Multiple Embeddings for Quantum Machine Learning
This addresses a limitation in quantum machine learning for researchers and practitioners, though it appears incremental as it builds on existing embedding strategies.
The paper tackles the insufficient fitting capability of quantum machine learning methods by proposing a framework that integrates multiple quantum data embedding strategies, resulting in significant improvements over state-of-the-art methods.
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.