Dimension Expansion Quantum Computing: Adaptive Learning Algorithm

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

Quantum computing offers unprecedented computational power, but scaling this power requires significant computational resources and careful parameter tuning. Traditional optimization techniques often rely on fixed parameters, limiting the potential for efficient scaling. This paper proposes a novel self-adaptive learning algorithm for quantum system parameter optimization, leveraging machine learning to dynamically adjust parameters based on the quantum system's inherent properties – specifically, quantum spin and quantum entanglement. The algorithm's core mechanism involves training a model to predict optimal parameter values, which are then used to iteratively refine the system's state, ultimately enhancing quantum computation's scalability and performance. This work addresses the limitations of static parameter optimization by introducing a dynamic and adaptive approach that exploits the unique characteristics of quantum systems. The research explores the potential of this algorithm to significantly improve the efficiency and expandability of quantum computations.

DOI: 10.5281/zenodo.22197957

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