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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The tensor network structure of quantum mechanics has inspired classical machine learning methods.
“Interestingly, the tensor network structure of quantum mechanics has even inspired classical machine learning methods [159].”
From Variational quantum algorithms (2021) · cites this paper as [159]
The Introduction and §2.1 describe quantum classifiers inspired by classical neural networks, while §6.5 compares the proposed classifier with classical methods. Searches for tensor networks and quantum-inspired classical methods found no passage in our copy addressing the claimed direction of influence. (LLM, conf=0.76)
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Xu, H., Zhang, A., Wang, Q., Hu, Y., Fang, F., & Cheng, L. (2025) · Applied Energy · doi:10.1016/j.apenergy.2025.125279 · Cited by 41 · see citing works
Symbiotic evolution of photonics and artificial intelligence: a comprehensive review
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Natural evolutionary strategies for variational quantum computation
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Quantum machine-learning phase prediction of high-entropy alloys
Brown, P., & Zhuang, H. L. (2023) · Materials Today · doi:10.1016/j.mattod.2023.02.014 · Cited by 40 · see citing works
Predicting toxicity by quantum machine learning
Suzuki, T., & Katouda, M. (2020) · Journal of Physics Communications · doi:10.1088/2399-6528/abd3d8 · Cited by 40 · see citing works
Quantum utility – definition and assessment of a practical quantum advantage
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Sushmit, M. M., & Mahbubul, I. M. (2023) · Energy Conversion and Management · doi:10.1016/j.enconman.2023.117555 · Cited by 36 · see citing works
Quantum Transfer Learning for Real-World, Small, and High-Dimensional Remotely Sensed Datasets
Otgonbaatar, S., Schwarz, G., Datcu, M. P., & Kranzlmüller, D. A. (2023) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2023.3316306 · Cited by 39 · see citing works
Learning Quantum States and Unitaries of Bounded Gate Complexity
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How quantum computing can enhance biomarker discovery
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A Characterization of Quantum Generative Models
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Quantum-Trained Convolutional Neural Network for Deepfake Audio Detection
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Quantum reservoir computing in finite dimensions
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- 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 › Computer Science › Artificial Intelligence › 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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