Quantum Entanglement Matrix Adaptive Computation Strategy

Jincheng Zhang

Published Sep 1, 2026• 0 citations

Abstract

Quantum entanglement matrix (QEM) is a powerful computational paradigm leveraging the unique properties of entangled quantum states. This paper introduces a novel adaptive computation strategy for QEM, designed to exploit the inherent parallelism of quantum computers. We explore a method that dynamically adjusts computational parameters based on the specific quantum circuit being executed, optimizing for increased speed and efficiency. The core mechanism centers around intelligently mapping quantum operations to parallelizable quantum circuits, minimizing the number of quantum gates required and maximizing overall computational throughput. We demonstrate the effectiveness of this approach through simulations and preliminary results, highlighting its potential to significantly accelerate complex calculations involving QEM.

DOI: 10.5281/zenodo.22222185

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