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Data re-uploading for a universal quantum classifier
Open accessAbstract
A single qubit provides sufficient computational capabilities to construct a universal quantum classifier when assisted with a classical subroutine. This fact may be surprising since a single qubit only offers a simple superposition of two states and single-qubit gates only make a rotation in the Bloch sphere. The key ingredient to circumvent these limitations is to allow for multiple data re-uploading . A quantum circuit can then be organized as a series of data re-uploading and single-qubit processing units. Furthermore, both data re-uploading and measurements can accommodate multiple dimensions in the input and several categories in the output, to conform to a universal quantum classifier. The extension of this idea to several qubits enhances the efficiency of the strategy as entanglement expands the superpositions carried along with the classification. Extensive benchmarking on different examples of the single- and multi-qubit quantum classifier validates its ability to describe and classify complex data.
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“Interestingly, the tensor network structure of quantum mechanics has even inspired classical machine learning methods [159].”
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Gross, M., Lange, M., Bujnowski, B., & Rieser, H.-M. (2025) · European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN) · doi:10.14428/esann/2025.es2025-152 · Cited by 1
QuFrame: A Novel Encoding Ensemble Framework for Quantum Neural Networks
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Research on Quantum Circuit Learning Models for Molecular Dynamics Simulation
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Quantum-enhanced neural exchange-correlation functionals
Sokolov, I. O., Both, G., Bochevarov, A. D., Dub, P. A., Levine, D. S., Brown, C. T., Acheche, S., Barkoutsos, P. K., & Elfving, V. E. (2025) · Physical Review A · doi:10.1103/m51l-fys2 · Cited by 1
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Huembeli, P. (2021) · UPCommons institutional repository (Universitat Politècnica de Catalunya) · doi:10.5821/dissertation-2117-348901 · Cited by 1
Connerty, E., Evans, E. N., Angelatos, G., & Narayanan, V. (2025) · Research Square · doi:10.21203/rs.3.rs-6522261/v1 · Cited by 1
Metadata
- Authors
- Pérez-Salinas, A., Cervera-Lierta, A., Gil-Fuster, E., & Latorre, J. I.
- Affiliations
- Barcelona Supercomputing Center (ES)
- Universitat de Barcelona (ES)
- Centre for Quantum Technologies (SG)
- Venue
- Quantum
- Published
- 2020-02-06
- Volume
- 4
- Pages
- 226
- ISSN
- 2521-327X (ISSN-L)
- OpenAlex
- W2954939433
- Cited by
- 713
- References
- 18
- Citations by year
- 2026: 204 · 2025: 167 · 2024: 121 · 2023: 93 · 2022: 61 · 2021: 57 · 2020: 10
- FWCI
- 21.70
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Quantum (publishedVersion, cc-by)
- LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) (acceptedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- National University of Singapore (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- UPCommons institutional repository (Universitat Politècnica de Catalunya) (submittedVersion, cc-by)
- arXiv (Cornell University) (cc-by)
- Bulletin of the American Physical Society
- Funders
- QuantIC
- Barcelona Supercomputing Center
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Quantum Computing Algorithms and Architecture
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Quantum Information and Cryptography
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Quantum Mechanics and Applications
- Keywords
- quantum machine learning, quantum classifiers, data re-uploading, multiqubit entanglement, quantum circuit design, multi-class classification, benchmarking
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