Self-Adapting Quantum State Prediction and Algorithm Optimization

Jincheng Zhang

Published Sep 4, 2026• 0 citations

Abstract

This paper investigates the potential of self-adaptive quantum state prediction as a novel approach to algorithm optimization within the quantum computing realm. Traditional quantum algorithms often rely on computationally expensive pre-calculations, limiting their practical efficiency. We propose a new algorithmic framework leveraging self-adaptive quantum state prediction to dynamically adjust quantum operations, thereby significantly enhancing algorithm performance. This research explores the design of a system that predicts the state of a quantum system and uses this prediction to intelligently guide the execution of quantum circuits. The core mechanism centers around real-time state evolution and optimization, resulting in improved efficiency. This work presents a comprehensive analysis of the benefits and challenges associated with this approach and outlines a roadmap for future advancements in quantum algorithm design.

DOI: 10.5281/zenodo.22291341

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