Quantum Hiding Matrix Optimization

Jincheng Zhang

Published Sep 2, 2026• 0 citations

Abstract

Quantum Hiding Matrix Optimization (QHMO) presents a novel quantum computation paradigm that leverages dynamic parameter adjustments through a hidden matrix optimization algorithm. Traditional quantum algorithms are constrained by fixed parameters, limiting their potential for optimization and efficiency. This research introduces QHMO, a quantum algorithm capable of adapting to changing system conditions, thereby achieving improved performance. The core mechanism centers around creating a quantum "hiding matrix" that dynamically modifies the quantum state, allowing for optimized computation. We detail the algorithm's design, the underlying mathematical principles, and its potential impact on quantum computation. This paper explores the advantages of dynamic parameter adjustment and its implications for enhancing quantum algorithms.

DOI: 10.5281/zenodo.22249487

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