Self-Adaptive Quantum State Optimization

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

Quantum computing holds immense promise for scientific discovery and technological advancement, but its practical implementation is severely hampered by the inherent fragility of quantum states and the prevalence of errors. This paper presents a novel quantum state optimization algorithm, termed "Adaptive Quantum State Optimization" (AQSO), designed to address these challenges by dynamically adjusting quantum states to minimize errors and enhance computational performance. AQSO leverages the unique properties of quantum states to create a feedback loop that continuously refines the quantum state, ultimately improving the fidelity and reliability of quantum computations. The proposed algorithm incorporates a dynamic adjustment mechanism based on error metrics, offering a significant departure from static optimization approaches. We detail the algorithm's architecture, key components, and experimental validation, demonstrating its effectiveness in reducing error rates and enhancing the speed of quantum algorithms. This work contributes to the ongoing research and development of robust and scalable quantum computing paradigms.

DOI: 10.5281/zenodo.22194428

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