Multiple Embeddings for Quantum Machine Learning

• URL: http://arxiv.org/abs/2503.22758v1
• Date: Thu, 27 Mar 2025 15:16:53 GMT
• Title: Multiple Embeddings for Quantum Machine Learning
• Authors: Siyu Han, Lihan Jia, Lanzhe Guo,
• Abstract summary: We propose a novel quantum machine learning framework that integrates multiple quantum data embedding strategies.<n> Experimental results validate the effectiveness of the proposed framework, demonstrating significant improvements over existing state-of-the-art methods.
• Score: 12.187305031893711
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This work focuses on the limitations about the insufficient fitting capability of current quantum machine learning methods, which results from the over-reliance on a single data embedding strategy. We propose a novel quantum machine learning framework that integrates multiple quantum data embedding strategies, allowing the model to fully exploit the diversity of quantum computing when processing various datasets. Experimental results validate the effectiveness of the proposed framework, demonstrating significant improvements over existing state-of-the-art methods and achieving superior performance in practical applications.

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