Quantum State Adaptation and High-Order Matrix Computation

Jincheng Zhang

Published Sep 4, 2026• 0 citations

Abstract

Quantum State Adaptation and High-Order Matrix Computation explores the application of adaptive optimization techniques to enhance the efficiency of quantum computation. Traditional matrix calculations often rely on pre-computation, limiting real-time performance. This research proposes a novel approach utilizing adaptive optimization to dynamically adjust matrix operations, thereby significantly improving quantum computing efficiency. The core mechanism centers on a method that optimizes high-order matrix transformations in real-time, reducing computational bottlenecks and enabling more efficient quantum algorithms. This work addresses a critical challenge in quantum computing – the need for efficient matrix manipulations – with a focus on achieving substantial performance gains through algorithmic innovation.

DOI: 10.5281/zenodo.22291349

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