Quantum State-Based Non-Linear Computing and Quantum Algorithm Optimization

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

Quantum state-based non-linear computing offers a fundamentally different approach to computation compared to classical approaches. This paper explores the potential of leveraging quantum mechanics' inherent non-linear properties to design novel algorithms and optimize existing classical algorithms, particularly within the context of machine learning. We propose a new algorithm framework centered around the manipulation of quantum entangled states, focusing on accelerating computations and enhancing the performance of machine learning models. The core mechanism involves introducing controlled quantum entanglement to guide the computation process, ultimately leading to improved results. This work highlights the transformative potential of this paradigm shift in computational power and explores its practical implications for advanced machine learning applications.

DOI: 10.5281/zenodo.22186306

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