Quantum Potential Adaptation via Adaptive Neural Networks

Jincheng Zhang

Published Sep 4, 2026• 0 citations

Abstract

Quantum potential optimization is a critical challenge in quantum computing, significantly impacting algorithm efficiency and the ability to harness quantum resources. Traditional optimization methods often struggle with the inherent complexities of quantum systems, requiring significant computational resources and fine-tuning of parameters. This paper proposes a novel approach to quantum potential optimization leveraging adaptive neural networks, aiming to address these challenges and enable more efficient quantum computation. The core concept centers around dynamically adjusting the network's parameters based on the quantum potential, fostering a more adaptive and robust optimization strategy. We present a detailed framework for implementing this approach, incorporating key mathematical formulations and demonstrating its efficacy through theoretical analysis and simulations. The research explores the potential of this method to improve the performance of various quantum algorithms, highlighting its promise for advancing the field.

DOI: 10.5281/zenodo.22296680

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