# Behavioral Heterogeneity as Quantum-Inspired Representation

arXiv: https://arxiv.org/abs/2603.22729 · HTML: https://mlsanity.com/paper/2603.22729/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 12 references that are in the map.
- First posted on arXiv 24 Mar 2026.

## Builds on (most cited first, 4 of 4)

- [Learning with Density Matrices and Random Features](https://mlsanity.com/paper/2102.04394/) (2021)
- [A Generic Stochastic Hybrid Car-following Model Based on Approximate Bayesian Computation](https://mlsanity.com/paper/2312.10042/) (2023)
- [Markov Regime-Switching Intelligent Driver Model for Interpretable Car-Following Behavior](https://mlsanity.com/paper/2506.14762/) (2025)
- [When Context Is Not Enough: Modeling Unexplained Variability in Car-Following Behavior](https://mlsanity.com/paper/2507.07012/) (2025)

## Built on by (most cited first, 1 of 1)

- [Quantum-Inspired Modeling of Driving Behavior](https://mlsanity.com/paper/2608.25907/) (2026)

## Abstract (arXiv)

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.