Multiple Embeddings for Quantum Machine Learning
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Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 11 of its 24 references that are in the map. First posted on arXiv 27 Mar 2025.
Builds on
11 of 24 references.
References · 11
- 2018Supervised learning with quantum enhanced feature spacescited by 654
- 2020Power of data in quantum machine learningcited by 336
- 2020The effect of data encoding on the expressive power of variational quantum machine learning modelscited by 328
- 2020Predicting Many Properties of a Quantum System from Very Few Measurementscited by 229
- 2021Generalization in quantum machine learning from few training datacited by 214
- 2023Challenges and Opportunities in Quantum Machine Learningcited by 202
- 2020Robust data encodings for quantum classifierscited by 91
- 2021Quantum machine learning beyond kernel methodscited by 91
- 2024Better than classical? The subtle art of benchmarking quantum machine learning modelscited by 82
- 2021Encoding-dependent generalization bounds for parametrized quantum circuitscited by 81
- 2022Classical surrogates for quantum learning modelscited by 39
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Citing papers per year: 2026: 2.
2 papers.