Quantum Hamiltonian-Based Generative Modeling of Single-Cell Transcriptomics for Gene Regulatory Network Inference
The research develops a quantum Hamiltonian-learning framework to infer gene regulatory networks from single-cell transcriptomics data. It models gene expression changes as quantum dynamics governed by a parameterized Hamiltonian, using time-resolved measurements from an Informationally Complete POVM. The method provides a scalable and sample-efficient way to uncover complex biological interactions by treating gene regulation as a quantum evolution problem.