Title: Adaptive Non-Linear Matrix Operations for Quantum Algorithms (ANMQ)

Jincheng Zhang

Published Sep 7, 2026• 0 citations

Abstract

This paper introduces the ANMQ, a novel quantum algorithm leveraging adaptive matrix operations to enhance the efficiency of large-scale matrix computations. Traditional quantum algorithms often employ static complexity, limiting performance in scenarios requiring high throughput. ANMQ dynamically adjusts the computational complexity of matrix operations, enabling efficient processing of massive datasets. The core mechanism involves integrating quantum algorithms with adaptive matrix operations, allowing for a flexible and optimized computation strategy. This approach addresses the challenges associated with scaling quantum computation to large matrices, offering a potentially transformative advancement in quantum algorithm design. The paper details the algorithm's architecture, demonstrates its efficacy through theoretical analysis and preliminary simulations, and explores potential applications across various quantum computing domains.

DOI: 10.5281/zenodo.22608541

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