Adaptive Quantum Potential Optimization via Adaptive Neural Networks

Jincheng Zhang

Published Sep 4, 2026• 0 citations

Abstract

Quantum computing promises revolutionary advancements in computation, offering the potential to solve problems intractable for classical computers. However, realizing this potential requires overcoming significant challenges, particularly in the design and optimization of quantum circuits. This paper proposes a novel optimization algorithm, Adaptive Quantum Potential Optimization via Adaptive Neural Networks (AQPO-AN), which leverages the power of adaptive neural networks to dynamically adjust the quantum potential landscape, thereby enhancing the fidelity and efficiency of quantum computations. We demonstrate the effectiveness of AQPO-AN through rigorous analysis and experimental validation on several benchmark quantum algorithms. The core mechanism centers around employing a dynamically parameterized neural network to refine the quantum potential through a series of iterative adjustments based on the quantum state. We analyze the algorithm's convergence properties and show its superior performance compared to existing optimization techniques. The paper concludes with a detailed discussion of the algorithm's architecture, the rationale behind the adaptive neural network design, and future directions for research.

DOI: 10.5281/zenodo.22296647

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