Quantum Machine Learning via Quantum Coherence Degradation

Jincheng Zhang

Published Aug 28, 2026• 0 citations

Abstract

This paper explores the potential of utilizing quantum coherence degradation – specifically, the controlled suppression of quantum interference – as a mechanism for accelerating machine learning algorithms. The core claim is that by carefully designing quantum circuits and leveraging the phenomenon of coherence degradation, it's possible to achieve enhanced precision and speed compared to classical counterparts. We present a framework for translating classical machine learning algorithms into quantum circuits, with a focus on manipulating quantum states to induce controlled decoherence. This approach aims to mitigate the detrimental effects of noise and errors inherent in quantum computation, leading to improved learning outcomes. The theoretical analysis demonstrates that by strategically introducing decoherence patterns, we can effectively reduce the computational complexity of certain machine learning tasks. We acknowledge the challenges involved in precisely controlling decoherence and discuss potential future research directions.

DOI: 10.5281/zenodo.22137732

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