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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Distance-based classifiers using quantum interference circuits have been proposed.
“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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Singh, J., Ali, F., Shah, B., Bhangu, K. S., & Kwak, D. (2022) · IEEE Access · doi:10.1109/access.2022.3216890 · Cited by 18 · see citing works
On the expressivity of embedding quantum kernels
Gil-Fuster, E., Eisert, J., & Dunjko, V. (2024) · Machine Learning Science and Technology · doi:10.1088/2632-2153/ad2f51 · Cited by 19 · see citing works
Jia, K., Meng, F., & Liang, J. (2024) · Information Sciences · doi:10.1016/j.ins.2024.121543 · Cited by 19 · see citing works
Gyöngyösi, L., & Imre, S. (2025) · Quantum Information and Computation · doi:10.2478/qic-2025-0006 · Cited by 18 · see citing works
Chalumuri, A., Kune, R., & Manoj, B. S. B. S. (2020) · Procedia Computer Science · doi:10.1016/j.procs.2020.04.061 · Cited by 18 · see citing works
Quantum latent diffusion models
Falco, F. D., Ceschini, A., Sebastianelli, A., Saux, B. L., & Panella, M. (2024) · Quantum Machine Intelligence · doi:10.1007/s42484-024-00224-6 · Cited by 19 · see citing works
Ensemble-learning error mitigation for variational quantum shallow-circuit classifiers
Li, Q., Huang, Y., Hou, X., Li, Y., Wang, X., & Bayat, A. (2024) · Physical Review Research · doi:10.1103/physrevresearch.6.013027 · Cited by 16 · see citing works
Shedding Light on the Future: Exploring Quantum Neural Networks through Optics
Yu, S., Jia, Z., Zhang, A., Mer, E., Li, Z., Crescimanna, V., Chen, K., Patel, R. B., Walmsley, I. A., & Kaszlikowski, D. (2024) · Advanced Quantum Technologies · doi:10.1002/qute.202400074 · Cited by 17 · see citing works
Variational learning for quantum artificial neural networks
Tacchino, F., Barkoutsos, P. K., Macchiavello, C., Gerace, D., Tavernelli, I., & Bajoni, D. (2020) · IEEE International Conference on Quantum Computing and Engineering (QCE) · doi:10.1109/qce49297.2020.00026 · Cited by 17 · see citing works
Classification with Quantum Measurements
Gonzalez, F. A., Vargas-Calderón, V., & Vinck-Posada, H. (2021) · Journal of the Physical Society of Japan · doi:10.7566/jpsj.90.044002 · Cited by 17 · see citing works
Relevance of Near-Term Quantum Computing in the Cloud: A Humanities Perspective
Barzen, J., Leymann, F., Falkenthal, M., Vietz, D., Weder, B., & Wild, K. (2021) · Communications in computer and information science · doi:10.1007/978-3-030-72369-9_2 · Cited by 17 · see citing works
Diabetes Type 2: Poincar�Data Preprocessing for Quantum Machine Learning
Sierra-Sosa, D., D. Arcila-Moreno, J., Garcia-Zapirain, B., & Elmaghraby, A. (2021) · Computers, Materials & Continua · doi:10.32604/cmc.2021.013196 · Cited by 16 · see citing works
Quantum Hybrid Diffusion Models for Image Synthesis
Falco, F. D., Ceschini, A., Sebastianelli, A., Saux, B. L., & Panella, M. (2024) · Künstliche Intell. · doi:10.1007/s13218-024-00858-5 · Cited by 16 · see citing works
On the learnability of quantum neural networksPreprint
Du, Y., Hsieh, M.-H., Liu, T., You, S., & Tao, D. (2020) · Research Square · doi:10.21203/rs.3.rs-80242/v1 · Cited by 18 · see citing works
Venkatesh, K., Naik, K. J., & Shankar, A. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-023-17992-1 · Cited by 15 · see citing works
Quantum Simulation of Klein Gordon Equation and Observation of Klein Paradox in IBM Quantum ComputerPreprint
Kapil, M., Behera, B. K., & Panigrahi, P. K. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1807.00521 · Cited by 19 · see citing works
Distributed Quantum Machine Learning
Neumann, N. M. P., & Wezeman, R. S. (2022) · Communications in computer and information science · doi:10.1007/978-3-031-06668-9_20 · Cited by 14 · see citing works
- 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 › Computer Science › Artificial Intelligence › 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.
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