| _version_ | 1866902037420048384 |
|---|---|
| author | Rita Pias, Marcelo Flores, Everson Guterres, Bruna Dalla Riva Cucco Barbosa, Kauã da Silva Botelho, Silvia |
| author_facet | Rita Pias, Marcelo Flores, Everson Guterres, Bruna Dalla Riva Cucco Barbosa, Kauã da Silva Botelho, Silvia |
| contents | <p>Preprint</p> <p> </p> <p>Quantum machine learning (QML) is a machine learning approach that uses quantum computation to improve learning performance. Research in this area has primarily followed a model-centric approach. However, near-term quantum hardware shows that data preparation also plays an important role, and can influence the quality and scalability of the learning process. Existing QML research focuses on circuits, kernels, and variational models; however, the data requirements that shape these methods remain less explored. This topical review addresses two questions. First, what forms of data processing are required across representative QML methods? Second, how can a data-centric approach improve the accuracy, scalability and resource use in QML models? A taxonomy was introduced to classify QML architectures according to their requirements for dimensionality reduction, encoding, dataset balance and compression. Based on this taxonomy, a data-centric QML (dc-qml) framework is proposed. It integrates various subprocesses into a coherent data flow, such as data ingestion, abstraction, tokenization, quantum encoding, and synthetic data generation. Results from recent benchmarks and case studies show that improvements in near-term QML optimizations often stem from data-focused practices rather than model refinement alone.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_20314756 |
| institution | Zenodo |
| language | |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Data-Centric Quantum Machine Learning: data-to-models continuum Rita Pias, Marcelo Flores, Everson Guterres, Bruna Dalla Riva Cucco Barbosa, Kauã da Silva Botelho, Silvia Quantum machine learning data-centric QML quantum data encoding quantum data tokenization model-centric QML <p>Preprint</p> <p> </p> <p>Quantum machine learning (QML) is a machine learning approach that uses quantum computation to improve learning performance. Research in this area has primarily followed a model-centric approach. However, near-term quantum hardware shows that data preparation also plays an important role, and can influence the quality and scalability of the learning process. Existing QML research focuses on circuits, kernels, and variational models; however, the data requirements that shape these methods remain less explored. This topical review addresses two questions. First, what forms of data processing are required across representative QML methods? Second, how can a data-centric approach improve the accuracy, scalability and resource use in QML models? A taxonomy was introduced to classify QML architectures according to their requirements for dimensionality reduction, encoding, dataset balance and compression. Based on this taxonomy, a data-centric QML (dc-qml) framework is proposed. It integrates various subprocesses into a coherent data flow, such as data ingestion, abstraction, tokenization, quantum encoding, and synthetic data generation. Results from recent benchmarks and case studies show that improvements in near-term QML optimizations often stem from data-focused practices rather than model refinement alone.</p> |
| title | Data-Centric Quantum Machine Learning: data-to-models continuum |
| topic | Quantum machine learning data-centric QML quantum data encoding quantum data tokenization model-centric QML |
| url | https://doi.org/10.5281/zenodo.20314756 |