Supervised learning with quantum-enhanced feature spaces, Vojtěch Havlíček, Antonio D. Córcoles, Kristan Temme, Aram W. Harrow, Abhinav Kandala, Jerry M. Chow, and Jay M. Gambetta, 2019Nature, Vol. 567DOI: 10.1038/s41586-019-0980-2 - This foundational paper introduces quantum-enhanced feature spaces and the concept of quantum kernels for supervised learning, demonstrating their use in support vector machines.
A universal quantum kernel for quantum machine learning, Basil Bichsel, Thomas B. Bäck, Tobias J. Osborne, and Moritz Weber, 2022Quantum Science and Technology, Vol. 7 (IOP Publishing)DOI: 10.1088/2058-9565/ac771d - This paper investigates the conditions for universality in quantum kernels, linking the expressivity of quantum feature maps to their ability to approximate arbitrary continuous functions.