Quantum Adaptive Matrix Computation

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

Quantum adaptive matrix computation represents a paradigm shift in matrix multiplication, leveraging quantum mechanics to achieve significantly faster and more efficient computations compared to classical methods. This paper investigates the potential of quantum algorithms to optimize and accelerate the core operations of matrix multiplication, specifically focusing on the design and implementation of a novel quantum algorithm. The core claim posits that by harnessing quantum self-adaptability, we can fundamentally reshape matrix multiplication, enabling substantial improvements in computational speed and scalability. The research will explore a specific type of matrix computation, demonstrating the feasibility of quantum-enhanced matrix multiplication through a rigorous theoretical analysis and preliminary experimental validation. The paper will also address the challenges and future directions of this emerging field.

DOI: 10.5281/zenodo.22202975

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