Adaptive Quantum State Prediction Modeling

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper presents an innovative approach to quantum state prediction, introducing an adaptive quantum state prediction model that dynamically adjusts its parameters based on the predicted state. Traditional methods are static and often struggle to accurately predict the states of complex quantum systems. Our model leverages quantum state evolution and feedback to achieve significantly improved accuracy and speed, offering a real-time prediction capability. We detail the model's architecture, the feedback mechanism, and the resulting performance improvements through theoretical analysis and experimental validation. The core claim of this work is that a self-adaptive quantum state prediction model, driven by dynamic parameter adjustment, can dramatically enhance the speed and accuracy of quantum state prediction.

DOI: 10.5281/zenodo.22200073

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