dc-qml: data-centric quantum machine learning
Resumo
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.
Referências
Carrera Vazquez, A. et al. (2024). Combining quantum processors with real-time classical communication. Nature, 636(8041):75–79.
Di Meglio, A. et al. (2024). Quantum computing for high-energy physics: State of the art and challenges. PRX Quantum, 5:037001.
Havlíček, V. et al. (2019). Supervised learning with quantum-enhanced feature spaces. Nature, 567:209–212.
Hong, Y.-Y. and Josh Domingo Lopez, D. (2025). A review on quantum machine learning in applied systems and engineering. IEEE Access, 13:144607–144631.
Jarrahi, M. H. et al. (2023). The principles of data-centric AI. Commun. ACM, 66(8):84–92.
McClean, J. R. et al. (2018). Barren plateaus in quantum neural network training landscapes. Nature Communications, 9(1):4812.
Raisa, M. et al. (2023). A Parallel Quantum Feature Encoding Scheme for Effective Classical Data Classification in QCNN. In Proc. IEEE TENCON, pages 1–5.
Rombach, R. et al. (2022). High-resolution image synthesis with latent diffusion models. In Proc. IEEE/CVF CVPR, pages 10684–10695.
Thanasilp, S. et al. (2024). Exponential concentration in quantum kernel methods. Nature Communications, 15(1):5200.
Thumwanit, N. et al. (2021). Trainable Discrete Feature Embeddings for Quantum Machine Learning. In Proc. IEEE QCE, pages 479–480.
Yang, Z. et al. (2023). A survey of important issues in quantum computing and communications. IEEE Communications Surveys & Tutorials, 25(2):1059–1094.
Di Meglio, A. et al. (2024). Quantum computing for high-energy physics: State of the art and challenges. PRX Quantum, 5:037001.
Havlíček, V. et al. (2019). Supervised learning with quantum-enhanced feature spaces. Nature, 567:209–212.
Hong, Y.-Y. and Josh Domingo Lopez, D. (2025). A review on quantum machine learning in applied systems and engineering. IEEE Access, 13:144607–144631.
Jarrahi, M. H. et al. (2023). The principles of data-centric AI. Commun. ACM, 66(8):84–92.
McClean, J. R. et al. (2018). Barren plateaus in quantum neural network training landscapes. Nature Communications, 9(1):4812.
Raisa, M. et al. (2023). A Parallel Quantum Feature Encoding Scheme for Effective Classical Data Classification in QCNN. In Proc. IEEE TENCON, pages 1–5.
Rombach, R. et al. (2022). High-resolution image synthesis with latent diffusion models. In Proc. IEEE/CVF CVPR, pages 10684–10695.
Thanasilp, S. et al. (2024). Exponential concentration in quantum kernel methods. Nature Communications, 15(1):5200.
Thumwanit, N. et al. (2021). Trainable Discrete Feature Embeddings for Quantum Machine Learning. In Proc. IEEE QCE, pages 479–480.
Yang, Z. et al. (2023). A survey of important issues in quantum computing and communications. IEEE Communications Surveys & Tutorials, 25(2):1059–1094.
Publicado
19/07/2026
Como Citar
FLORES, Everson da Silva; BARBOSA, Kauã Dalla Riva Cucco; GUTERRES, Bruna de Vargas; BOTELHO, Silvia Silva da Costa; PIAS, Marcelo Rita.
dc-qml: data-centric quantum machine learning. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO E COMUNICAÇÃO QUÂNTICAS (SBCCQ), 1. , 2026, Gramado/RS.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2026
.
p. 192-195.
DOI: https://doi.org/10.5753/sbccq.2026.22509.
