Quantum-Inspired Stochastic Differential Equations (QSDEs)
Jincheng Zhang
This paper introduces Quantum-Inspired Stochastic Differential Equations (QSDEs), a novel approach to modeling stochastic systems by leveraging core concepts from quantum mechanics. Traditional Stochastic Differential Equations (SDEs) often rely on Gaussian noise, which can be a significant limitation when accurately representing complex, real-world phenomena. QSDEs propose a fundamental shift by incorporating superposition and entanglement principles to model noise, moving beyond Gaussian distributions. The core mechanism involves introducing a "quantum noise" term, denoted as *η(t, x, P)*, where *η* is a superposition of probability distributions dynamically adjusted based on the system's state *x*. This allows for a more nuanced representation of uncertainty, potentially leading to vastly improved simulations across diverse fields. We outline the theoretical framework, explore the key mathematical formulations, and discuss the potential impact of QSDEs on areas such as financial modeling, physics simulations, and control systems. The central claim of this work is that classical SDEs can be significantly enhanced by incorporating quantum mechanical principles.
•Sep 7, 2026•0 citations