Quantum State Adaptive Probability Distribution Optimization

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

Quantum state adaptive probability distribution optimization is presented, utilizing the inherent self-adaptative properties of quantum states to optimize probability distributions. This approach aims to enhance the efficiency of quantum computing through automated parameter adjustment based on computational results. The core mechanism involves designing a probabilistic distribution optimization method that leverages the quantum state's self-adaptability, ultimately leading to improved quantum computation performance. This work addresses a significant challenge in quantum computing – the need for adaptable optimization – by introducing a novel framework that adapts to the unique characteristics of quantum systems. This paper explores the potential of this method and its implications for future quantum algorithms.

DOI: 10.5281/zenodo.22199183

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