Quantum-Enhanced Stochastic Simulation for Interdisciplinary Systems

Stochastic simulations (e.g., Muniruzzaman & Rolle's contaminant transport or Zhang et al.'s EV networks) are computationally expensive. This research proposes quantum-accelerated stochastic simulation (QSS) by encoding probability distributions into qubits and leveraging quantum walks for parallel sampling. For example, in DOM molecular dynamics (She et al., 2023), QSS could simulate 10⁶ molecular interactions exponentially faster. Unlike classical ML methods (Papacharalampous et al., 2019), QSS exploits quantum superposition to capture rare events (e.g., contaminant breakthroughs) with fewer samples. We integrate this with stochastic optimization (Li et al., 2022) for real-time control in systems like autonomous vehicles (Candela et al., 2021). The impact includes making previously intractable stochastic models feasible, opening new frontiers in econophysics or systems biology.

References:

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