This preprint presents a quantum-like fractal representational framework for classifying physiological stress states from heart rate variability (HRV). The approach treats HRV not as a stationary time series or feature-aggregation problem, but as a structured, scale-dependent dynamical signal, whose discriminative power emerges from its underlying geometric organization across temporal scales. Rather than optimizing predictive performance through model complexity alone, the framework emphasizes representational separability arising from fractal coherence, scale invariance, and phase-consistent structure in HRV dynamics. Using a large retrospective dataset, the study demonstrates that stress-state classification accuracy can be achieved through geometry-aware representations that preserve multi-scale structure, without reliance on opaque end-to-end optimization pipelines. Empirical results are presented in a cross-validated setting and are intended to illustrate the representational properties of the proposed framework rather than to claim deployable clinical performance. Implementation details, operational architectures, and real-time system considerations are intentionally abstracted. This work relates to a U.S. patent-pending application and is shared to establish theoretical foundations, empirical evidence, and a basis for further scientific discussion. The manuscript is positioned as a conceptual and analytical contribution to physiological signal processing, stress modeling, and representation-driven approaches to biosignal classification.