dc-qml: data-centric quantum machine learning - Flores, Everson da Silva; Barbosa, Kauã Dalla Riva Cucco; Guterres, Bruna de Vargas; Botelho, Silvia Silva da Costa; Pias, Marcelo Rita (Anais do Simpósio Brasileiro de Computação e Comunicação Quânticas (SBCCQ), 2026)
Quantum machine learning research (QML) has largely followed a model-centric perspective that focuses on circuit design and algorithm refinement. However, near-term quantum hardware constraints place equal importance on how data are prepared and encoded before quantum processing. This short paper presents preliminary work on a data-centric quantum machine learning methodology termed dc-qml. The approach structures data ingestion, tokenization, encoding and synthetic data generation within a unified data-to-model workflow. The goal is to explore how systematic data preparation can improve the performance of QML systems. This work in progress is intended to generate discussion at the first SBCCQ workshop.