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Implementing a distance-based classifier with a quantum interference circuit
Open accessAbstract
Lately, much attention has been given to quantum algorithms that solve pattern recognition tasks in machine learning. Many of these quantum machine learning algorithms try to implement classical models on large-scale universal quantum computers that have access to non-trivial subroutines such as Hamiltonian simulation, amplitude amplification and phase estimation. We approach the problem from the opposite direction and analyse a distance-based classifier that is realised by a simple quantum interference circuit. After state preparation, the circuit only consists of a Hadamard gate as well as two single-qubit measurements, and computes the distance between data points in quantum parallel. We demonstrate the proof of principle using the IBM Quantum Experience and analyse the performance of the classifier with numerical simulations.
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“Further, distance-based classifiers using quantum interference circuits have been proposed in (Schuld et al. , 2017).”
From Noisy intermediate-scale quantum algorithms (2022) · cites this paper as Schuld et al. , 2017
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Metadata
- Authors
- Schuld, M., Fingerhuth, M., & Petruccione, F.
- Affiliations
- University of KwaZulu-Natal (ZA)
- Maastricht University (NL)
- National Institute for Theoretical Physics (ZA)
- Venue
- EPL (Europhysics Letters)
- Publisher
- IOP Publishing
- Published
- 2017-09-01
- Volume
- 119
- Issue
- 6
- Pages
- 60002
- ISSN
- 0295-5075 (ISSN-L)
- 1286-4854
- OpenAlex
- W2751668559
- Semantic Scholar
- 2770d683a8a63ed1d87e51ca73a8571e930269a7
- Cited by
- 285
- References
- 15
- Citations by year
- 2026: 20 · 2025: 24 · 2024: 45 · 2023: 57 · 2022: 30 · 2021: 37 · 2020: 35 · 2019: 15 · 2018: 16 · 2017: 6
- FWCI
- 11.76
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Europhysics Letters (EPL) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Research Foundation
- 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 many-body systems
- Keywords
- quantum machine learning, distance-based classifiers, Hadamard gate, single-qubit measurement, quantum state preparation, pattern recognition, IBM Quantum Experience
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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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