A Stochastic Reconstruction of Entangled States: from Quantum Foundations to Machine Learning and Decision Making

arXiv:2601.17070v2 Announce Type: replace Abstract: This article presents a concrete mathematical framework for the generation of entangled quantum states from classical stochastic processes. We demonstrate that any density operator $\rho_{AB}$ of a composite system can be derived from the correlations between two underlying stochastic processes, $X(t)$ and $Y(t)$, representing the random fluctuations of its subsystems. This construction utilizes a two-scale temporal scheme - micro and macro time - where quantum correlations emerge as macro-correlations derived from underlying micro-correlations. We propose the Double Covariance Model (DCM), which reproduces the some properties of quantum theory by treating quantum states as the fourth-order moment structure of an underlying classical probability space. DCM may also have potential implications for applications, particularly in quantum-inspired simulation and technology, and quantum-like modeling of cognition and decision making, as a tool for generation of density matrices with classical stochastic processes.