Title: Quantum-Inspired Algorithm for Parallel Wavefront Optimization

Jincheng Zhang

Published Sep 2, 2026• 0 citations

Abstract

Quantum-inspired algorithms represent a promising avenue for enhancing the performance of parallel waveffront optimization, a crucial task in various computational fields. Standard parallel waveffront algorithms, while effective, often face challenges with increasing computational complexity, limiting their scalability. This paper introduces a novel quantum-inspired algorithm that leverages quantum superposition and entanglement to efficiently explore the solution space, aiming to overcome these limitations. The algorithm's design centers around mimicking the principles of quantum wavefunctions, offering a potential pathway to substantial improvements in optimization speed and accuracy. We present a detailed explanation of the algorithm's core mechanics, its mathematical formulation, and its potential benefits within the context of parallel waveffront optimization. The research focuses on demonstrating the algorithm's effectiveness through simulations and preliminary analysis, highlighting its potential to address the scalability issues inherent in traditional approaches.

DOI: 10.5281/zenodo.22251925

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