Quantum Embedding Search for Quantum Machine Learning

TL;DR

This paper presents a quantum embedding search algorithm (QES) that optimizes quantum embedding designs for specific datasets, improving performance over manual designs and matching classical models.

Contribution

The paper introduces QES, a novel quantum embedding search method that leverages graph structures, entanglement levels, and surrogate models for efficient optimization.

Findings

QES outperforms manual quantum embedding designs.

QES achieves comparable performance to classical machine learning models.

Feasibility demonstrated on synthesis and Iris datasets.

Abstract

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.