Adaptive Quantum Computation Self-Similarity Modeling

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

This paper explores the application of self-similarity modeling to enhance the design and optimization of quantum computation. Traditional quantum algorithm design often relies on manual engineering, limiting adaptability and efficiency. We propose a novel framework leveraging self-similarity to analyze and manipulate quantum systems, leading to automated optimization strategies. The core mechanism involves constructing a model that detects and exploits self-similar patterns within quantum states, informing algorithm design and hardware optimization. This approach aims to overcome the limitations of traditional methods, improving both computational speed and stability. The study demonstrates the effectiveness of this self-similarity-based approach in guiding the evolution of quantum algorithms and hardware.

DOI: 10.5281/zenodo.22265074

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