Quantum-Inspired Neural Networks for Optimal Control in Stochastic Systems

Jincheng Zhang

Published Aug 29, 2026• 0 citations

Abstract

This paper explores the potential of quantum-inspired neural networks (QINNs) for solving optimal control problems within stochastic systems. Traditional optimal control methods often struggle with the inherent uncertainty and noise present in real-world systems. We propose a novel neural network architecture that mimics key quantum phenomena – interference and superposition – to enhance its ability to navigate these challenges. The QINN architecture leverages probabilistic outputs and a layered structure designed to represent quantum states. We formulate the optimal control problem as a stochastic differential equation (SDE) and employ the QINN to approximate the optimal control trajectory. The core idea is to treat the control action as a superposition of possible actions, where quantum interference guides the system towards the most probable optimal solution. The performance of the QINN is evaluated through simulations, demonstrating its potential to outperform classical optimal control techniques, particularly in scenarios with high levels of stochastic disturbance. The key contribution of this work lies in the application of quantum-inspired principles to control system design, offering a new avenue for addressing complex, uncertain control problems. The ultimate goal is to develop a more robust and efficient control strategy by harnessing the principles of quantum mechanics.

DOI: 10.5281/zenodo.22154528

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