Adaptive Quantum Machine Learning for Quantum State Compression

Jincheng Zhang

Published Sep 6, 2026• 0 citations

Abstract

Quantum machine learning (QML) offers the potential to revolutionize various computational tasks, particularly in areas requiring complex quantum computations. However, the inherent complexity of quantum states – the superposition and entanglement of quantum bits – poses significant challenges for efficient data representation and manipulation. This paper introduces an innovative quantum state compression algorithm, Adaptive Quantum Machine Learning, designed to address these challenges by dynamically adjusting quantum states during computation. We propose a novel approach that leverages the unique properties of quantum circuits to optimize the representation of quantum states, ultimately leading to improved quantum machine learning performance. The algorithm employs a feedback mechanism to iteratively compress the quantum state, achieving a balance between computational cost and data fidelity. We demonstrate the effectiveness of this approach through a series of simulations and analyses, showcasing significant improvements in both data compression and quantum algorithm efficiency. The paper also discusses potential future extensions and implications for broader QML applications.

DOI: 10.5281/zenodo.22504215

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