Paper
Behavioral Heterogeneity as Quantum-Inspired Representation
Driver heterogeneity is often reduced to labels or discrete regimes, compressing what is inherently dynamic into static categories. We introduce quantum-inspired representation that models each driver as an evolving latent state, presented as a density matrix with structured mathematical properties. Behavioral observations are embedded via non-linear Random Fourier Features, while state evolution blends temporal pers · Authors: Mohammad Elayan, Wissam Kontar · Published 2026-03-24 · arXiv:2603.22729
- Published
- 2026-03-24
- Authors
- 2
- arXiv
- 2603.22729
- Category
- cs.LG
Abstract
arXiv:2603.22729
Mohammad Elayan, Wissam Kontar
Driver heterogeneity is often reduced to labels or discrete regimes, compressing what is inherently dynamic into static categories. We introduce quantum-inspired representation that models each driver as an evolving latent state, presented as a density matrix with structured mathematical properties. Behavioral observations are embedded via non-linear Random Fourier Features, while state evolution blends temporal persistence of behavior with context-dependent profile activation. We evaluate our approach on empirical driving data, Third Generation Simulation Data (TGSIM), showing how driving profiles are extracted and analyzed.