractor with an 8-qubit variational quantum circuit (VQC) and a compact fully-connected classifier, for four-class OCT image classification. A large, clinician-graded CSR-severity dataset was not available to us, so the system was trained and evaluated on a stratified 4,000-image subsample of the public Kermany OCT2017 dataset, with its DME and CNV categories used as structural proxies for early- and advanced-stage CSR. On a held-out 800-image validation set the system reaches 79.0% accuracy (macro F1 = 0.787), and recall is weakest for the Other Pathology class (0.645). We report full per-class metrics, analyse the confusion pattern, and discuss what the evaluation does and does not establish. In particular, the VQC’s 48 parameters were held fixed rather than jointly trained, so its actual contribution to accuracy has not yet been isolated by ablation. We position this work as a feasibility study of the hybrid architecture, and we set out the concrete steps that would be required before stronger performance or clinical-validity claims could be supported.