Quantum Embedding Search for Quantum Machine Learning
This work addresses the challenge of automating quantum embedding design for quantum machine learning practitioners, representing an incremental improvement over manual methods.
The paper tackles the problem of designing optimal quantum embeddings for specific datasets by introducing a quantum embedding search algorithm (QES) that connects quantum embedding structures to directed multi-graphs, reduces search space via entanglement levels, and uses surrogate models for efficiency. It demonstrates on synthesis and Iris datasets that QES outperforms manual designs and achieves performance comparable to classical machine learning models.
This paper introduces a novel quantum embedding search algorithm (QES, pronounced as "quest"), enabling search for optimal quantum embedding design for a specific dataset of interest. First, we establish the connection between the structures of quantum embedding and the representations of directed multi-graphs, enabling a well-defined search space. Second, we instigate the entanglement level to reduce the cardinality of the search space to a feasible size for practical implementations. Finally, we mitigate the cost of evaluating the true loss function by using surrogate models via sequential model-based optimization. We demonstrate the feasibility of our proposed approach on synthesis and Iris datasets, which empirically shows that found quantum embedding architecture by QES outperforms manual designs whereas achieving comparable performance to classical machine learning models.