Supervised learning with quantum-enhanced feature spaces, Vojtech Havlicek, Antonio D. Córcoles, Kristan Temme, Aram W. Helwegen, Abhinav Kandala, and Jay M. Gambetta, 2019Nature, Vol. 567 (Nature Portfolio)DOI: 10.1038/s41586-019-0980-2 - Introduces the concept of quantum-enhanced feature spaces and quantum kernel methods, including the definition of quantum kernels, forming the basis for algorithms like QSVM.
Power of data in quantum machine learning, Hsin-Yuan Huang, Michael Broughton, Masoud Mohseni, Ryan Babbush, Hartmut Neven, Jarrod R. McClean, and Sergio Boixo, 2021Nature Communications, Vol. 12DOI: 10.1038/s41467-021-22539-9 - Discusses the influence of data encoding and feature map design on the performance and generalization of quantum machine learning models, touching upon concepts related to kernel alignment.
Quantum Machine Learning in Feature Hilbert Spaces, Maria Schuld, Nathan Killoran, 2019Physical Review Letters, Vol. 122 (American Physical Society)DOI: 10.1103/PhysRevLett.122.040504 - Provides a theoretical framework for quantum machine learning with quantum kernels and feature maps, emphasizing the Hilbert space perspective for data representation.