ArticleScientific reports2026
Benchmarking quantum kernels and modern vision models for compound facial expression recognition.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We present a unified, compute-accounted comparison of seven pipelines for compound facial-expression recognition on RAF-DB: two classical hybrids (ResNet50-SVM, VGGFace-SVM), two modern baselines (EfficientNetV2-S, ViT-B/16), and three quantum-enhanced hybrids (QCNN, QKNN, QSVM). Beyond top-1 accuracy, we report feature-extraction (FX), training, and per-sample classification time to expose accuracy-efficiency trade-offs. ViT-B/16 achieves the highest accuracy (63.13%) with very low FX (~ 32.84 s) but at the cost of longer training time; EfficientNetV2-S is competitive (60.9%) with a short training time but higher FX (~ 2056.92 s). Among quantum hybrids, QSVM offers the best accuracy (54.97%) at moderate FX (~ 61.6 s), QKNN yields the most deployment-friendly FX (~ 24.47 s; 36.02% accuracy), and QCNN is FX-minimal (~ 11.9 s) but accuracy-limited (35.69%). Confusions cluster along fear-surprise and sadness-disgust, suggesting AU-aware local attention, margin-shaping objectives, and fairness-oriented augmentation. Overall, QSVM is the accuracy-leading quantum option under moderate budgets, QKNN suits tight latency envelopes, and EfficientNet/ViT remain strong when compute is ample. The protocol, ablations, and statistical tests (McNemar, BCa CIs, Cliff's δ) support reproducible, decision-oriented benchmarking. We do not claim near-term "quantum advantage"; instead we provide a compute-accounted benchmark and a feasibility-oriented analysis. Additional analyses include a strictly matched classical SVM baseline for quantum-kernel attribution, a lightweight kernel-shaping validation (fusion + regularisation), and a hardware-normalized cost discussion motivating simulator-based experiments at dataset scale.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.