Quantum Embeddings of Classical Data for Quantum Machine Learning
Gennaro De Luca, Yinong Chen · 2022
A major area of research in the field of quantum machine learning is the analysis of the loss landscape, particularly of variational quantum algorithms. These works often provide bounds and generalizations for various ansatzes and quantum embedding strategies. These analyses include approaches such as the Hessian and Fisher information matrices as well as generalized trigonometric polynomials. However, many such reviews often rely on a rotational encoding in practice or focus on few different approaches. The goal of this work is to statistically analyze experimental results from a quantum machine learning model that employs various different quantum embedding approaches, including those covered in related work, as well as the effect of measurement basis on the model.