Adaptive Quantum Potential Model via Quantum Machine Learning

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

This paper presents an innovative quantum machine learning model, termed the Adaptive Quantum Potential Model (AQPM), designed to accelerate and enhance quantum chemical simulations. Traditional quantum chemistry relies heavily on precise, handcrafted quantum potential functions, limiting the model's applicability and hindering the ability to accurately predict molecular properties. This work proposes a novel approach utilizing a dynamically adjusted quantum potential landscape, achieved through a quantum machine learning framework, to represent and learn these functions. The AQPM leverages quantum superposition and entanglement to enhance the model's ability to capture subtle correlations within molecular systems, leading to improved accuracy and efficiency in molecular modeling and quantum simulation. This advancement promises to significantly broaden the scope and capabilities of quantum chemistry, particularly in areas demanding high-fidelity simulations and molecular design.

DOI: 10.5281/zenodo.22280991

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