Title: Non-Local Quantum Machine Learning with Wave Function Decomposition

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

Quantum machine learning has emerged as a promising field, offering the potential to revolutionize areas such as pattern recognition, data analysis, and optimization. However, current quantum algorithms are often constrained to local computations, hindering their ability to fully exploit the power of quantum mechanics. This paper explores a novel approach to quantum machine learning, utilizing wave function decomposition to facilitate non-local data representation and significantly accelerate computations. We propose a quantum algorithm that decomposes data into entangled states, enabling computations across distances without the need for data transmission. This approach tackles the limitations of current quantum machine learning by introducing a fundamental shift towards distributed computation. The core mechanism centers around the creation of entangled states, allowing for parallel processing across multiple qubits. This paper details the algorithm's design, mathematical underpinnings, and preliminary experimental results demonstrating its potential for accelerating machine learning tasks. We aim to provide a foundation for future research exploring the application of wave function decomposition in quantum machine learning.

DOI: 10.5281/zenodo.22188478

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