Dimension Multi-Adaptive Quantum Adaptive Computation

Jincheng Zhang

Published Sep 4, 2026• 0 citations

Abstract

This paper introduces a novel framework for quantum computation termed Dimension Multi-Adaptive Quantum Adaptive Computation (DMAC). The core principle of DMAC leverages quantum self-adaptation to dynamically adjust the dimensionality of quantum states during computation, optimizing performance and accommodating complex models. We propose a novel quantum model architecture that iteratively refines the quantum state's dimensionality based on the input problem characteristics. This dynamic dimensionality adjustment allows for improved efficiency and accuracy compared to static dimensionality schemes. The paper details the model's design, its application to a specific problem, and its advantages in achieving optimal results. The research highlights the potential of DMAC to address challenges in quantum machine learning and computational modeling.

DOI: 10.5281/zenodo.22298791

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