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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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Li, J., Alam, M., Sha, C. M., Wang, J., Dokholyan, N. V., & Ghosh, S. (2021) · ACM/IEEE Design Automation Conference (DAC) · doi:10.1109/dac18074.2021.9586268 · Cited by 35
Optimal Tuning of Quantum Generative Adversarial Networks for Multivariate Distribution Loading
Agliardi, G., & Prati, E. (2022) · Quantum Reports · doi:10.3390/quantum4010006 · Cited by 34
Programming Quantum Neural Networks on NISQ Systems: An Overview of Technologies and Methodologies
Markidis, S. (2023) · Entropy · doi:10.3390/e25040694 · Cited by 41
Kottmann, K., Metz, F., Fraxanet, J., & Baldelli, N. (2021) · Physical Review Research · doi:10.1103/physrevresearch.3.043184 · Cited by 37
Evolutionary quantum architecture search for parametrized quantum circuits
Ding, L., & Spector, L. C. (2022) · Genetic and Evolutionary Computation Conference Companion (GECCO Companion) · doi:10.1145/3520304.3534012 · Cited by 35
Fock state-enhanced expressivity of quantum machine learning models
Gan, B. Y., Leykam, D., & Angelakis, D. G. (2022) · EPJ Quantum Technology · doi:10.1140/epjqt/s40507-022-00135-0 · Cited by 43
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A Characterization of Quantum Generative Models
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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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