Authors:
Danel Arias Alamo
;
Sergio Hernández López
and
Javier Lázaro González
Affiliation:
University of Deusto, Avda. de las Universidades, 24, Bilbao 48007, Spain
Keyword(s):
Data Reuploading, Variational Quantum Circuits, Quantum Machine Learning, Expressibility, Barren Plateaus, Quantum Classification, Quantum Embedding, Quantum Optimization, Quantum Computing.
Abstract:
Data Reuploading has been proposed as a generic embedding strategy in Variational Quantum Circuits (VQCs), offering a systematic approach to encoding classical data without the need for problem-specific circuit design. Prior studies have suggested that increasing the number of reuploading layers enhances model performance, particularly in terms of expressibility. In this paper, we present an experimental analysis of Data Reuploading, systematically evaluating its impact on expressibility, trainability, and completeness in classification tasks. Our results indicate that while adding some reuploading layers can improve performance, excessive layering does not lead to expressibility gains and introduces barren plateaus, significantly hindering trainability. Consequently, although Data Reuploading can be beneficial in certain scenarios, it is not a ”cheat code” for optimal quantum embeddings. Instead, the selection of an effective embedding remains an open problem, requiring a careful ba
lance between expressibility and trainability to achieve robust quantum learning models.
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