Quantum Phase Field Reconstruction with Adaptive Kernel Selection

Jincheng Zhang

Published Sep 4, 2026• 0 citations

Abstract

Quantum phase fields are crucial for various quantum technologies, including quantum optics and quantum computing. Accurate reconstruction of these fields is essential for various applications. Current methods often suffer from computational inefficiency and limited accuracy, particularly when dealing with complex systems. This paper introduces a novel method for quantum phase field reconstruction that leverages adaptive kernel selection based on the system's evolution. The core mechanism involves a neural network dynamically selecting the most appropriate kernel function for reconstruction, optimizing both speed and accuracy. We demonstrate the efficacy of this approach through simulations and preliminary experimental results. The paper highlights the potential for significant improvements in both computational efficiency and reconstruction fidelity.

DOI: 10.5281/zenodo.22299675

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