Influence of Embedding-Methods on the Generalization of Quantum Machine Learning

Influence of Embedding-Methods on the Generalization of Quantum Machine Learning

Abstract:

Quantum Machine Learning is a promising field of application for quantum computers. However, to see real advantages over classical computers, advanced quantum fundamentals are needed. One of the building blocks of quantum computers are embeddings, which convert real data into quantum data. In this work, the focus is on the influence of various embedding methods on the ”quality” of a Quantum Machine Learning model. As the focus is on these embeddings, the model and quantum circuit are kept simple. They solve a binary classification problem. Nevertheless, the interplay of certain embeddings with different circuits is also of interest and is briefly discussed in this work. Since much already exists in the literature about the embedding methods ”Angle Embedding” and ”Amplitude Embedding”, this work also focuses on other embedding methods from the literature. To determine the quality of a model, we examined its generalizability. For this, we used various metrics from classical Machine Learning. Although the question of the best embedding could not be answered, interesting insights were gained about the effects of the embeddings on different datasets.

Author:

Advisors:

Einfluss von Embedding Methoden auf Generalisierbarkeit in Quantum Machine Learning

Leo Sünkel, Thomas Gabor, Claudia Linnhoff-Popien

Student Thesis | Published August 2023 | Copyright © QAR-Lab
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