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Dynamic Scalable Quantum Computing (DCQC) Framework

Jincheng Zhang

Published Aug 31, 2026• 0 citations
Publisher

Abstract

Quantum computing holds immense potential for revolutionizing various fields, offering unparalleled computational power and enabling breakthroughs in areas like drug discovery, materials science, and financial modeling. However, current quantum algorithms often struggle to achieve optimal performance due to the difficulty of accurately controlling and tuning quantum systems. This paper proposes a novel dynamic-adaptive quantum computing framework, termed Dynamic Scalable Quantum Computing (DCQC), which dynamically adjusts quantum system parameters based on the quantum computation's current state. We present a design that leverages the inherent variability of quantum states to achieve enhanced efficiency and performance. This approach distinguishes DCQC from existing methods by providing a mechanism for real-time parameter optimization, directly addressing a key bottleneck in quantum algorithm design. The paper details the framework's architecture, explores its implementation and validation using simplified model scenarios, and provides a theoretical analysis of its potential benefits. We demonstrate that DCQC offers a significant improvement in computational efficiency, particularly when dealing with complex quantum systems. This work lays the foundation for a new paradigm in quantum algorithm design, offering a pathway to more robust and adaptable quantum computation.

DOI
10.5281/zenodo.22204599

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