Supervised Latent Restructuring for Small-Data Quantum Learning in Plant Phenomics
For researchers in quantum machine learning and plant phenomics, this work identifies representation geometry as a key design variable in small-data quantum learning, though the results are incremental and expose practical limitations.
The paper addresses the challenge of small-data classification in high-dimensional biological data, proposing a hybrid workflow that compresses deep embeddings via PCA and restructures them with LDA before quantum kernel alignment. The method improves Silhouette coefficient from -0.006 to 0.197, but quantum performance remains limited, highlighting the difficulty of recovering nonlinear structure from aggressive compression.
High-dimensional biological data often exhibit a severe mismatch between feature dimensionality and sample size, making reliable classification difficult in extremely small-data regimes. In these settings, kernel methods can lose discriminative power when latent compression fails to preserve class-separating structure. We study this problem in fine-grained plant phenomics and propose a hybrid workflow that compresses 1280-dimensional deep image embeddings into a 64-dimensional PCA space and then restructures them into an 11-dimensional supervised latent space using Linear Discriminant Analysis (LDA), followed by GPU-accelerated Quantum Kernel Alignment (QKA) on NVIDIA L40S hardware. Empirically, supervised latent restructuring substantially improves the geometric separability of the compressed representation, increasing the Silhouette coefficient from 0.003 in the raw embedding space and -0.006 in PCA-64 to 0.197 in the supervised LDA-11 space. However, downstream classical evaluation reveals a clear compression trade-off: Linear SVM and XGBoost improve in the restructured latent space, whereas RBF-SVM and Random Forest degrade under the same 11-dimensional bottleneck. Under a constrained optimization budget, QKA in this regime remains challenging, indicating that latent geometry alone is not sufficient for strong trainable quantum performance. These findings position representation geometry as a central design variable in small-data quantum learning and expose the practical difficulty of recovering nonlinear discriminative structure from aggressively compressed biological representations.